Exoskeleton technology has undergone significant developments for the adult population but is still lacking for the pediatric population. This paper presents the design of a hip–knee exoskeleton for children 6 to 11 years old with gait abnormalities. The actuators are housed in an adjustable exoskeleton frame where the thigh part can adjust in length and the hip cradle can adjust in the medial-lateral and posterior-anterior directions concurrently. Proper control of exoskeletons to follow nominal healthy gait patterns in a time-invariant manner is important for ease of use and user acceptance. In this paper, a hybrid zero dynamics (HZD) controller was designed for gait guidance by defining the zero dynamics manifold to resemble healthy gait patterns. HZD control utilizes a time-invariant feedback controller to create dynamically stable gaits in robotic systems with hybrid models containing both discrete and continuous dynamics. The effectiveness of the controller on the novel pediatric exoskeleton was demonstrated via simulation. The presented preliminary results suggest that HZD control provides a viable method to control the pediatric exoskeleton for gait guidance. Gait abnormalities in the pediatric population can arise as a secondary effect of various disorders such as spina bifida, muscular dystrophy, and cerebral palsy. Therapeutic intervention to address walking impairment is critical to mitigate the progression of gait disability, especially for the pediatric population . TraditiLower-limb exoskeletons are powered orthotic devices capable of providing torque to the patient joints. Such devices have been suggested for providing robot-assisted gait training to alleviate the aforementioned limitations of traditional approaches. In addition, other benefits include improved repeatability in applied torque or motion profile and direct measurement of gait which can be used for automatic quantitative assessment. Many devices are currently available for the adult population, as presented in other review articles ,6. RelatThere are two devices that alleviate these limitations. The ATLAS 2020 exoskeleton device is a highly actuated system with five actuated DoF per leg: hip flexion-extension, hip abduction-adduction, knee flexion-extension, ankle dorsiflexion-plantarflexion, and ankle eversion-inversion . The adjControl of exoskeletons for rehabilitation has primarily focused on enforcing a trajectory along some reference gait . The refThis paper presents significant contributions toward the development and control of a pediatric lower-limb exoskeleton for gait assistance and rehabilitation with the design of an adjustable pediatric exoskeleton frame that can be reconfigured to fit the anthropometrics of children between the ages 6–11 years old, and the application of a HZD-based controller to a pediatric human-exoskeleton system in simulation. The adjustable exoskeleton frame was designed to utilize the developed actuator discussed in the authors’ prior work . This paThe target indication for the pediatric exoskeleton is children 6 to 11 years old with gait disorders such as cerebral palsy ,27. The For the purpose of gait analysis and simulation, the body was composed of five rigid body segments: one head–arms–torso (HAT), two thighs, and two shanks with the feet included. Normalized body segment parameters were taken from the literature  which we2, which represents a 49% reduction in inertia with respect to the previous iteration of the actuators. Prior work with the previous iteration of the actuators in an anthropometrically parametrized exoskeleton demonstrated the backdrivability of the previous iteration of the actuators [The pediatric exoskeleton under development is powered at the hip and knee joints using identical modular actuators, shown in ctuators . Thus, tWith the actuator design established, what remained was the design of a pediatric exoskeleton frame that is lightweight and is capable of link adjustment. The adjustability must allow for the exoskeleton to fit children 6 to 11 years old with minimal disassembly and reassembly. The exoskeleton design is discussed in terms of its three primary subassemblies: the hip cradle, the thigh, and the shank. The exoskeleton is portrayed in The hip cradle consists of seven total parts: a backplate, two aluminum arms, two hip side plates, and two torso wings. The backplate is attached to two aluminum arms, upon which the two hip side arms slide to adjust hip cradle width and depth. Screws, threaded inserts, and geometry features on the aluminum bar allow the various components to lock into place. The torso wings are attached to the hip side arms by a rotating shaft, and help affix the hip cradle onto the subject’s torso using straps.The thigh subassembly houses the actuators developed in  and consThe shank subassembly of the exoskeleton does not need to adjust along the proximal-distal axis of the link. The shank subassembly only needs to translate forces provided by the knee actuator torques onto the shank of the subject. To facilitate this, a shank extension arm is affixed to the actuator arm. Along the shank extension arms are screw holes which allow modular cuffs to be added. These cuffs can project off of the shank arm and allows the components to fix and translate torque from the knee actuators onto the lower leg of the subject via straps.For the purpose of trajectory tracking via HZD control, nominal data for healthy children were chosen as a reference . These dS, cadence C, and speed v are related to their dimensionless counterparts, respectively denoted as g is the acceleration due to gravity and L is the hip height of the subject. Knowing the standing height of the target patients from The spatiotemporal gait parameters from the literature are provided as values normalized to be dimensionless quantities . The steP usingδC is the recorded standard deviation of the cadence and δP is the calculated standard deviation of the period, which are also reported in The cadence of children is typically greater than that of adults, upwards of 144 steps per minute [For control and simulation, it is necessary to have a model of the entire patient-exoskeleton system. A planar robot model was derived based on rigid body dynamics. The parameters of the links in the connected rigid bodies are found in The human body segment parameters were previously identified in For each link of the human-exoskeleton system, the mass of the link is represented by the scalar To apply HZD control, it is necessary to have a hybrid model of the patient-exoskeleton system. This hybrid model entails two parts: a continuous swing phase model representing the bulk motion of the system, and a discrete double support phase model representing an instantaneous impact event.To derive the continuous swing phase model, Lagrangian mechanics is utilized assuming one foot is