In this paper, an annular/circular plate made of hyperelastic material and considering the viscoelastic property was investigated based on a novel nonlinear elasticity theory. A new approach for hyperelastic materials in conjunction with the Kelvin–Voigt scheme is employed to obtain the structure’s large deformation under uniform transverse loading. The constitutive equations were extracted using the energy method. The derived partial differential time-dependent equations have been solved via the semi-analytical polynomial method (SAPM). The obtained results have been validated by ABAQUS software and the available paper. In consequence, a good agreement between the results was observed. Finally, several affecting parameters on the analysis have been attended to and studied, such as the nonlinear elasticity analysis, the boundary conditions, loading, and the material’s viscosity. It can be possible to obtain the needed time for achieving the final deformation of the structure based on the applied analysis in this research. Various materials can be found in nature that demonstrate different mechanical behaviors under applied loads, such as metals displaying linear elastic behavior until yielding. At the same time, irreversible deformation is then observed up to failure. On the other hand, brittle materials exhibit a slight elastic deformation linearly and then fail without irreversible deformation. However, it is notable that all materials do not exhibit linear elasticity. Hyperelastic materials will exhibit elastic deformations excessively before failure without irreversible deformation. These materials exhibit an extremely nonlinear stress-strain behavior that ascends monotonically up to fracture. It is well-known that linear elastic materials can be defined with two material constants: modulus of elasticity and Poisson’s ratio. On the contrary, hyperelastic materials can be defined by a strain-energy density function. The stored strain energy remains constant when a sample is subjected to constant strain. Therefore, hyperelastic materials are modeled in the context of strain energy potentials such as Arruda-Boyce , Gent 22, Neo-HoSome recent studies on the mechanical responses of hyperelastic structures are summarized below. Erchiqui et al.  implemenHosseini and Rahimi  conducteHosseini et al.  perused As stated before, many recent studies have been performed on the mechanical analysis of structures made of hyperelastic materials based on various strain energy functions, especially the neo-Hookean model. However, there is no study on the static response of an annular circular visco-hyperelastic plate. The new approach for hyperelastic materials simulates the annular circular visco-hyperelastic plate in conjunction with the Kelvin–Voigt scheme. The general formulations are derived according to the first-order shear deformation theory (FSDT). The constitutive equations and boundary conditions are extracted by the energy method. Then, the semi-analytical polynomial method is utilized for solving the partial differential time-dependent equations. Finally, the effects of various parameters on the bending analysis of the annular circular visco-hyperelastic plate are investigated and discussed in detail.First, the geometry of the problem is discussed. The geometry of the analyzed structure will play a significant role in obtaining the governing equations. The more comprehensive the geometry of the problem, the more different structures can be modeled only by considering a single formulation. Analysis of the structures made of hyperelastic materials will make the simulation more complicated. Therefore, an attempt has been made to focus more on the effect of viscosity on hyperelastic materials. Hence, an annular–circular structure made of visco-hyperelastic material has been assumed. The geometry of the analyzed problem is shown in As can be seen, the inner radius of the structure is In this research, an attempt has been made to perform two nonlinear elastic properties simultaneously with the changes in the deformation of the material with respect to the strain rate. In other words, large strains against the strain rate are considered. The structure’s material is considered in such a way that the deformation created in it due to the applied load is different during the duration of the load application. On the other hand, the final deformation of the structure does not happen as soon as the load is applied, and it takes place over time. The Kelvin–Voigt model is used to consider the viscoelastic properties of the material . AdditioStress and strain will be written according to the following equations based on the Kelvin–Voigt simulation.It can be seen in In a linear elastic material, it can be observed that the changes in stress and strain are linear. However, in a structure with nonlinear elastic property, the stress increases nonlinearly with the increase of strain. In this research, the material with nonlinear elastic properties is assumed. Therefore, calculations based on nonlinear elasticity theory are considered. Various mathematical simulations and theories have been presented regarding the analysis of nonlinear elastic or hyperelastic structures ,10,11,12The strain field is assumed based on FSDT asIn the above equations, y method ,44,45  to solve the governing equations ,44. AccoNow, a system of algebraic equations can be obtained by inserting the functions (Equations (10)–(12)) in the mathematical definition of the boundary conditions at the edges and constitutive equations. Finally, by solving the obtained algebraic equations, the unknown functions in the displacement field and, accordingly, other unknowns, including stresses and strains, will be obtained for the visco-hyperelastic annular–circular sheet under uniform transverse load.First, the accuracy of the used solution method is checked. As can be seen, the accuracy of the results increases versus the increase of nodes in the calculations. A sudden jump is observed between the results of To investigate different boundary conditions on nonlinear-elastic analysis, The viscoelastic property in nonlinear elasticity analysis is a significant issue considered in this research. Therefore, In The new theory of hyperelastic structures can be used with appropriate confidence for viscoelastic properties.Structures made of nonlinear elastic material are sensitive to changes in applied transverse loads, and the changes are entirely nonlinear, even with the low-load application.For low loads, it has a significant impact on the deformation by the boundary conditions. However, as the load increases, these effects decrease.As the viscosity increases, the duration of the final deformation increases, which has a direct relationship with the viscosity of the material.The present study investigated the viscoelastic analysis of nonlinear elastic (hyperelastic) materials. The governing partial differential equations and mathematical definitions of boundary conditions were derived based on a new method and were solved by the semi-analytical method based on polynomials (SAPM). The viscoelastic property of the structural material is assumed by Kelvin–Voigt modeling. In general, the significant results obtained from the research can be categorized as follows:


Posterior pharyngeal flap palatoplasty (PPF) is one of the most commonly used surgical procedures to correct speech, especially for patients suffering from velopharyngeal insufficiency (VPI). During PPF, surgeons use the catheter to control the lateral velopharyngeal port on each side. Airway obstruction and sleep apnea are common after PPF. To understand the air dynamics of the upper airway after PPF, we used computational fluid dynamics (CFD) to demonstrate the airflow. In our previous study, we have revealed the expiration process of the upper airway after PPF and shown the features of how PPF successfully restores the oral pressure for speech. In this study, we focus on examining the inspiration process. Normal airway structures were included. For the normal velopharyngeal structure, one cylinder was applied to each model. For recapitulating the velopharyngeal structure after PPF, two cylinders were used in each model. The ports for borderline/inadequate closure, which can help the oral cavity get the required pressure, were chosen for this study. A real-time CFD simulation was used to capture the airflow through the ports. We found that the airflow dynamics of the upper airway’s inspiration were dependent on the velopharyngeal structure. Although the airflow patterns were similar, the velocities between one-port and two-port structures were different, which explained why patients after PPF breathed harder than before and suggested that the one-port structure might be a better choice for secondary VPI reconstruction based on the CFD analyses. A typical speech requires the soft palate to rise and make contact with the posterior pharyngeal wall to close off the nasal cavity from the mouth. Velopharyngeal insufficiency (VPI) happens when this contact is loose, or sometimes no contact exists. Under these circumstances, air can leak into the nose, causing hypernasal vocal resonance and nasal emissions . Althoug2 area, and according to its flexibility, it can help control the ports up to 10 mm2. The concept of controlling the size of the port in 10 mm2 was based on Warren’s pressure-flow device outcomes, which demonstrated that inadequate closure happened when the velopharyngeal port area was more extensive than 20 mm2  analyses for velopharyngeal conditions, the airflow dynamics were much more complicated  in the upper airway after PPF . In thisStudy individuals and airway model reconstructions were described in our previously published study . The norThe real-time CFD simulation was performed under laminar, steady-state airflow at 35°C in the inspiration direction , 20. TheT-test was applied to compare the velocity and pressure levels at the orifices between two manipulations of the same individuals. P-values <0.05 were considered to be significant.For testing the difference between PPF (two ports) and normal velopharyngeal closure (one port), the velocity and pressure at the orifice areas at the end of the calculation process (0.1s) were used. A paired The research protocol was censored and approved by the Ethics Committee of West China Hospital of Stomatology, Sichuan University . Individual participants could not be identified during or after data collection. Written informed consent was acquired from all the individuals enrolled in this study.Computational fluid dynamics demonstrated airflow velocity patterns. Differences can be found between different individuals. In individuals 3 and 5 , the maiT-test was used to compare the velocity and pressure at the orifice between one-port and two-port structures (ructures . The velFor decades, PPF palatoplasty has been applied to secondary VPI for restoring speech and correcting abnormal hypernasal vocal resonance and nasal emissions , 22. AftAs imaging and computational tools have been improved over the past decades, CFD has become an efficient method for researching patient-specific airway models . It has 2 (The mechanism of PPF, which was to connect the posterior pharyngeal wall physically, was clear . The por2 .2, similar to Warren’s recommendation of 10 mm2 (Our previous study has found that the area should be around 9.67 mmf 10 mm2 . HoweverAs we controlled the ports not based on the area but the velopharyngeal function status, the ports’ total areas were different under different manipulations of the airway model from the same patient sample. We had demonstrated the reasons in our study that the ports’ areas could not be simply divided as the whole airflow system had been changed after changing the structure . When weInterestingly, there was no significant difference in the inspiration airflow patterns between one-port and two-port structures. The phenomena were explainable as we controlled the velopharyngeal closure condition. In this study, these two kinds of manipulations, such as one port and two ports, of the same model were used in the areas under which the airway model worked as a borderline/inadequate closure. The total areas were different between the two manipulations. In other words, the one-port and two-port velopharyngeal closures