pinned to the ground. The Lagrangian The potential energy can be calculated from the sum of the constituent parts i. The resultant equations of motion can be expressed in standard form for robotic systemsWith the Lagrangian found in terms of To derive the discrete stance phase model, the equations of motion for a floating body model are required. Following the same procedure as above, the floating body model isStance phase is assumed to be an instantaneous event occurring when the swing leg impacts the ground. Stance phase is modeled using discrete dynamics with a mapping from the pre-impact states  . The joirived in . In exteThe hybrid model consists of continuous dynamics representing the swing phase and discrete dynamics representing the stance phase. Combining these together, the hybrid model is: A zero dynamics-based controller drives the system towards a manifold and renders the manifold invariant. In this paper, the manifold was defined as the set of functionsc c was chosen such that its vector product with the extended joint configuration vector is equal to the horizontal coordinate of the hip in the anterior-posterior direction relative to the location of the stance foot. The location of the stance ankle was taken to be the origin and zero point, as shown in s sweeps from 0 to 1 throughout locomotion. Here, For this paper, the virtual constraints were defined using the joint angles corresponding with the actuators, and therefore, i in a range of collocation indices for the stance hip, stance knee, swing hip, and swing knee. This objective function allows for the zero dynamics manifold resulting from TROPIC to exhibit gait patterns similar to that of healthy individuals. The weight matrix W is a positive definite diagonal matrix with elements manually chosen for yielding adequate performance across the joints. Optimization constraints were included to limit stride length and walking speed within two standard deviations of the mean as reported in Typically, cost of transport or torque is minimized in the optimization. Although gaits resulting from the optimization using such an objective function are dynamically stable, they do not necessarily resemble nominal gait patterns observed in healthy ambulation. Such a gait profile may not be ideal for application in therapeutic intervention of individuals with gait disabilities. Instead, similar to , this paThe TROPIC toolbox generates an optimal phase-based reference trajectory, which is then applied to the simulated human-exoskeleton system through the application of a phase-based PD control law. In prior simulation work, a proportional-derivative (PD) control law sufficed for achieving adequate performance . AdditioIt is important to note that the simulated system does not consider the effect of non-instantaneous double-support phases. While the hybrid model in both HZD control and in this simulation assumes an instantaneous double-support phase, this is not reflective of real human gait which exhibits non-instantaneous double-support phases. In the authors’ previous work, HZD control methods were employed on an adult human-exoskeleton system by adjusting the controller implementation to allow for easy transitions across the double-support phases ,24. HoweWith the model equations and parameters identified for the patient-exoskeleton system, and with the information about nominal healthy gait of children as provided by Schwartz et al. , the TROThe simulation initially had transients which decayed over the first 3 steps, after which the system settled on a gait cycle with a step period of 0.538 s. This is slightly longer than the optimized zero dynamic manifold at 0.518 s. This difference arose as a result of errors in tracking performance, which can affect the periodicity of the system. Additionally, there was a discrepancy between the input-output feedback linearization controller used in the TROPIC optimization during the generation of the ideal gait profile vs. the phase-based PD controller utilized in this simulation. The difference in applied controllers helps explain the existence of errors in the simulation despite initializing the simulation along the zero dynamics manifold. The root-mean-square (RMS) errors, with respect to the phase-based calculated reference, after reaching steady state were 1.42, 1.07, 1.10, and 0.77 deg for the stance hip, stance knee, swing hip, and swing knee, respectively. With respect to the full scale of joint angle ranges for each joint, this represents error percentages of 3.26% 3.74%, 2.68%, and 1.20% for the stance knee, swing hip, and swing knee, respectively. Although these are small, they can contribute to the difference in step period. Ultimately, the PD controller was capable of adequately following the manifold to achieve a dynamically stable walking gait. A comparison of the steady-state joint angles from the simulation to the ideal TROPIC-optimized trajectory can be found in This paper presented the latest design iteration of an adjustable pediatric exoskeleton, and applied a HZD-based controller onto said system with an 8 year-old model patient in simulation. The pediatric exoskeleton was designed to actuate the hip and knee joints, and was able to accommodate the expected range of anthropometrics for children 6 to 11 years old. The adjustment mechanisms require minimal disassembly for refitting, and the exoskeleton should weigh roughly 3.5 kg excluding internal electronics, miscellaneous wires, and other minor components. The HZD optimization and resultant zero dynamics manifold were designed to provide gait guidance by emulating nominal healthy gait patterns of children. The phase-based approach for providing gait guidance lends itself for eventual application in exoskeleton-assisted gait rehabilitation. The results showed that the controller, with no input from the subject, was able to achieve dynamically stable walking gaits while following a human-like gait pattern. These preliminary results suggest that the HZD controller is a promising control candidate for gait guidance and eventual experimental application for rehabilitation in children with gait abnormalities.Moving forward, the design of the adjustable pediatric exoskeleton must be finalized, fabricated, and validated, initially in healthy children within the target age range. Once the pediatric exoskeleton design has been proven to be compatible with the target age range in simple walking experiments, the authors aim to conduct a multi-subject clinical study using a HZD-based controller for children with gait abnormalities. Additionally, the pediatric exoskeleton designed and described in this manuscript can serve as a testbed for other rehabilitative exoskeleton controllers for pediatric subjects, including controllers which consider factors beyond gait stability such as for operator safety and device torque magnitude reduction .