were in the same condition to gather enough oral pressure. The airflow through the same upper airway should show no significant difference regardless of the port’s shape or size when the functions were the same. It needed to be clarified and emphasized that under the same velopharyngeal closure condition as borderline/inadequate closure, the total areas of the ports of one-port and two-port velopharyngeal structures were different, which again proved that the dynamic airflow mechanism of the upper airway is complicated and the two ports cannot be divided from the one port.The velocity at the orifice showed a significant difference between one-port and two-port groups. The velocities of the two-port structure were lower than those of the one-port structure. It could help us understand why patients suffered airway obstruction and had to use mouth breathing rather than nasal breathing . The volIn summary, we first used the simplified models to define the airflow areas for different VP conditions, which built the foundation for the following several CFD analyses . We compSome shortcomings remained in our models. For example, due to the need for demonstrating how the changing of port areas affects the airflow and a lack of a more realistic port structure, the two ports were simplified as two cylinders. Due to the simplification, we did not select the patient samples with VPI, but the validation should be done in the future. This study had to follow our last publication about the expiration of PPF and applied the same manipulations to the models. Moreover, the inspiration rate could not stay constant under real circumstances, although this is negligible due to the recording’s short duration. The setting of acquiring the data at 0.1 s was used to compare the airflow differences between structures, which can be changed according to the purpose of the study and should be validated with clinical measurement for the time of getting enough oral pressure before the speech. The airway resistance should be analyzed, and the validation of this CFD methodology by comparing the model predictions with actual surgical outcomes should be completed in future studies. Different times of daily life also could affect patients’ inspiration, such as at rest or during exercise and sleeping or awake so that the inlet condition might change. ANSYS Discovery Live is a good example for clinical use, which shows directly to the patients how the airflow through their airway quickly; however, if increased accuracy and high-fidelity of the calculated outcomes such as pressure or velocity are required, CFD software based on finite elements is recommended.Airflow dynamics of inspiration in the upper airway were found to be dependent on the velopharyngeal structure. Although the airflow patterns were similar, the velocities between one-port and two-port structures were different, which explained why patients after PPF breathed harder than before and suggested the one-port structure might be a better choice for secondary VPI reconstruction.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Ethic Committee of West China Hospital of Stomatology, Sichuan University. The patients/participants provided their written informed consent to participate in this study.CY, JaL, and HL contributed equally to this study and analyzed the data. CY, JaL, HL, NC, and XY contributed to the collection of data. CY, JaL, HL, NC, XY, BS, and HH contributed to writing and revising this study. BS, JtL, and HH supervised the study. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


MediHand Trace system) was a system designed to monitor and provide real-time feedback for handwashing in a hospital environment. It was functional for this task, although not sturdy enough for long-term use in a farm environment. The second system was a prototype designed for barns and with foot mats allowing the monitoring of footwear management as well as handwashing. These pilot studies have shown that real-time feedback helps improve compliance. However, the efficacy of the systems was very dependent on the physical set-up of the anteroom.People can act as mechanical vectors, and introduce and spread infectious diseases on farms. Preventive measures, such as changing boots and washing hands, need systematic implementation to manage this risk. Unfortunately, biosecurity compliance regarding biosecurity measures in barn anterooms has been shown to be generally low in all animal production systems. Indeed, the main challenge with biosecurity is maintaining compliance. The development of an effective on-farm biosecurity program requires several elements. These include farm and barn designs facilitating implementation of biosecurity measures; consistently communicating with all personnel and visitors informing them about threats and biosecurity; training programs for all farm personnel, explaining why biosecurity is effective in preventing infectious disease transmission, which measures are needed, and how to best implement them. All these components would be further optimized if automated monitoring systems were implemented with feedback mechanisms. Technologies are now available and are being adapted to the farm context to monitor biosecurity compliance. Two pilot projects using radio-frequency-identification-based (RFID) real-time continuous automated monitoring system quantifying hand sanitizing and boot compliance were conducted. The first one ( Biosecurity is a health strategy, a dynamic balance between a host and its environment, aimed at protecting an animal population from transmissible infectious agents . It reduCampylobacter contamination by more than three times  real-time continuous automated monitoring systems.A RFID continuous automated monitoring system has been developed to monitor compliance with hygienic behavior and provide regular feedback, in-real time and/or through pre-defined scheduled short message services (SMS) . When evaluated against video recordings in a hospital environment, the RFID system was found to be accurate (99.02%), sensitive (95.65%) and specific (100%) . This syMediHandTrace  technology in a farm production environment. The study also aimed at monitoring and evaluating the frequency of two biosecurity measures: hand washing and changing of boots according to the different biosecurity areas (clean/dirty) during a visit to a barn, and providing real-time feedback and employee performance evaluations. As the project was exploratory, a convenient sample of two farms near the Faculty of Veterinary Medicine in St-Hyacinthe, Quebec, Canada was chosen. Farms had to have a hygiene barrier in their barn entrances (red line or a bench) and require the targeted biosecurity measures. The selected barn had to have animals in production at the time of the study.In 2018, this RFID technology was adapted to quantify boot and hand sanitizing compliance when entering and exiting a barn, although it was not designed for poultry farm conditions. The main objective of this pilot project was to confirm the adaptability of the Employees were asked to participate on a voluntary basis. As per the Université de Montréal ethic approval , a consent form was completed by each participant. Recruitment was done in person. Explanations were given verbally and supported by written documents specifying in detail the purpose of the study as well as how to proceed. Diagrams and poster describing the steps to follow when entering a barn were also presented, and then installed in the barn entrances of participating farms throughout the study.Each employee was assigned a pair of farm boots with a rigid chip inserted in the sole of each boot for the duration of the project. With their approval, soft chips were inserted under the sole of the shoes that participants used to come to work. Each employee could then be assessed individually during their barn visits. Visitors were asked to wear specifically identified boots. Identification of the visitors was done using a logbook. At the end of the observation period, the chipped boots were recovered by the research team and the chips implanted in the participants' shoes were removed. The data were collected using the RFID system equipped with an antenna installed on the floor of the clean area and connected to the alcohol-based sanitizer, detecting hand sanitization and chipped farm boots . Detectivia a sound alarm was sent by the system reminding people to comply. The system was also designed to monitor if the biosecurity breach was corrected after hearing the alarm. The MediHandTrace device (e-box) had a data management program that allowed data collection via a USB key. Data were extracted at the end of the study period.Detection of chipped farm boots or personal shoes by the antenna (entering or leaving the barn) without pressure detected on the hand sanitizer device indicated a lack of compliance with hand hygiene. The system was thus able to monitor hand sanitizing compliance when entering and exiting the barn. In the event of a breach in applying this biosecurity rule, an immediate feedback This RFID system was tested on two farms in 2019: an egg layer farm and a broiler farm. A questionnaire was also designed to evaluate employees' appreciation of the system.For the egg layer farm, 254 entries and exits  by four different employees were recorded between February 18 and March 6, 2019 (17 days). The average number of entries and exits per day was 15, with no difference between the week days and the weekend. Boots were donned in the clean area 122 times out of 131 opportunities (93%). On four visits, boots were changed twice during a same visit. Hands were sanitized 173 times out of 254 opportunities (68%). Three out of four employees fully complied with boot change as their personal shoes were not detected by the RFID system during the entire trial. For the 9 non-compliant shoe events, farms boots were changed twice  after hearing the alarm. Out of the 81 non-compliant hand sanitizing occurrences, 26 (32%) were corrected after the alarm rang. The alarm improved hand sanitizing compliance by 2.5–25% at the individual level. The individual who did not get 100% compliance for changing boots also had a high compliance for handwashing (73%), which increased by 10% once the alarm system was activated. The individual with the lowest handwashing compliance rate (16.8%) had the highest improvement (25%) after hearing the alarm. The employee with the highest handwashing compliance rate (91.7%) was not influenced by the alarm.For the broiler farm, 56 entries and exists  by three different employees were recorded between May 27 and June 8, 2019 (13 days). The average number of entries and exits per day was 5 for the week days and 2 for the weekend. Boot compliance was at 100% for the entire period and hand sanitizing compliance was at 73.2% (41 hand sanitizing events out of 56 opportunities). Out of the 15 non-compliant hand sanitizing occurrences, one was corrected after the alarm rang.Compliance rates at the farm level are presented in MediHandTrace system allowed for the identification of strengths and weaknesses for that system under barn conditions. With this information, the research team contacted a local private company, Maximus , known for developing farm environmental control technologies, so that they could design a new prototype. System requirements and scenarios were given to the company engineers and a prototype was ready to be tested in 2021. The newly designed barn-adapted RFID-based system was designed to monitor farm boot and hand sanitizing compliance at the individual level when entering and leaving a barn.The first trial using the The general objective of this second pilot project was to confirm the validity of the prototype, a RFID system equipped with three antennas and two pressure mats . The freFarm boots were equipped with RFID chips (placed under the soles) for the duration of the pilot project. Three antennas in the barn entrance were connected to the prototype: the first in the frame of the exterior door identifying the person entering the barn , the second in the dirty area, allowing for the detection of chipped farm boots (indicating biosecurity non-compliance), and the third in the clean area, connected to the hand sanitizer, detecting hand sanitization when entering and leaving the clean