High-intensity repetitive task-specific practice might be the most effective strategy to promote motor recovery after stroke, and electromechanical-assisted gait training represents one of the treatment options. However, there is still difficulty in clarifying the difference between conventional gait training and electromechanically assisted gait training.The study is a multicenter, randomized, parallel-group clinical trial for stroke patients. Three clinical research centers in Korea  will participate in the clinical trial and 144 stroke patients will be registered. Enrolled patients are assigned to two groups, an experimental group and a control group, according to a randomization table. In addition, patients are treated for half an hour (one session) five times a week for 4 weeks. Both groups carry out basic rehabilitation  and the experimental group executes robotic walking rehabilitation treatment, and the control group executes conventional gait rehabilitation treatment. The primary endpoint variable is the Functional Ambulation Category (FAC) that determines the degree of independent walking and is measured before, after, and after 4 weeks of treatment. Secondary endpoint variables are 11 variables that take into account motor function and range, measured at the same time as the primary endpoint variable.There are still insufficient data on the effectiveness of electromechanical-assisted gait training for stroke patients and large-scale research is lacking. Thus, the research described here is a large-scale study of stroke patients that can supplement the limitations mentioned in other previous studies. In addition, the clinical studies described here include physical epidemiological analysis parameters that can determine walking ability. The results of this study can lead to prove the generalizable effectiveness and safety of electromechanical-assisted gait training with EXOWALK®.Clinical Research Information Service (CRIS), Republic of Korea KCT0003411, Registered on 30 October 2018The online version contains supplementary material available at 10.1186/s13063-022-06660-8. Gait training following a stroke has the potential benefits to increase walking speed as a result of training results in similar improvements in walking quality and economy . For gaiElectromechanical-assisted gait training that requires repetitive tasks can improve the neuro-plasticity with motor learning focus on the reorganization of brain tissue, resulting in better balance and faster gait speed . Seven pConventional gait training conducted by a physical therapist was a standard method of gait training for stroke patients. After the introduction of robot rehabilitation, many studies investigated the effectiveness of electromechanically assisted gait training by comparing it with that of conventional gait training . HoweverEXOWALK®  is a recently developed electromechanical exoskeleton-assisted gait training device. This design provides a stable and firm standing ability with little chance of falling and obviates the need for an additional cane or walker compared with currently popular exoskeletons. Such designs are user-friendly without the need for a harness for weight support. Clinical data on the validity and safety of this technology needs to explore this new market. Accordingly, in order to establish clinical efficacy and safety grounds for health insurance registration of new medical technology certification and rehabilitation robot therapy, clinical trials for proving the safety and effectiveness of walking rehabilitation of the new medical device EXOWALK® are conducted for stroke patients with walking disabilities.We have estimated the sample size by setting the change of gait function more conservatively and conduct a multi-center, large-scale randomized design to determine the effectiveness of electromechanical-assisted gait training with the EXOWALK®.This study on the efficacy and safety of electromechanical-assisted gait trainer EXOWALK ® (HR-01) is a multicenter, randomized, superiority, and parallel-group study with a 1:1 allocation ratio. All enrolled participants are patients with stroke. Three clinical research centers in Korea participate in this trial: Dongguk University Ilsan Hospital, Chungnam National University Hospital, and Seoul National University Bundang Hospital. The 144 participants who met the inclusion criteria were assigned 72 each to the test and control groups. If the study protocol is changed, the changes and reasons are delivered by e-mail to the research participating organizations. The changed research protocol is submitted to the IRB for deliberation.A strategy to achieve adequate participant registration is to promote the installation of posters and hand-outs in hospitals for patients and visitors to view. Each participant provides informed consent before enrollment. The research director explains the contents of the consent form to the patient for 30 min using terms that are easy for the general public to understand. We identify the schedule of the participants and schedule a visitable date to prevent participants from dropping out and complete the follow-up. Also, when the date of the visit approaches, we inform the participants in advance.The screening is conducted based on data from patients who agreed in the agreement, patients who met the inclusion and exclusion criteria, and hospitals. The target sample size is 144 participants. The eligibility assessment was conducted by a rehabilitation doctor according to inclusion and exclusion criteria. The patients were included within 1 week after eligibility assessment.The patients are screened for a stroke, 10 or more Mini-Mental State Examination (MMSE), Modified Ashworth Scale (MAS) grade 2 or lower, and standing with an assist. The following patients are excluded: (1) patients with poor cognition that are difficult to carry out instructions; (2) ataxia; (3) patients with MAS grade 3 or above; (4) patients with severe leg arthritis; (5) difficulty walking due to joint swelling of the lower leg; and (6) situations where other walking training cannot be performed.Compliance was assessed clinically on a per patient per visit.Records of the study medical device used, the number of treated, and intervals between visits are kept during the study. The number of treated is confirmed by counting at each visit and by cross-verification with the researcher. The staff member responsible for study supply handling is responsible for device accountability. Medical device accountability is recorded on a form not accessible to other staff members participating in the assessment of patients.In addition to the routine monitoring procedures, a Good Clinical Practice (GCP) Quality Assurance audit is initiated and the purpose of audits is to evaluate compliance with the principles of GCP, international and local regulatory requirements, and the study protocol.This study uses a newly created paper case report form (CRF) before the start of clinical trials according to the study protocol. The clinical research coordinator (CRC) of the clinical research center is responsible for CRF writing. After creating a