area as well as the RFID chips in farm boots (indicating compliance with changing boots). Two pressure mats were also installed in the barn entrance (one in the dirty area and one in the clean area) to facilitate the detection of visitors, assess the direction of people  and detect lack of compliance if farm boots were recorded in the clean area .via a secure Cloud system. The camera had Wi-Fi access for real-time video viewing and had a storage capacity for archiving activities in the anteroom.The on-farm biosecurity automated monitoring system was installed on a turkey farm in Quebec, Canada for 1one month. The barn had a three zone entrance . The system was installed on December 10 2021, and data were collected from that date until January 10 2022. For the first 2 weeks, no feedback on biosecurity breaches was made by the prototype to avoid annoying employees with alarms in case adjustments to the prototype were needed. However, we did record compliant/non-compliant events during these 2 weeks. For the last 2 weeks, the alarm system was activated and were providing real-time feedback when biosecurity breaches occurred. The prototype had a data management program allowing data collection Seven employees and four visitors took part in the study. Overall, 105 visits were recorded, 73 during week days and 32 during weekends. Visits in the morning accounted for 57% of all visits . Employees did on average 15 visits each during the study period according to their access cards read by antenna 1 , 5. The Detections of non-compliance with biosecurity measures were calculated as per the camera installed in the barn entrance and the RFID system. When entering the clean area, the camera recorded 21 (20 %) non-compliant farm boot events, i.e., the person did not don farm boots, 16 in the first 2 weeks ; 5 in the last 2 weeks . The prototype recorded 68 (64.8%) non-compliant events, i.e., a person was detected by the pressure mat in the clean area, but the RFID chip was not detected by antenna #3 . The discrepancy was attributed to antenna #3 not being sensitive enough to detect RFID chips inserted in farm boots. This problem can be easily adjusted. In addition, only one farm boot for a given pair was chipped. Chipping both boots should increase detection. When entering the clean area, the camera recorded 53 (50%) non-compliant hand sanitizing events, 35 in the first 2 weeks ; 18 in the last 2 weeks . The prototype recorded 89 (85%) non-compliant events, 60 in the first 2 weeks ; 29 in the last 2 weeks . When leaving the barn, the camera recorded 60 (57.7%) non-compliant hand sanitizing events, 41 in the first 2 weeks ; 19 in the last 2 weeks . The prototype recorded 95 (91.3%) non-compliant events, 59 in the first 2 weeks ; 36 in the last 2 weeks . The discrepancy was attributed to the fact that the prototype was programmed for a two-area barn entrance design and it was expected that hands would be sanitized in the clean area when entering and leaving a barn. The selected barn had a three-zone entrance for which hands are expected to be sanitized in the intermediate area (before moving to the clean area when entering the barn and before moving to the dirty area when leaving). Therefore, the prototype overestimated hand sanitizing non-compliance. This can be fixed by allowing hands to be sanitized before detection of a person on the pressure mat located in the clean area when entering the barn and after detection on the pressure mat when leaving the barn.MediHandTrace system confirmed the ability of the system to monitor boot and hand sanitizing compliance. Having a continuous monitoring system seems to improve biosecurity compliance with almost twice the compliance percentage compared to previous studies using hidden cameras . In addition, the system was not able to detect if someone was going back and forth in the clean area or leaving the barn, resulting in potentially hand sanitizing non-compliance overestimation. Furthermore, we had to assume that participants would always use the personal footwear that they agreed to be set with a chip when coming to work. In the situation where participants would have worn unchipped personal footwear and would not have followed the biosecurity rules, the RFID system would not have detected this visit and would not have recorded any information on compliance, which is a significant limitation. Pressure mats are needed to detect when people enter and leave the barn, instead of relying on chipped personal footwear to be detected. Finally, visitors were difficult to monitor as most would likely only wear disposable plastic boots, which are not chipped, thus not detected by the antenna. Consequently, the system had limited capacity in monitoring visitors' compliance.MediHand Trace system was useful to design the Maximus prototype with the objective to have a more in-depth compliance monitoring by adding pressure mats and antenna in the dirty area. However, as we fixed the limitation associated with having no antenna in the dirty area, we added a challenge, since the extra antenna cannot simply be located in the dirty area without considering the design of each barn anteroom. Farm 1 was an egg layer farm with a barn entrance well-designed (corridor-type). Farm 2 was a broiler farm with a barn entrance with significant space limitation to display the antenna and hand sanitizer. The corridor-type barn entrance was more suitable for the RFID system and was not impacting the daily biosecurity routine, while the second farm was not as intuitive.The trial with the MediHand Trace trial, it was possible to notice that biosecurity breaches were lower when alarms were activated on the Maximus prototype. Participants reacted each time the alarm sounded, often wondering what they may have done wrong. As biosecurity protocols in barn entrance are similar on poultry and swine premises, it is expected that the next trials will be performed on swine farms.Even if the initial Maximus prototype did not perform as expected, the trial allowed to better understand the impact of the barn entrance design in monitoring compliance and make adjustments accordingly. Indeed, as biosecurity compliance was monitored using cameras to compare with biosecurity breaches recorded by the RFID prototype, it was possible to identify the system limitations after the first tested farm and apply changes for upcoming farms. As seen with the Monitoring biosecurity is needed to maintain compliance. Relying on video surveillance is too burdensome and expensive to be largely implemented. However, relying on new technologies will improve biosecurity compliance and reduce the likelihood of introduction and spread of infectious diseases. Having evidence-based results will help convincing producers of the importance of these measures and of providing feedback for maintaining biosecurity compliance. These pilot studies have shown that real-time feedback helps improve compliance. However, the efficacy of the systems was very dependent on the physical set-up of the anteroom. An implementation of the technology by producers will allow us to collect long-term data without the potential bias associated with assessments following instructions.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Université de Montréal Ethics Committee . The patients/participants provided their written informed consent to participate in this study.MediHand Trace system to a barn environment, recruiting the company Maximus for the design of their prototype, helped creating the prototype and supervised the technician and the student working in the field, and also produced the first draft of the paper. J-PV provided the funding for the purchase of the MediHand Trace system, recruited and covered the salary of the veterinary student and assisted with her supervision, and also covered the expenses associated with the technician, contributed to the design of the Maximus prototype and edited the article, adding to its different sections. DT and A-MC conducted the field work, collected the data, contributed to adjustments for the two systems, identified limiting factors, and proposed solutions. All authors contributed to the article and approved the submitted version.MR had the original idea and produced the initial design of the pilot studies, including adapting the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This paper presents a numerical investigation of the shear creep behavior of the adhesive joint in concrete structures strengthened by externally bonded fibre-reinforced polymers (FRP) composites. Based on experimental data collected in a previous study, creep constitutive equations were developed for the adhesive layer and implemented into a finite element code. The proposed model extends the classical one-dimensional formulation of Burgers creep model to a fully 3D model and introduces the nonlinearity of the model parameters. This numerical approach was first used to simulate the nonlinear creep behavior of bulk epoxy samples; it was then extended to predict the nonlinear creep response of the FRP-concrete interface in double lap shear specimens. Globally, a fair agreement was obtained between numerical results and experimental evidences. As a main result, it was found that creep induces a redistribution of the interfacial shear stress along the FRP-concrete lap joint, leading both to a stress relaxation near the loaded end of the adhesive joint and to an increase in the effective transfer length. Externally Bonded (EB) Fiber Reinforced Polymer (FRP) strengthening systems have been widely adopted for the rehabilitation of Reinforced Concrete (RC) structures since the late 1990’s. The effectiveness of this retrofitting method has been clearly demonstrated in the literature ,3,4,5,6 The viscoelastic response of bulk polymer materials has been widely investigated in the literature, through both experimental studies and theoretical approaches. Experimental works rely most often on tensile tests at constant load levels ,21,22,23Hadjazi et al.  also stuIn this study, a simple fully 3-D formulation of the creep behavior of the adhesive layer is proposed, that considers the stress-dependency of model parameters. The main originality of this approach lies in the proposal of a multi-axial creep model developed in a 3D tensor formalism. In this framework, the nonlinearity of the adhesive creep is managed by correlating the model parameters with the mechanical stress state of the polymer material. This approach could also be enriched at a further stage by taking into account the dependency of the adhesive creep upon hygro-thermal conditions. A second originality of the model is the use of the constitutive law generator MFront, which allows a portability of the behavior law between different FE codes  without further development. The proposed model is then used to predict the creep behavior of double shear specimens, and the numerical results obtained are compared to those previously obtained by Houhou et al.  for the When an FRP retrofitted RC structure is exposed to sustained load, concrete creep occurs in the compression zone, FRP laminates are subjected to tensile creep and the adhesive joint is rather subjected to shear creep. In this paper, we will focus on the creep behavior of the adhesive layer, as this latter is considered to bring the highest contribution to the delayed response of the overall assembly near the loaded end of the adhesive joint. Moreover, in actual repair works, the RC beam has generally been subjected to sustained load for a long period before application of the FRP strengthening system and, thus, concrete and steel have already experienced significant part of their creep strains. However, the proposed model could be applied  to all the component materials of the FRP-strengthened RC structure. In the following section, the key characteristics of the adhesive joint are discussed. As proposed in , an uncrAlthough creep of polymers is known to be dependent on time, stress, and environmental conditions , the proFor the sake of clarity, the one-dimensional Burgers material, which is the base of the present model, is depicted . HoweverTherefore, the total strain rate is decomposed into three terms:From Equation  it can bThe elastic part of the strain rate, corresponding to instantaneous strain response, is given by Hooke’s law:The evolution of the viscous part of the strain (Equation ) and theEquation . Thus, tver time ,38 and iEquation .(5)Gkv=j refers to Experimental evidences from the literature ,39,40 haodzewicz , the nonEquation ) is usedquation (σ¯=(α+1)2MFront constitutive law generator ,43,44 avFor the aforementioned -The increment of total strain:-The increment of elastic strain:-The increment of the two viscous strains:f of strain increments, the implicit equations associated to the state variables are given by:For the resolution of these equations, the quantities t in between For a given time With this first estimation of the stress increment Equation ) at t+θΔNonlinear parameters are computed according to Equations  and 6b)6b).The new increment quations , 11b) a aΔσi+1 wquations  is deterIf the minimization criterion is reached with the computed stress increment, then the minimization process stops, otherwise it returns to stage 2.i is incremented and the process goes to stage 1.Finally, these equations are solved by a Newton-type optimization method. The solving algorithm is presented in  and compAt the beginning of the time step, the nonlinear parameters  . A Newto® from Sika Company, Stuttgart, Germany) were subjected to sustained tensile loading. In this work, compliance master curves were constructed from series of a thermally stimulated tests according to the TTSP method. These master curves made it possible to obtain reference creep curves of the bulk epoxy adhesive over a 30-day period and for three load levels  , providita sheet . A valuetal data  and are Simulations provided by the two modelling methods (use of Mtest or combination of MFront-Code_Aster) are found consistent and are also fitting very well the experimental evidences . MoreoveThe second application is concerned with the modelling of a creep test on a double shear specimen previously developed by Houhou et al. ,45. The  surface . These sDue to the symmetry of the system (geometry and boundary conditions), only one eighth of the system is modelled b consistisplayed a and the−6 at the loaded edge of the adhesive joint and fall to zero at 140 However, the elastic and linear creep models tend to underestimate the strain values along the adhesive joint, both on the instantaneous and delayed responses. Differently, the nonlinear creep model captures well the strain profile from the loaded edge of the joint to a distance of 40–60 The modelling of adhesive shear creep was conducted using a creep model calibrated on tensile creep experiments. This simplified approach previously used by other authors ,29 was aTo assess the influence of the properties of each component material  on the strain response of the bonded assembly, the effect of a finite variation in the Young’s modulus .(13)τ estimatThis study has proposed a numerical investigation of the shear creep behavior of adhesive joints in the case of concrete structures reinforced by externally bonded CFRPs. This behavior is of particular importance near the loaded edge of the joint which is subjected to stress concentrations. In a first stage, constitutive equations were established using fully-3D formulation in order to describe the nonlinear creep response of the bulk adhesive. They were implemented using MFront tool and following an implicit method. The model parameters were identified by fitting creep strain data of bulk epoxy specimens which were available from a previous study. A FE model was then constructed by introducing the constitutive law in Code_Aster. In order to validate this numerical model, simulations were carried out according to the double shear creep test configuration proposed by Houhou et al. . A good In addition, another interesting point revealed by those simulations is that the creep phenomenon occurring within the polymer adhesive modifies the stress distribution in the bonded joint and mitigates stress concentrations near the loaded edge (in comparison with the stress profile provided by a pure elastic model). This effect seems more effective when considering a nonlinear creep behavior of the polymer adhesive rather than a linear behavior. Furthermore, the simulations suggests that creep leads to an increased transfer length. This feature should be taken into account in the design of bonded FRP reinforcements. Finally, to further investigate the ageing behavior and the creep of adhesive joints, it may be interesting to refine the proposed model by taking into account the impact of environmental conditions such as temperature, relative humidity and material ageing in the modelling process. Furthermore, most of time, externally bonded systems are applied after several years of service and concrete has already experienced most part of its creep potential (basic creep or drying creep). Nonetheless, it may be interesting to consider concrete creep in the modelling in order to improve the robustness of theoretical outcomes.


Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in MRI as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction. The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate clinical diagnosis and adequately treatment. As a result, deep learning-based brain tumor segmentation and classification algorithms are extensively employed. In the deep learning-based brain tumor segmentation and classification technique, the CNN model has an excellent brain segmentation and classification effect. In this work, an integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and classification of brain MRI tumor. A CNN is proposed in the first stage to learn the feature map from image space of brain MRI into the tumor marker region. In the second step, a faster region-based CNN is developed for the localization of tumor region followed by region proposal network (RPN). In the last step, a deep convolutional neural network and machine learning classifiers are incorporated in series in order to further refine the segmentation and classification process to obtain more accurate results and findings. The proposed model's performance is assessed based on evaluation metrics extensively used in medical image processing. The experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary. The segmentation and classification results demonstrate that the proposed model outperforms state-of-the-art techniques by a significant margin. Brain tumors are lumps that arise as a result of aberrant brain cell proliferation and the loss of the brain's regulatory systems. Tumors in the head cranium can grow and strain on the brain, affecting physical health. Early tumor segmentation is an essential research topic in medical imaging's field since it helps doctors choose the best treatment strategy for a patient's health. Over the last several decades, medical researchers have found more than 120 different kinds of brain tumors. There are two types of brain tumors: primary brain tumors that form in the brain and secondary brain tumors that can be found in the brain but arise elsewhere in the body [In most cases, noninvasive medical imaging methods such as computer tomography (CT) and MRI are preferred over invasive procedures for brain tumor segmentation of gliomas, meningioma, and pituitary tumors, allowing clinicians to safely remove tumors within the maximum range . As a reFurthermore, manual segmentation is frequently dependent on the intensity of the image as seen by the human eye, which can be easily influenced by the image quality as well as observer personal observations. It is susceptible to incorrect segmentation and redundant area segmentation. However, the following are the issues that have been identified in the investigation of automated glioma segmentation methods: (1) the difference in pixel intensity between the tumor region and surrounding normal tissues is commonly used to identify brain tumor in images. The intensity differential between neighboring tumor tissues will be flattened due to the existence of a gray-scale field, which will result in blurry tumor borders. (2) It is challenging for image segmentation methods to clearly diagnose the brain tumor as the size, structure, and location of tumor vary . As a reOver the last two decades, medical image segmentation and classification have improved drastically with the advancement in machine learning and computer vision techniques. In recent years, machine learning-based computer-aided diagnostic technology has grown in popularity in medical imaging . MachineA hybrid and integrated classifier based on deep CNN and machine learning classifiers, i.e., random forest (RF), support vector machine-RBF (SVM-RBF), and extreme learning machine (ELM), is proposed for the accurate segmentation and classification of brain tumor into glioma, meningioma, and pituitary tumor. Image registration approach is adopted in the preprocessing of brain MRI scans. The brain MRI images were either linearly or nonlinearly register, merely cropped or padded to the required size. In comparison to no image registration, both linear and nonlinear registration improves the accuracy of the classifier by about 4-5 percent. The performance of the classifier is improved by image registration, although the choice of linear or nonlinear image registration has minimal effect on segmentation and classification accuracy. In the first stage, a CNN is developed to learn the feature map from brain MRI image space into the tumor marker area. The proposed CNN is trained using 3 separate preprocessed brain MRI scans. In the second step, a region-based CNN is proposed for tumor localization, preceded by a region proposal network (RPN). One of the most important problems in medical image processing is the lack of labeled data. As a result, the focus of our research on employing R-CNN-based tumor localization in scenarios when annotated data is limited. We extended the segmentation and classification procedure to build the structure of the next deep CNN and machine learning classifiers in series in order to improve the accuracy of segmentation and classification outputAs part of this research, we were able to provide an end-to-end, systematic method for brain tumor segmentation and classification utilizing brain MRI. The system composed of three parts: brain tumor segmentation with a basic CNN algorithm, tumor localization with a faster R-CNN-based network, and exact tumor segmentation and classification using a deep CNN and machine learning classifier framework. The final outcome of all three algorithms was the exact tumor boundary, which was categorized into glioma, meningioma, and pituitary tumor typesThe experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitaryDeep learning has been applied in medical imaging to identify cells of various sizes and shapes, identify organs and body components, and detect local anatomical features . Deep leThe rest of the paper is organized in the following way: The proposed method is described in Artificial intelligence are largely utilized in image processing techniques for segmenting, identifying, and classifying MRI images, as well as for classifying and detecting brain cancers. There have been several studies on the classification and segmentation of brain MRI images. These technologies use techniques such as conventional image processing and a machine learning approaches based on neural networks to diagnose brain cancers. The authors in  utilizedFurthermore, Papageorgiou et al.  used fuzThe overall architecture of our proposed framework is presented in this section. The contents of four important components are then described in the subsections. Preprocessing is used to enhance image data by removing undesirable distortions and improving specific visual features that are important for subsequent processing. Image registration is a type of image processing that combines several scenes into a single image. When overlaying images, it helps to overcome problems such as image rotation, size, and skew. The process of converting multiple images into the same coordinate system with matching imaging information is known as image registration. It is been used in a variety of clinical settings and medical research. The images to be recorded may be obtained for the same subject using multiple modalities, in the same modality but from separate subjects, or in the same modality but from the same subject