CRF for all participants, data coding is performed.Two independent entry staff enter into different databases, respectively. Check whether the entered data match using the verified SAS program (V.9.3 or later version), and if it does not match, check the data against the CRF to modify the data. Before entering into the database, set a minimum input rule and train the entry staff. Input rules can help improve the reliability of the input data by minimizing input errors that may occur when typing, and the entry staff is entered in line with the data entry standard operating procedures (SOP). If electronic data transmission is required, the DataBase Programmer (DBP) delivers the annotated CRF and table schema to the researcher.For the protection of personal data and data security of the participants, physical security is maintained to prevent leakage of collected personal data. All participants’ paper CRFs and electronic documents are stored in a secure, access-controlled space or a locked file cabinet. Documents are stored and managed for 5 years after the completion of clinical trials.This study is a multicenter, parallel-group, and single-blind trial. For this clinical trial, participants who meet all of the participants’ inclusion/exclusion criteria and agree to participate in this study are assigned to two groups, an experimental group and a control group, according to a randomization table. Randomization is performed by an independent statistician using a random number generator computerized by the block randomization method in SAS version 9.4  or later. A separate randomization file is created for each research institute. Outcome evaluators were blinded for reducing the bias. In order to increase reliability by minimizing measurement error, the physical therapists with more than 5 years of experience conducted interventions and evaluations. At enrollment, we instructed patients not to reveal their allocation arm to the outcome evaluator. The blinding was achieved by allocating the tasks of study device handling and dispensation to an independent study team member who was unblinded and who was provided with the randomization schedule.Randomization plans may be viewed in case of emergency such as a serious adverse event (SAE) when the blind should be removed for any participant. In this case, we will describe the reasons why randomization should be released, the procedure, the documentation required, the series of treatments, and the evaluation of the participants.Treatment and assessment were done by different physiotherapists with 5 years or more of experience, to increase the reliability by minimizing the measurement error. The experimental group received electromechanically assisted gait training with EXOWALK and the control group received conventional gait training by therapists Fig. . ConventThe intervention presented in this study, electrochemical-assisted gait training, is a treatment that has already been verified and applied clinically and is currently applied to stroke patients in research institutes. There are no expected side effects from academic research to verify the effectiveness of electromechanical-assisted gait training from various angles. However, due to the nature of the robot, a large amount of treatment is performed compared to conventional training, which can lead to weak muscle spasms and fatigue. To prevent this, training is conducted with sufficient rest according to the instructions of a professional therapist.All patients in both groups undergo the existing rehabilitation treatment. The existing rehabilitation consists of neurodevelopmental treatment (NDT) and strength training. NDT is a rehabilitation treatment that induces the balance and control of sitting and standing postures by activating the reflex action of the paralyzed lower limb using the Bobath technique. Strength training is a mobility exercise to increase the range of motion of the paralyzed muscles and strength training to improve strength.As a treatment performed in the experimental group, the patients perform the electromechanical-assisted gait training in addition to the existing rehabilitation treatment. The medical device is EXOWALK® (HR-01), a rehabilitation robot for the lower limbs under clinical trials.As a treatment performed in the control group, the physiotherapist guides and walks the patient while assisting on the side or back of the participants.In this study, the demographic and clinical characteristics of the participants are measured and documented after screening. Demographic information record gender, date of birth, height, weight, and joint problems or not. And clinical characteristics record a total of five screening criteria. Screening criteria are the name of the diagnosis, cause of the disabilities , the paralysis side , the possibility of expression of intention (standard: MMSE 10 or higher), and the lower limb spasticity MAS score (standard: MAS grade 2 or lower).The Functional Ambulation Category (FAC) is the primary endpoint for determining the existence of independent walking through a concise level assessment. Primary endpoints are evaluated once in the baseline (pre-treatment), 4 weeks after the baseline (post-treatment), and 4 weeks after the last treatment (follow-up), a total of three assessments Table . FAC is Secondary endpoints are Rivermead Mobility Index (RMI), 10-m walk test , 6-min walk test , Motricity Index (MI), Berg Balance Scale, the Korean version of the Modified Barthel Index (K-MBI), step counting, Borg scale, swing time asymmetry, step length asymmetry, and propulsion asymmetry. These evaluations are conducted once on the baseline (pre-treatment), 4 weeks after the baseline (post-treatment), and 4 weeks after the last treatment (follow-up), a total of three assessments . Similarly, the third walking capacity is a 6-min walk test that measures the distance that can walk for 6 min, and the unit is meter (m). The fourth MI is an item to be evaluated in a 1 to 99 point system by measuring the lower leg force level from the ankle to the knee. These assessment items consist of three questions, each with a score of 0/9/14/19/25/33 and the total sum of the scores as a result of the evaluation. The fifth Berg Balance Scale is an item that evaluates balance ability by 0 to 56 points. There are 14 questions in total, and each question is scored from 0 to 4 points and the total sum of the scores is used as the result of the evaluation. The sixth K-MBI evaluates the independence of daily activities from 0 to 100 points. This assessment item consisted of 10 questions and, similarly, the total sum of the scores is used as a result of the evaluation. The seventh step counting measures the total number of steps performed by the patient during the specified walk-through time using the same mechanical counter by the three institutes. The measured number of steps is then used as a result of the assessment. The definition of a walk is the number of times a foot touches the ground again. The eighth Borg scale uses Borg’s rating of perceived exertion. Perceived exertion is defined as how much effort it takes to move or exercise. This