at a different time, depending on the medical reasons. For time series analysis or longitudinal investigations, registration may also be done on images recorded over time.Ty − z) were acquired using AIR, which was deployed to each patient's brain mask for dice coefficient measurement. In this work, the brain MRI scans were only registered to linear or nonlinear template, cropped or padded to needed size. It has been showed that image registration increases the accuracy of classifier by about 4-5 percent compare to no image registration. The affine transformation was used to directly align data from each sample to the MNI template, resulting in linear registration. Transformations  from the input image, then executes region-based segmentation on those ROI. Region based-CNN and RPN  are the pj represent the anchor's predicted probability, j shows the anchors index, vj is a vector which represent the four coordinates of identified bounding box, M represents the total size of minibatch, SL is the segmentation loss, P shows the location of anchor, and RL represent regression loss. pj′ represents the positive anchors, and its value is assigned 1 when object lies inside anchor. The value 1 is assigned based on algorithm. vj′ stands for the ground truth box, which is related to a positive anchor. where The segmentation and classification process is improved, and an iterative technique is used to connect the CNN and the interconnected machine learning classifiers such as random forest, SVM-RBF, and extreme learning machine to increase the accuracy of the results. After extracting the bounding box using the proposed faster R-CNN based model, a deep CNN and machine learning classifiers are used to produce a precise tumor boundary. The localized tumor is classified into three groups after segmentation: glioma, meningioma, and pituitary.Each convolutional layer is followed by a max-pooling layer and eventually a fully connected layer in the proposed CNN. A convolutional kernel of size 7 × 7 is multiplied on each input image. For the first, second, third, and fourth convolutional layers, same convolutional kernel of size 3 × 3 is used, as well as filter sizes of 32, 64, 128, and 256 for the first, second, third, and fourth layers, respectively. A kernel size of 2 × 2 is used in each pooling layer. The segmented output of fully connected layer is further given to machine learning classifier for the classification of segmented tumor into glioma, meningioma, and pituitary. The final step is repeated again to refine the segmentation and classification results. For the segmentation and classification process, convolutional layers create a feature map for each point of the input MRI image. Neurons are the most basic and important component of the convolutional layer that creates the feature map. Although all neurons in the feature map must have identical weights, different feature maps result in varied weights in the same convolutional layer, allowing for the extraction of several features at each and every location. Each convolutional layer uses ReLu as an activation function.The major objective and goal of the pooling layer is downsampling, i.e., reducing the resolution and size of the feature map produced from the convolutional layer. Average pooling and maximum pooling are the two most prevalent pooling techniques. The originality of the input image is reduced by the average pooling layer, but the originality of the source image is preserved by the max-pooling layer. The highest value of the feature map is also preserved by the max-pooling layer, which is why it is the major subject of the research. Kernel size of 2 × 2 and stride of 2 is deployed in the max-pooling layer.Following the convolutional layer and max-pooling, the FC layer includes final classification results with distinct classes. In the fully connected layer, a dropout probability of 0.5 is utilized to minimize overfitting problems, and softmax was used as an activation function. The CNN network's layers receive training and testing samples from the previous layer and pass them along to the next layer. Finally, the fully connected layer takes input from the previous max-pooling layer and classifies the feature map into subclasses.N represents the total number of classes, v is vector which shows the ground truth label, and wi represent the output of last layer.The loss function is used to compute the loss, which is the neural network's prediction error. The loss is utilized to calculate the gradients and update the weights of the neural network in the training step. The cross-entropy loss function, which is the most widely used loss function in convolutional neural network, is utilized in the FC classifier's training phase. It estimates the difference between the ground-truth label and the soft target calculated by the softmax function and can be expressed asn random characteristics or features to determine the best splitting position using the Gini index as a cost function. This random selection of characteristics or features helps minimize ensemble error rates by reducing correlation among trees. New attributes and features are given as input to random forest classification tree in order to forecast the class target. Total number of predictions is calculated for each class, and class having most predictions is selected as label for new entity. When exploring the optimum split, the set of features to examine is limited to the square root of the entire number of characteristics. A total number of trees are set from 1 to 150 and choose tree having best accuracy.Breiman  introducK represents the kernel function, vj shows deep features of brain tumor MRI in the form of vector data, zi is the target class, and σi represents Lagrange multipliers.Cortes and Vapnik  proposedM shows the hidden layer output matrix, the weighted vector is denoted by v, while the required output matrix is denoted by T. The fundamental goal of the extreme learning machine technique is to select the optimal strategy for v, which is then utilized to minimize the gap between the network's estimated and actual outputs. If the training dataset and hidden nodes are identical, M will be a square matrix. The input weights and hidden layers are hard to distinguish if M is a nonsquare matrix though.The extreme learning machine (ELM) is a fundamental learning algorithm for feed-forward neural networks with a single hidden layer (SLFNs). Huang et al.  first deWe conduct a series of experiments using three publicly accessible brain MRI datasets for the segmentation and classification of brain tumors. The first dataset was obtained from the Kaggle website  which coThe Adam optimizer (adaptive moment estimation), a technique for stochastic optimization, was used to train our model using 100 epochs and a learning rate of 0.00001. The proposed model's performance and efficiency were validated using evaluation measures. The four primary and fundamental metrics frequently used to evaluate the performance are true negative (tn), true positive (tp), false positive (fp), and false negative (fn). Specificity, sensitivity, PPV , NPV , accuracy, and dice similarity coefficient (DSC) are the classification performance evaluation metrics of the proposed model. Mean square error (MSE), peak signal-to-noise ratio (PSNR), boundary displacement error (BDE), variation of information (VOI), probabilistic random index (PRI), and global consistency error (GCE) are the segmentation performance evaluation metrics.The capability of a model to properly identify relevant brain tumors can be expressed as follows:The ability of a model to properly detects and classifies an actual brain tumor can be expressed asPPV  and precision are similar. It calculates true positive measures, which can be computed using the formula below. The probability of the absence of a brain tumor based on actual negative value can be computed asIt refers to the system's capacity to distinguish between different forms of brain tumors. The following formula was used to determine accuracy:It is a performance metric that can be used to assess sample overlap and can be written asm represents the total image's sample, Xi represents the actual image.It represents the average squared difference between actual and predicted value and can be expressed using following expression:f represents the original image and g represents the segmented and classified image. The ratio of a signal's maximum power to the signal's maximum noise power is what it is called PSNR. PSNR is calculated using peak signal power. The PSNR is expressed in decibels. Let us assume M and N represent the image's size, while P denotes the image's pixels. A higher PSNR number implies a higher quality. PSNR is an excellent quality indicator for white noise interference.∂ denotes the fuzzy relation.The average displacement error between the projected border pixels and the ground truth pixel and can be calculated as follows:x and FSy denote the image fuzzy segmentation, entropy is represented by E(FS), E denotes the combined entropy of two images, and M represents the mutual information of two images.It calculates the distance between the two segmentations in terms of information. The entropy and mutual information are used to define VOI:It is a metric for the algorithm's rate of success in making accurate predictions. The following formula can be used to calculate it:(1) Global Consistency Error (GCE). The GCE determines how much one segmentation may be considered a refinement of another, because they might reflect the same image segmented at various scales. GCE is calculated using the following formula:Sx and Sy denote two segmentations and pi represent position of pixel.The experimental findings clearly show that increasing the system structure and complexity increased the proposed model's efficiency. In terms of texture and intensity, each tumor type belonging to a particular class differs considerably from other tumor type belonging to the same class. The proposed deep CNN and machine learning classifiers can segment and categorize different types of brain tumors, regardless of their appearance or contrast. We compared the effectiveness of the developed deep CNN and ML classifiers on the brain dataset-1 and the Figshare dataset. Tables 4Figures In this part, we compare our proposed technique to prior research that employed the same types of brain tumors but different network topologies and parameters. In this paper, a hybrid and integrated classifier based on deep convolutional neural networks and machine learning classifiers is proposed to improve segmentation and classification accuracy and achieve automatic segmentation and classification of brain tumors in MR images into glioma, meningioma, and pituitary without user intervention. The preprocessing of brain MRI images uses an image registration technique. The brain MRI images were either registered linearly or nonlinearly or simply cropped to the appropriate size. Both linear and nonlinear image registrations have enhanced the classifier's accuracy by around 4-5 percent when compared to no image registration. The implementation of the model is divided into three sections. In the first stage, a convolutional neural network is used to learn the feature map from brain MRI image space into the tumor marker area. A region-based convolutional neural network for tumor localization is presented in the second phase, followed by a region proposal network (RPN) to get the precise tumor contour. The segmentation and classification method is further expanded to create the structure of the next deep CNN and machine learning classifiers in series to enhance the accuracy of segmentation and classification output. The experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary.We plan to expand this research in the future by experimenting with larger datasets and other tumor kinds. As a result, the suggested framework may be implemented as a useful system for doctors to give acceptable medical treatment methods for brain tumor early detection. The proposed model, however, still has flaws, such as a long computation time. The next study topic will be how to improve the algorithm and reduce the running time. Our work with CNN to determine the specific location of the tumor is likely to grow in the future with 3D brain imaging.


Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the angle of the movement, but this method is time-consuming, and it will interfere with the movement of the patient. In the recent outbreak of the coronavirus disease, the need for remote diagnosis and treatment of CD has become extremely urgent for clinical practice. To solve these problems, we propose a multi-view vision based CD severity scale scoring method, which detects the keypoint positions of the patient from the frontal and lateral images, and finally scores the severity scale by calculating head and neck motion angles. We compared the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) subscale scores calculated by our vision based method with the scores calculated by a neurologist trained in dyskinesia. An analysis of the correlation coefficient was then conducted. Intra-class correlation (ICC) was used to measure absolute accuracy. Our multi-view vision based CD severity scale scoring method demonstrated sufficient validity and reliability. This low-cost and contactless method provides a new potential tool for remote diagnosis and treatment of CD. Dystonia was first proposed by Oppenheim . In 2013In clinical practice, the target muscle of botulinum toxin therapy is usually selected by the abnormal movement pattern of the patient. According to the clinical manifestations, the abnormal movement patterns of patients can be classified as torticollis (rotation in the transverse plane), laterocollis , retrocollis , anterocollis , or a combination of the above . With thAccurate scoring of the severity scale is significant for botulinum toxin therapy. There are three methods commonly used in the clinical scoring of the severity scale. The manual assessment uses a head protractor  to assesThe Inertial Measurement Unit (IMU) based method uses a multi-axis sensor combination device  to assesX-ray assessment accurately determines the abnormal movement patterns of dystonia by comparing the positions of different vertebral bodies and the cross-sectional area of the corresponding muscles in the neck through multi-angle X-ray photography of the head and spine. This method is more accurate than manual assessment and sensor assessment, but it increases patient radiation exposure, and the operation is more complex. Conventional Computed Tomography (CT) requires the patient to be supine, which may interfere with the patient’s abnormal posture. Therefore, the assessment of abnormal patterns of CD needs to be completed under the special orthostatic CT.In recent years, vision based human pose estimation (HPE) has made significant progress and has begun to be widely used in related fields. Two-dimension (2D) HPE methods such as Convolutional Posture Machine (CPM) , HourglaAbove traditional methods requires experienced neurologist trained in dyskinesia and professional medical equipment. In many areas where medical resources are scarce, it is difficult for CD patients in these areas to obtain effective diagnoses. The recent outbreak of Corona Virus Disease 2019 exacerbates the shortage of medical resources. Many CD patients cannot go to the hospital for diagnosis and treatment, which delays the best opportunity for rehabilitation. Therefore, it is urgent to study a low-cost and portable method to score CD scales without a neurologist trained in dyskinesia and professional medical equipment and even realize remote diagnosis at home.One of the challenges of vision based methods is how to accurately describe the head and neck movement of each CD abnormal pattern using the vision based HPE, since the abnormal movement pattern of CD is complex and changeable. Another challenge is the deep ambiguity in estimating 3D body keypoints from a single 2D image. Using multiple views to estimate 3D human keypoints is a preferred solution. Another preferred solution to solve the depth ambiguity problem is using a depth sensor. Yu et al. proposed DoubleFusion  using a We propose a multi-view vision based method for scoring the CD severity scale. It measures head and neck movement angle by calculating the 3D keypoint positions obtained by frontal Azure Kinect and the 2D keypoint positions obtained by the common lateral camera.We conducted a pilot study to compare the subscales of the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) severity scale calculated by the multi-view vision based method with the manual method scores rated by a neurologist trained in dyskinesia. The results show a good correlation and agreement between the two methods. It demonstrated sufficient validity and reliability of the multi-view vision based method.We compare the subscales of the TWSTRS calculated by the multi-view vision based method with the scores rated by the single-view method and wearable IMU based method. The results show that our multi-view vision based method has higher accuracy and robustness than the single-view method and wearable IMU based method.This study aimed to study a low-cost and portable vision based method for scoring CD scales. The main contributions of this study can be summarized as follows:Compared with the traditional scoring methods, our vision-based method uses the patient’s image to assess the abnormal movement pattern. This method only uses the camera device to collect image data without direct contact with the patient. While maintaining the accuracy of the assessment, it is convenient, quick and easy to be applied in remote diagnosis and treatment. Compared with the single view methods, our multi-view method has significantly improved accuracy at the cost of adding a lateral camera.The work is structured as follows: In The Institutional Review Board of Tongji Hospital, Tongji University School of Medicine approved this pilot study. This study was conducted based on the clinical videos from the Neurology Department of Tongji Hospital. There are now 31 patients participating in our pilot study. Of these, 8 participants have completed data collection and 23 others are waiting for data collection. The data contains 24 frontal videos, 24 lateral videos and 24 IMU data of 8 CD patients. The movement disorder-trained neurologist collected detailed participant information and scored the TWSTRS severity scale. Frontal videos were captured using an Azure Kinect  at 30 frThe multi-view vision based scoring method uses the Azure Kinect to capture the frontal color and Infrared Radiation (IR) images and the HP 320 FHD Webcam to get the lateral color image, as shown in The flow chart of our method is shown in The keypoints of the subject used in our method are shown in The YOLOv3 model, which performs well in the subject detection task, is used to obtain subject bounding boxes in the lateral image. The YOLOv3 model is a single-stage object detection model, which can maintain high detection accuracy while having a high detection speed, and occupies fewer computing resources. It is suitable for real-time medical diagnosis. To improve the accuracy of the YOLOv3 model for subject detection, we pre-trained the YOLOv3 model with the Common Objects in Context (COCO)  dataset.The HRNet model is used to estimate the subject’s 2D keypoint positions. The backbone network of HRNet has maintained high resolution rather than recovery from the low-resolution features. Through up-sampling and down-sampling with mutual connection, the parallel network realizes the multi-scale feature extraction and fusion. It solved the need for a reliable high-resolution feature map in the task of human keypoint estimation.With the lateral 2D keypoint positions of the subject, the antecaput/retrocaput and antecollis/retrocollis subscale of the TWSTRS severity scale can be scored. For antecaput/retrocaput, the angle between the vector passing ear-nose and the horizontal vector in the sagittal plane is calculated. For antecollis/retrocollis, the angle between the vector passing neck-ear and the vertical vector in the sagittal plane is calculated.Both multi-view and single-view methods use the same human keypoints to calculate the motion angle. So the scheme of using human keypoints to calculate the head and neck motion angle is the same, as shown in Manual measurement is the method used by the neurologist trained in dyskinesia to score CD scales. This method uses the protractor to measure the neck and head motion angle. For antecaput/retrocaput, the angle between the vector from the external auditory foramen to CZ (the top of the head) and the vertical vector in the sagittal plane is calculated. For antecollis/retrocollis, the angle between the vector passing through the midpoint of the neck and parallel to the neck contour and the vertical vector in the sagittal plane is calculated. For latercaput, the angle between the vector from the nose to the glabellum and the vertical vector in the coronal plane is calculated. For laterocollis, the angle between the vector from the midpoint of the thyroid cartilage to the suprasternal notch and the vertical vector in the coronal plane is calculated. For torticaput, the angle between the vector from nose to external occipital protuberance and the horizontal vector in the sagittal plane is calculated. For torticollis, the angle between the vector from the midpoint of the thyroid cartilage to the C4 spinous process and the horizontal vector in the transverse plane is calculated.The IMU based method captures the neck and head motion angle from 3 LPMS-B2 attached to the subject body. The LPMS-B2 is a wearable inertial sensor released by Alubi. It can capture 6 degrees of freedom data from a triaxial accelerometer and a gyroscope at 400 Hz. Its weight is 12 g, and its size is 39 mm × 39 mm × 8 mm . As showIn this study, three methods were used to score the rotation (torticaput/torticollis), laterocollis (latercaput), and antecollis/retrocollis (antecaput/retrocaput) subscales of the TWSTRS severity scale, which are the multi-view vision based method, neurologist trained in movement disorders and the wearable IMU based method. The antecollis/retrocollis subscale was also scored by the single-view vision-based method, which calculated angles only using the frontal keypoint positions captured by the frontal Azure Kinect.Data were analyzed to ascertain the coefficient of correlation between the subscales of the TWSTRS calculated by the multi-view vision based method and the scores obtained by a movement disorder-trained neurologist. The coefficient of correlation between the subscales of the TWSTRS was calculated by the sensor based method, and the scores obtained by a movement disorder-trained neurologist were also ascertained. Spearman’s correlation was used to evaluate the relative agreement between these methods. Intra-class correlation (ICC) and 95% limits of agreement were used to measure absolute accuracy. The SciPy library v1.7.3 was used to calculate Spearman’s correlation, and the Pingouin library v0.5.1 was used to calculate ICC.p < 0.05) was obtained in rotation, 0.727(p < 0.05) was obtained in laterocollis, and 0.739(p < 0.05) was obtained in antecollis/retrocollis. We compared our method’s validity and accuracy with those of Nakamura’s method [As shown in s method , which uIn this study, we also compared the validity and accuracy of the multi-view vision based method with the wearable IMU based method. Spearman’s correlation coefficient