item evaluates the cognitive level of the subjective effort intensity of the participants in the exercise from 0  point to 10  points. Lastly, the three evaluation items are measured by motion analysis devices. There are swing time asymmetry analyzing gait phase, step length asymmetry analyzing gait phase, and propulsion evaluation item analyzing propulsion.In addition, 4 weeks after baseline (post-treatment), patients in the experimental and control groups are questioned on the usability assessment of the electromechanical-assisted gait training. Also, the therapist who gave the treatment is questioned. The goal of this assessment is to verify and supplement the assessment items by verifying the usability level of various types of walk-through electromechanical-assisted gait trainers and verifying the reliability and validity of the usability assessment.Finally, during the follow-up evaluation, the patients receive a follow-up assessment questionnaire to determine if the patients are treated for a period of 4 weeks of follow-up observation.In the evaluation of research, there are two types of motion analysis devices to be used.HumanTrack , a gait analysis system, is a machine capable of performing walking analysis only at a distance of 5–7 m without space restriction, and even a seriously ill patient using an assist instrument can be measured.Freemed  is a foot pressure measurement system that allows patients and ordinary people to evaluate static and dynamic foot pressure, walking, and balancing abilities. In the static inspection, load ratios in the medical/lateral and anterior/posterior directions can be analyzed by the distribution of the pressure in the sole, and in the dynamic foot low-pressure inspection, not only temporo-spatial parameters such as the ratio of the foot angle standing device (instance phase) and the swing phase, but also walking pressure, average visibility, and walking force are provided.The physical content of the clinical alteration is reported by the auditors, practitioners, and patients at each visit. All indication, data of onset, and period are recorded.The primary endpoint is the FAC difference before and after training. Data were available on this parameter from a previous trial . Hiroko Watanabe et al. show a study on the improvement of stroke walking ability using the hybrid assisted limb (HAL), a representative device of the walking rehabilitation robot. According to the reference, the mean change in existing medical devices was 0.54 and 1 in the test group and HAL group. The newly developed medical device for use in this study was expected to produce about 25% better results in performance than in the HAL group, assuming a variation of 1.25. Therefore, it was assumed that the difference between the variation in the FAC of the test medical device and the comparator device was 0.71 and that the standard deviation was conservatively approached to assume the largest value of 1.4. Two-sided tests were used for sample size estimation. The number of participants per group to achieve overall power of 80% under the significant level of 0.05 is 65. To allow for a possible 10% dropout rate, 144 people were registered per group with 72 people.n (%). The pre-homogeneity will be analyzed by the chi-squared test or Fisher’s exact test. Continuous variables such as age, height, weight, the possibility of expression of intention, and the lower limb spasticity MAS score will be expressed as mean ± standard error (SE) and range. The pre-homogeneity will be analyzed using Student’s t-test or Wilcoxon’s rank sum test depending on whether the normality assumption is satisfied.Categorical variables such as gender, joint problem disorder, the cause of the disability, and paralysis side in demographic and clinical characteristic information will be presented as t-test or Wilcoxon rank sum test depending on the satisfaction of the normality assumption. The comparison analysis of differences before and after treatment in the group will be conducted using either the paired t-test or Wilcoxon’s signed tank test depending on the satisfaction of the normality assumption. In analyzing continuous variables, analysis of covariance (ANCOVA) will be performed after setting the variables as covariates when there are variables that seem to require control in the pre-homogeneity results for demographic and clinical characteristics.For all continuous variables of primary and secondary outcomes, the post-treatment measurement results will present the technological statistics, the observed number of patients, mean, SE, and range and compare the differences between groups using Student’s Additional details regarding subgroup and adjusted analyses and definition of analysis populations are available in the clinicaltrials.gov (KCT0003411) in the study summary.All data will be analyzed using SAS version 9.4 or later. All statistical tests will be two-sided, and the level of significance was set at 0.05.In the event of damage or damage directly related to this clinical trial, the clinical trial manager will be responsible for providing compensation for the damage following the victim compensation regulations. In the case of side effects, the victim will be treated with a known treatment method. If an adverse event occurs due to a clinical trial, it will be treated properly until the subject recovers and compensated for the damage caused by clinical trial medical devices in accordance with the regulation for clinical trial victim compensation.Automated electromechanical gait training devices have been developed and their effectiveness has been proven , 13. HowThere are still sufficient papers on the effectiveness of electromechanical-assisted gait training for domestic patients and large-scale research is lacking. Thus, the research described here is a large-scale study of domestic patients that can supplement the limitations mentioned in other previous studies. In addition, the clinical studies described here include physical epidemiological analysis parameters that can determine walking ability. Therefore, it can prove that robotic therapy is effective for walking. The clinical is a multicenter study for domestic patients conducted by all three institutions in a thorough random sampling system, so it will be possible to prove its generalizable effectiveness and safety.The clinical trial protocol is registered in the Clinical Research Information Service (CRIS) of the Republic of Korea. CRIS has joined the WHO International Clinical Trials Registry Platform (ICTRP) as the world’s 11th representative primary registry in Korea. Research information registered in CRIS is disclosed on the web in real time from the time of approval of the manager and is regularly transmitted to WHO ICTRP at regular intervals to foreign researchers and the public. The recruitment of patients was completed in June. The last patient registration date is 29 June 2020. A total of 144 people have been registered. Trial completion will be expected to be completed by the end of 2020. This protocol version is 1.4 (4 April 2019).Additional file 1. Study registraion information and protocol in CRIS.


The Nodulus and Uvula (NU) (lobules X and IX of the cerebellar vermis) form a prominent center of vestibular information processing. Over decades, fundamental and clinical research on the NU has uncovered many aspects of its function. Those include the resolution of a sensory ambiguity inherent to inertial sensors in the inner ear, the otolith organs; the use of gravity signals to sense head rotations; and the differential processing of self-generated and externally imposed head motion. Here, I review these works in the context of a theoretical framework of information processing called the internal model hypothesis. I propose that the NU implements a forward internal model to predict the activation of the otoliths, and outputs sensory predictions errors to correct internal estimates of self-motion or to drive learning. I show that a Kalman filter based on this framework accounts for various functions of the NU, neurophysiological findings, as well as the clinical consequences of NU lesions. This highlights the role of the NU in processing information from the otoliths and supports its denomination as the “otolith” vermis. Lobules IX and X of the cerebellar vermis, also known as the Nodulus and Uvula (NU) , are a pThis multiplicity of viewpoints complicates the effort to understand the role of the NU in vestibular information processing and raises the question of whether the NU performs a unitary function at all. Here, I show that physiological and clinical findings can be explained by a single theoretical concept: that the NU implements an internal model of head motion to predict the activation of the otoliths, and outputs sensory prediction errors that are broadcasted to other brain regions to correct internal estimates of self-motion, or to drive learning. Based on the afferent and efferent connections of the NU, and the physiology of neighboring regions, I discuss the position of this internal model in the anatomical vestibular and cerebellar networks.This framework indicates that the NU plays a pivotal role in processing information from the otoliths and sends otoliths-based feedback to other brain regions, hence supporting the notion of the NU as the “otolith” vermis.Tilt/translation discrimination is one of the fundamental steps of central vestibular information processing. It resolves a sensory ambiguity by disambiguating the sensory signals from the otoliths that cannot discriminate tilt from translation. This is easily illustrated by considering the following analogy: the otolith organs are similar to a pendulum fixed to the head that swings relative to the head during tilt or translation motion . Thus, bAfter a brief summary of the theoretical concepts involved in tilt/translation discrimination, I will review the involvement of the NU and associated vestibular networks:From a physical point of view, the otolith organs sense the gravito-inertial acceleration (GIA), which can be expressed as the sum of gravitational (G) and inertial (A) accelerations . These tHow (or whether) the brain deals with this ambiguity has been the subject of considerable attention and debate, both at the experimental and theoretical levels . For sevThe second hypothesis stipulates that the brain uses semicircular canals information to separate tilt from translation. Indeed, tilt movements are rotations and are sensed by the canals. By integrating rotation velocity signals in three dimensions  based on the rotation signal Ω: this is accomplished by a vectorial cross-product G × Ω. This is illustrated by two examples in The crucial step in the tilt/translation discrimination model is the 3D integration. This step is developed in detail in In agreement with both models, experiments in humans and non-human primates have revealed that low-frequency translation is indeed interpreted as head tilt: this effect is called oculogravic or somatogravic illusion . Both thThe crucial experiment to distinguish these frameworks is to test whether the brain can discriminate high-frequency tilt from translation, as predicted by the discrimination model. This model also predicts that artificially activating the canals can induce illusory translation. From the last 90s onward, these predictions were both confirmed by a series behavioral studies in macaques  and humaStarting in the early 2000s, a series of studies have uncovered neurons that encode specifically translation or tilt , thereby providing a direct and compelling confirmation of the discrimination model. These neurons exist in the NU, and in regions closely associated with it, including the fastigial and VN. In the NU, these neurons amount to about two-third of the Purkinje cells and are the only Purkinje cells for which a clearly defined function has been proposed. This suggests that the NU is indeed mainly involved in computation related to tilt/translation discrimination. In this section, I will summarize these experiments and the properties of translation- and tilt-selective cells in the NU.Most experiments on tilt/translation discrimination use an experimental paradigm where the head is translated in the horizontal plane , head motion is sensed by the semicircular canals. These canals act as a high-pass filter, with a time constant of ∼4 s. This implies that, during a constant-velocity rotation , gray, tt = 60 s). This after-effect is the basis of some experimental protocols discussed in the next section.Although the VS increases its time constant, rotation sensation keep high-pass characteristics. A consequence of this is that, when stopping after a long period of constant velocity rotation, one experiences an after-effect that is symmetric to the response to the initial rotation   . In an e = 60 s) : this ph = 60 s) , 1995b,  = 60 s) , and hum = 60 s) : note th = 60 s) : this rut < 60 s) and then tilting the head while maintaining the rotation (t = 60 s) and stabilizes to a steady-state level called “bias velocity.” This rotation sensation is mediated by the VS: this can be shown by re-aligning the head with vertical (t = 120 s). After this, rotation sensation persists and decreases with the typical time constant of the VS (t = 120 s) is stored in the VS. The bias velocity can be observed in macaques  the angular velocity of the head in egocentric coordinates , the allocentric tilt of the head , and the linear acceleration . Note that head rotation and tilt are linked by a causal relationship: changes in head tilt occur through rotation movements, and therefore head tilt is the 3D integral of Ω. These motion variables and the causal relationship between them constitute the internal representation of head motion.This internal representation can be updated by motor efference copies. Note that this model does not address motor control explicitly. For simplicity, it is assumed that motor centers provide signals that encode self-generated rotations , blue anNext, the model computes sensory prediction and sensory prediction error. From a physical point of view, otoliths sense the sum of tilt and translation . The modThe concept of otolith prediction error is central to understanding the NU. To fully apprehend it, we may examine when these errors occur or not:Otolith prediction errors occur during passive or unexpected translations. This is because translations activate the otoliths, but