and ICC between the scores was calculated by the wearable IMU based method and neurologist are shown in As the ablation experiment, Spearman’s correlation coefficient and ICC between the score of antecollis/retrocollis subscale measured by the single-view vision based method that only uses frontal image and neurologist were ascertained to study the necessity of adding the lateral image of the subject. As shown in In addition, we counted the actual costs of implementation and maintenance of the above methods, as shown in Our results are comparable with a previous study that used a single Kinect v2 to capture the 3D keypoint positions of the subject . As showThe Azure Kinect includes a higher resolution depth sensor and deep learning-based body tracking algorithms; a convolutional neural network model might provide more accurate kinematic measurements than Kinect v2, which uses the random forest model . The BodThe previous study showed poor agreement in antecollis/retrocollis, since pitch errors are always higher than yaw and roll errors when using Kinect v2 . Clark eCompared with the vision based method, the wearable IMU based method has mediocre validity and accuracy. The physical contact between the sensor and the patient’s body will inevitably interfere with the patient’s movement, and the patient will feel uncomfortable. The wearable inertial sensors are attached to the subject’s skin, but the position of the sensor will deviate each time, and the muscle movement state and the skin movement state will be inconsistent. The drifting problem of the IMU also might cause errors. All these factors will cause errors in the measurement.The device cost of vision based method is higher than manual measurement. But the medical resources cost of vision based method is significantly lower than manual measurement. Our vision based method can automatically assess abnormal movement patterns without a neurologist trained in dyskinesia. Compared with a sensor based method, the device cost for the vision based method is slightly lower. Moreover, the sensor based method also requires a doctor to attach the sensors to the correct position on the patient’s body.The Azure Kinect has a similar performance to the Vicon Motion System, another body tracking system in real-life scenarios . CompareThis study has some limitations that are important to be aware of. Firstly, the abnormal pattern of CD is complex and diverse, which can be combined by different base abnormal patterns. For example, some patients might have symptoms of both antecollis and laterocollis. However, there were only 8 participants enrolled in this study, which is not sufficient to cover the whole breadth of abnormal patterns. It might limit the universality of these results to the majority of patients. Furthermore, our current method calculates the motion angle separately for each abnormal pattern and then scores the scale without considering the correlation between each abnormal pattern. In fact, for most of the obtained patient’s diagnosis reports, there is a certain correlation between abnormal patterns. For example, laterocollis is often accompanied by latercaput, and antecollis is often accompanied by antecaput. The symptoms of one abnormal pattern might affect the angle calculation of another abnormal pattern, which might cause errors.Different from Parkinson’s disease, abnormal level assessment and gait analysis, which just use the human skeleton for analysis, the scoring of the CD severity scale required detailed position information of the human head and neck. However, the existing keypoints of human datasets such as COCO and Human3.6 M  are usedThere is another limitation that must be mentioned. Although the vision based method using Azure Kinect is lower cost and more efficient than the traditional method, the Azure Kinect Body tracking SDK has stringent computer host hardware requirements. The recommended minimum Azure Kinect Body Tracking SDK configuration includes Intel Core i5 Processor and NVIDIA GEFORCE GTX 1050 or equivalent. As shown in In future work, more and more CD patients will gradually participate in this study. With continuous patient data collection, when data reaches a large number, the machine learning methods, which need a lot of patient data to train the model, can be considered to study the correlation between base abnormal patterns.Artificial intelligence technology such as deep learning has been widely applied in intelligent medicine. To further promote the development of automatic diagnosis and telemedicine based on computer vision, future work could start with building a dataset suitable for use in medicine. The dataset can describe more special keypoints of humans. In this case of the scoring scale of CD, the dataset for neck and head motion analysis containing special human keypoints, such as spinous processes, should be built.In order to make intelligent diagnoses benefit more patients, future work also could focus on studying how to reduce the computational complexity and memory consumption of the current method. That will be a lightweight deep learning vision based method that can run on common smartphones.This study proposes a multi-view vision based CD severity scale scoring method. This method uses the Azure Kinect to capture the 3D keypoint positions and a common camera to capture the lateral 2D keypoint positions. Finally, it calculates the head and neck motion angles to score the TWSTRS severity scale. The experiments showed a good correlation between our method and the movement disorder-trained neurologist. It demonstrates the validity and accuracy of our method. Compared with the traditional scoring method, our multi-view vision based method is contactless, portable and cost-effective. It provides a new potential tool for clinical diagnosis.


High sugar intake in humans is associated with the development of overweight and other diet-related diseases. The World Health Organization and other health organizations recommend limiting the sugar intake to 10% of the total energy intake. There have been different approaches of front-of-pack labelling to reduce the amount of sugar in food products. Companies use nutrition claims to advertise the sugar content . Such nutrition claims can lead to false assumptions about the healthiness of foods and can lead to health-halo effects. Nutrition claims make products appear healthier than they really are, the aspect advertised in the nutrition claim is transferred to the entire food product. As a result, food products can be perceived as healthy even though they are not. Recently, the Nutri-Score was introduced in an increasing number of countries throughout Europe to provide consumers with an overview of the overall nutritional quality of a product. This study analyzes if the Nutri-Score can help to prevent health-halo effects caused by nutrition claims on sugar. Therefore, an online survey consisting of a split-sample design with more than 1,000 respondents was assessed. The results show that, depending on the initial perceived healthiness of a product, the Nutri-Score is able to prevent health-halo effects caused by claims on sugar. Making the Nutri-Score mandatory when using nutrition claims would be one possible way to reduce misperceptions about unhealthy food and reduce health-halo effects caused by claims on sugar. High sugar intake is associated with the development of overweight and other diet-related diseases, e.g., Type 2 diabetes and cardiovascular diseases in humans . The WorIn terms of nutrition policy, reducing the amount of added sugar in food is a key goal of many European countries . In GermAdditionally, the public is increasingly aware of the need to reduce the sugar intake, and a growing number of consumers are paying attention to foods that advertise a low sugar content –9.Research shows that the food environment is underestimated in public and political debates, while individual control of action is overestimated . AccordiIn most countries, consumers can infer the nutritional quality of foods when shopping at the supermarket by looking at various indications on the packaging. First, the nutrition fact table on the back of the product, second, front-of-pack nutrition labels , and third, nutrition and health claims on the packaging. The nutrition fact table is mandatory on food products, while front-of-pack labels are voluntary in many countries. Only a minority of consumers use the nutrition fact table to get information about the nutritional quality of food items . ReasonsNutrition and health claims highlight the beneficial nutritional character of a food item. In Europe, the European Commission Regulation 1924/2006 reports the allowed definitions for nutrition and health claims . A healtIn addition, the number of food products containing a reduced  or higher  level of specific ingredients is growing. The food industry is increasingly using nutrition claims related to the sugar content to market their product lines. These claims are known as sugar claims, and examples include ‘without added sugar’ and ‘sugar reduced’. Additionally, food companies are also using claims concerning the sweet taste of a product, e.g., less sweet, also known as ‘taste claims’ .Such taste claims carry the risk that consumers associate them with a reduced sugar content. However, taste claims are only related to a taste description and do not provide any information about the sugar content, and thus conclusions about the nutritional quality of a product are not possible. Research shows that nutrition claims are effective in targeting health-oriented consumers . This maAgainst this background, the first objective of this research was to find out if sugar and taste claims make foods appear healthier than they actually are. As the Nutri-Score (detailed explanation can be found in the method section) gives consumers an overview of the nutritional quality of food products  the secoThe Nutri-Score is an interpretive front-of-pack label that shows the nutrient quality on a graded scale of five colors, from dark green to red, in combination with a letter (A to E). The Nutri-Score algorithm combines positive characteristics with negative characteristics to achieve a score between −15  and +40 . The dark green A reflects the highest nutritional quality, while the red E stands for the lowest nutritional quality . The NutResearch has shown that the Nutri-Score is an intuitively understandable label compared to other labels , 33. MorParticipants provided their electronic consent in the beginning of the online survey. They were able to withdraw from participation by just closing the survey or browser. No data can be identified and/or linked to individual participants and all participants were informed that data would be anonymously analyzed. Data collection was in full accordance with the strict guidelines to the German DSGVO. The survey is in accordance with the Association of German Professional Psychologists (BDP) and with the Ethical Principles of the German Psychological Society (DGP).An online survey was conducted in Germany in October 2020. A professional online access panel provider (respondi) was responsible for data collection. Quota sampling was used with gender, age, education, income, and the region of residence as quota parameters to mimic the German