the model does not have any information to anticipate this activation.Otolith prediction errors occur during canal/otolith conflicts similar to the post-rotatory tilt . This isIn contrast, otolith prediction errors do not occur during active tilt or translations, because motor efference copies allow anticipating the activation of the otoliths.Based on , otolithFinally, otolith prediction errors can occur during OVAR, but only when the rotation signal Ω does not match the actual rotation of the head. This occurs, for instance, during the first second following head tilt in Next, sensory prediction errors drive feedback loops that update the internal model of head motion. The optimal organization of these feedback loops can be predicted using the Kalman filter algorithm . As a ruThe feedback to the internal model of translation has gain of 1, and therefore that otolith prediction error is interpreted by the brain as an unexpected translation. This allows the internal model to detect passive translations, during which otolith prediction errors occur. This feedback also implies that canal/otolith conflicts should induce a sense of translation: this important prediction was verified by behavioral experiments in The feedback to the internal model of tilt is fed to the 3D integrator. As a consequence, this feedback matches exactly the somatogravic feedback in Finally, the Kalman filter predicts that the feedback to the rotation estimate is fed to the VS and that it acts to adjust the internal model of rotation to match head motion relative to gravity see . As a coNote that the models in This shows that a wide range of behavioral observations can be explained by a simple mechanism where otolith prediction errors drive corrective feedback loops to the internal model of motion. Next, we will discuss how these loops correspond to neuronal response in the central vestibular network. Before this, we can emphasize an important point: the notions of “translation signal” and “otolith prediction error” are closely associated and practically indistinguishable during passive motion, since otolith prediction errors are always interpreted as passive translations.I will now examine how the internal model frameworks match neuronal responses in the NU and associated regions.First and foremost, the response of translation-selective cells in the NU and downstream (VN and FN) corresponds precisely to otolith prediction errors. Indeed, by definition, translation-selective neurons in the NU, VN, and FN respond during passive translations but not passive tilt. Furthermore, translation-selective neurons in the NU respond to canal/otolith conflict , or duriWe have determined in Thus, the internal model hypothesis accounts for all known responses in the NU, as well as in the associated regions . At the Interestingly, this framework suggests that the velocity feedback to the VS may be driven by translation-selective neurons themselves. This illustrates that the functions of these neurons may be much wider than simply conveying translation signals. In fact, they may be seen as an output channel of the NU that broadcast feedback signals to regulate multiple variables of the internal model.In the Kalman filter framework, the function of feedback loops is to correct the internal model of motion online. However, our recent finding that IO neurons project to the NU are translation-selective  indicateThrough this review, I have summarized a variety of findings regarding the physiology and function of the NU. I have proposed that these findings are explained by the theory that the NU implements a forward internal model of head motion to predict how the otolith organs are activated by movements on the head and broadcasts feedback signals to other brain regions when prediction errors occur. Note that previous theoretical works based on internal model  proposedThe theoretical concept of internal model was initially proposed in the 70s  and evolIt is also worth noting that theoretical models of vestibular information processing were developed and refined based on decades of behavioral data . The facEarlier modeling works  emphasizThe Kalman filter framework also encourages us to understand the vestibular system in the wider context of motor loops . By emphRecordings in the PC layer of the NU have allowed discovering two components of the internal model highlighted in The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.


The steam turbine is one of the major pieces of equipment in thermal power plants. It is crucial to predict its output accurately. However, because of its complex coupling relationships with other equipment, it is still a challenging task. Previous methods mainly focus on the operation of the steam turbine individually while ignoring the coupling relationship with the condenser, which we believe is crucial for the prediction. Therefore, in this paper, to explore the coupling relationship between steam turbine and condenser, we propose a novel approach for steam turbine power prediction based on the encode-decoder framework guided by the condenser vacuum degree (CVD-EDF). In specific, the historical information within condenser operation conditions data is encoded using a long-short term memory network. Moreover, a connection module consisting of an attention mechanism and a convolutional neural network is incorporated to capture the local and global information in the encoder. The steam turbine power is predicted based on all the information. In this way, the coupling relationship between the condenser and the steam turbine is fully explored. Abundant experiments are conducted on real data from the power plant. The experimental results show that our proposed CVD-EDF achieves great improvements over several competitive methods. our method improves by 32.2% and 37.0% in terms of RMSE and MAE by comparing the LSTM at one-minute intervals. A stable electricity supply is an important guarantee for effective production. Thermal power generation is currently one of the most important power generation ways in the world. A complete thermal power generation system contains different equipment with different functions .  has poinThere exist many approaches for output power forecasting of turbines. Some approaches adopt machine learning methods, such as regression-based methods, to predict the output power of turbines. Furthermore, many neural network-based methods have been used to exploit the features of turbine operating data and predict the output power of turbines more accurately.However, a notable drawback of the methods mentioned above is that they only focus on the output power prediction based on the turbine information individually while ignoring the correlation with the rest equipment of the power generation system. For example, they ignore the coupling relationship between the condenser (the front-end equipment of the steam turbine) and the steam turbine, which is contradictory to the practical scenario because some of the input factors are unavailable in reality . In this paper, we explore the prediction of the steam turbine output power by introducing the coupling relationship with the condenser. A natural intermediary for coupling the condenser and the steam turbine is the condenser vacuum degree , which is a key factor for