population in these characteristics.https://doi.org/10.25625/FEKUCY). GRO.data is managed by the University of Goettingen and publicly accessible. The sociodemographic sample parameters are shown in To ensure data quality, two quality check questions were included. If participants incorrectly answered the questions, they were directly excluded from the survey (n = 276). Additionally, respondents who answered too fast (below ½ of the average response time (n = 71)), or with stereotypical response behavior ) were eliminated from the dataset. After this procedure, 1,103 valid respondents  remained in the data set for analysis. The raw data file is available via the GRO.data repositorium  had to be evaluated regarding their health value on a Likert scale from 1 = very healthy to 10 = very unhealthy. The instant cappuccino and the chocolate crunchy muesli were chosen as product examples of a medium to poor Nutri-Score rating, while the oat drink represented products consisting of a positive Nutri-Score rating. The ratings for the products were based on real examples from the supermarket. Afterwards, respondents were randomly allocated to one of five claim variations per product example. The random assignments to the claims led to a split sample design of 3 products x 5 claim variations .Claims about the sugar content are very popular and widely used on products in Western Europe and the US , 39. TheThe statistical analysis was undertaken by using the software SPSS 26. To find out if the different claim variations influence consumers’ perceived health evaluation of the products, analysis of variance, including Games-Howell or Turkey as post hoc tests, were used. Additionally, an analysis of variance was chosen to test if the Nutri-Score influences consumers’ perceived health evaluation when such claims are visualized on the products.When purchasing food, consumers paid the highest attention to the ingredients (4.22), followed by the sugar content (4.13) and fat content (3.77) of a product. The results can be seen in The meaning of the Nutri-Score was known by 32.8% of the respondents, while 37.5% could roughly imagine what the label means, and 29.6% had no idea what the label could mean. The health perception differed between both the product examples and between the product variants investigated. A rating of respondents was classified as healthy if the respondents rated it on the Likert scale with a value between 1–5, while a rating between 7–10 was classified as unhealthy. Of all the products, the chocolate crunchy muesli was considered the unhealthiest product. The instant cappuccino was rated somewhat more positively, and the oat drink had a clearly positive health image.The differences in health perception varied in the respective product variants and were not statistically significant in all cases. The health assessments for the various claim and label variants of a product also differed from one another. The neutral package of the instant cappuccino (meaning no claim or Nutri-Score on the packaging) was rated nutritionally rather unhealthy by almost 70% of the respondents. The claim ‘less sweet’ improved the health value by 11%, meaning that only 59% rated it as unhealthy. When only the Nutri-Score D was shown on the packaging, 82% of the respondents valued the instant cappuccino to be unhealthy. The effect of the Nutri-Score D, which was highlighted in light red, was stronger than the Nutri-Score C, which was yellow. In comparison, the oat drink without claim was rated as healthy by 84% of the respondents. It was the product example with the most positive health value without a claim or Nutri-Score influence. The health value of the oat drink did not increase when the claim ‘without added sugar’ was on the package. As this was a product which was perceived as healthy, the Nutri-Score only slightly increased the health evaluation. The chocolate crunchy muesli without claim was perceived as unhealthy by 78.9% of the respondents. The claim ‘30% less sugar’ increased the positive assessment as healthy by 16.9%. In combination with the Nutri-Score D, the positive assessment as healthy was diminished. When comparing the chocolate crunchy muesli and the instant cappuccino, the health assessment of the chocolate crunchy muesli was not reduced by the combination of ‘without claim and Nutri-Score D’. There was no effect of Nutri-Score C in combination with the respective claims (less sweet and 30% less sugar). The results are shown in The results of the ANOVA calculation illustrate product-specific differences in the effect on the various labelling variants (combinations of claims and Nutri-Score) on the health perception. In case of the chocolate crunchy muesli, the health assessment significantly increased when the sweet claim (30% less sugar) was present compared to the product without the sweet claim. Therefore, the health-halo effect can be confirmed for products with the worst health image at baseline. In the case of products on which the Nutri-Score was also displayed, it was shown that this had an influence on consumers. The Nutri-Score was able to correct the health image for products with a sweet or taste claim. This could be shown for some product variants for all three products regardless of whether they were initially assessed as healthy or unhealthy. For the rather unhealthy products (instant cappuccino and chocolate crunchy muesli), the taste claim (less sweet) and the sweet claim (30% less sugar) both increased the health assessment of the respective product, while the Nutri-Score C or Nutri-Score D were able to correct the health assessment. With the product perceived as healthy (oat drink), the nutrition claim (without added sugar) did not increase consumers’ health assessment of the product, while the Nutri-Score A increased the health assessment of the oat drink. Results can be found in Our results show that, when shopping for food, consumers pay most attention to the ingredients, followed by the sugar and the fat content of food items. This finding underlines the importance of the sugar content as a purchasing argument for foods. It has been shown that nutrition or taste claims related to sugar on front-of-pack labels can increase the health perception of the product. The results suggest that products with a poor health image at baseline, in particular, could be enhanced by nutrition claims. In this research, it can be assumed that the taste claim ‘less sweet’ may be considered as problematic, as consumers associate the claim ‘less sweet’ with a reduced sugar content.None of the claims considered in this research ensure that the products labeled with them have a favorable nutritional profile. Neither the legally regulated nutrition claims nor the unregulated taste claim say anything about the natural sugar content of a product. In this respect, the claims are a poor indicator of the nutritional quality of a product. Even products with a high sugar content can use claims for advertisement. This means that consumers consider the products to be healthier even though this is not the case. In case of the less health-promoting products examples (instant cappuccino and chocolate crunchy muesli) the sweet claim can reinforce the health-halo effect only for the chocolate crunchy muesli. Do nutritional or taste claims trigger a health-halo effect, the Nutri-Score is able to correct consumers’ health assessment of the food item. One possible explanation why only the chocolate crunchy muesli yielded significant results could be the high discrepancy between consumers’ personal health assessment of the product and the classification of the Nutri-Score. Moreover, consumers did not distinguish between the legally regulated nutrition claims  and the unregulated taste claims (less sweet). As a consequence, both types of claims led to distortions in consumers’ health assessments.Another challenge is that many food purchase decisions are made under time-pressure  and are From a nutrition policy perspective, nutrition claims, such as those on sugar reduction in foods, are intended to contribute to a health-promoting diet. At the same time, they are suitable for concealing the nutritional quality of foods. This occurs because the nutrition claims focus on one aspect of the food and thus mask other nutritional characteristics. In order to prevent misinformation about the nutritional quality of foods, the European legislator has introduced the Health Claims Regulation (EU-VONr.1924/2006) and regulated sub-areas of health marketing. Nevertheless, not all claims in the market are covered by the regulation and used by companies for marketing purposes only. The present study results concur with studies demonstrating health-halo effects for advertising with nutritional claims, which may result in unhealthy food choices and even in an increased calorie intake , 45. TheGiven the negative impact that nutrition and taste claims could have on consumers’ perceptions of the healthiness of products, their use should be restricted. Policymakers could prohibit the use as such. But such comprehensive advertising bans are hardly politically enforceable. A less intrusive approach to regulation would be to limit the use to food products with minimum requirements for nutritional quality. The results of this study confirm research that has identified a positive effect of interpretive labels for a reality-based assessment of nutritional quality by consumers. A meta-analysis showed that food labelling, especially interpretive labels , are an effective approach to empower consumers to choose healthier food products . AdditioOne limitation of this research is that the Nutri-Score was not established in Germany at the time of the online survey. However, the majority of participants had an idea about the meaning of the Nutri-Score. Therefore, it would be interesting to strengthen the results of this study once the Nutri-Score is well established and on many different products in retail stores. Moreover, only a limited number of product variants could be investigated in this study. To the best of our knowledge, this is the first study to analyze the contribution of the Nutri-Score on the perception of nutrition and taste claims about sugar. The results show the importance and need to discuss and regulate the role of advertising claims on consumers’ food choices.From a nutrition policy perspective, labeling is a popular tool to support consumer decision-making. However, in order to anchor a health-promoting diet more broadly in society, a mix of instruments with other measures (e.g. sugar taxes) is required , 53.The sugar content of foods is important to consumers when purchasing. Food products with a poor health image can be enhanced by nutrition or taste claims related to sugar on front-of-pack labels. The mandatory introduction of the Nutri-Score, at least as a prerequisite for health marketing, could help to reduce problematic health effects caused by misleading claims. Compared to other instruments such as advertising bans or taxes, such a form of regulation would presumably be easier to implement.Further investigation of the corrective effect of health-halo effects by the Nutri-Score should be explored for additional product categories, different Nutri-Score classifications, and additional advertising claims.