predicting the output power of the steam turbine. However, as an intermediate factor between the condenser and the steam turbine, the condenser vacuum degree varies dynamically based on the time and the condition status of the equipment –4, whichCondenser Vacuum Degree guided approach based on Encoder-Decoder Framework for steam turbine output power prediction (CVD-EDF). We model the condenser and steam turbine at the encoder and decoder, respectively. The encoder predicts the condenser vacuum degree dynamically and the decoder predicts the steam turbine output power at the target moment. In specific, we adopt the multi-layer LSTM as the basic architecture of the encoder and the decoder. Moreover, a connection module consisting of an attention mechanism and a convolutional neural network (CNN) is proposed to capture the local and global information within the encoder. All the information is further introduced into the decoder to predict the steam turbine output power at the target moment.To overcome the above issues, in this paper, we propose an In summary, the main contributions of this paper are listed as follows:A novel condenser vacuum degree guided approach based on the Encoder-Decoder framework for steam turbine power prediction is proposed. To the best of our knowledge, our work is the first attempt to take into account the coupling relationship between the steam turbine and the condenser when predicting the steam turbine output power.Experimental results on the real data from the power plant show that our proposed CVD-EDF outperforms several competitive baselines. It achieves an improvement of 32.2% RMSE and an improvement of 37.0% MAE by comparing the LSTM at one-minute intervals.The Encoder-Decoder framework is popular in artificial intelligence which consists of an encoder and a decoder. The encoder is a neural structure that extracts features from raw inputs and passes them to the decoder. The decoder is another neural structure that incorporates the features from the encoder and makes decisions for the task. In the beginning, the framework is widely used in the field of signal processing because of its ability to compress dimensions.  adopts tIn general, the methods of predicting the output power of turbines can be divided into two categories according to their basic techniques and methodologies, i.e., machine learning approaches and deep learning approaches. For machine learning approaches,  adopts t takes inLong short-term memory network (LSTM)  is a RecLSTM is designed to handle long-time dependencies and the architecture of LSTM is shown in t, the input of LSTM is xt and the hidden states and the cell states at time step t − 1 are ht−1, ct−1 respectively. The forget gate ft decides what information should be discarded and it is calculated as follows:Wf is a trainable parameter and bf is a bias vector. The input gate it determines what information should be stored in the cell states, and the new candidate value Wi, Wc are trainable parameters and bi, bc are bias vectors. The cell states ct at time step t is calculated as follows:ot determines what is going to be output, and the output ht can be obtained:Wo is a trainable parameter and bo is a bias vector. Following previous research, we adopt LSTM as the basic component for constructing our model in this paper.At time step In this paper, we explore the coupling relationship between the steam turbine and the condenser and propose a novel approach for steam turbine power prediction based on the encode-decoder framework guided by the condenser vacuum degree (CVD-EDF). We introduce the proposed CVD-EDF in detail in this section. The model architecture is shown in Encoder: A LSTM is adopted to capture the historical information of the condenser operating conditions data, and the condenser vacuum degree of the target moment is predicted through a multi-layer perceptron network (MLP).Connection module: An attention mechanism and a convolutional neural network (CNN)  are propDecoder: The local, global features and the steam turbine operating condition data are concatenated as the input of the decoder. In this way, the information of the condenser vacuum is introduced into the decoder. Then, the history information of the steam turbine operating conditions data is captured by another LSTM. The initial hidden states and cell states of the decoder LSTM are initialled with the last hidden states and cell states of the encoder LSTM. The output power of the steam turbine at the target moment is predicted by fusing various information.X =  is the historical data sequence of the condenser operating conditions, where t is the length of the sequence. The encoder extracts features from X and predicts the vacuum degree at the target time step.In the encoder part, the condenser is modelled. The input of the encoder vt at timestep t can be calculated via:W1 and W2 are trainable parameters, and ReLU denotes the ReLU  of the steam turbine operating conditions is concatenated with the local features L =  and the global features G and forming the input I =  of the decoder:In the decoder part, the steam turbine is modelled. To take into account the coupling relationship between the condenser and the steam turbine, the local and global features extracted by the connection module are used as additional inputs to the decoder. The decoder fuses the multiple features and predicts the output power at the target time step. The historical data sequence Another multi-layer LSTM is adopted as the decoder. The initial hidden states pt at timestep t can be calculated via:W3 and W4 are trainable parameters, and The steam turbine power vt, the predicted steam turbine output power pt with the real value:Pt and Vt represent the real condenser vacuum degree and the real steam turbine output power. N is the number of training samples. The training objective is to minimize the total loss We adopt the mean square error loss (MSE) to represent the error of the predicted condenser vacuum degree In this section, we evaluate the effectiveness of CVD-EDF by comparing it to other approaches and ablating several design choices in CVD-EDF to understand their contributions.The data used for experiments are collected from a thermal power plant via sensors in Jiangsu Province of China, which range from July 1 to November 22, 2021, at a time interval of one minute.For condenser operating conditions, the following real-time data needs to be collected: feedwater flow, circulating water inlet pressure, reheat steam temperature, reheat steam pressure, main steam temperature, main steam pressure, main steam flow rate, heat supply temperature, heat supply pressure, and heat supply flow rate. For steam turbine operating conditions, the following real-time data needs to be collected: supply line flow, supply line pressure, supply line temperature, reheat steam temperature, reheat steam pressure, main steam temperature and main steam pressure. Moreover, real-time data on the vacuum degree of the condenser and the output power of the steam turbine also needs to be collected.As shown in The length of historical data sequence t is set to 10. For CVD-EDF, the number of layers of the LSTM encoder and LSTM decoder is 2, and the hidden state dimension is set to 64. The whole model is trained by the Adam optimizer  with a lRoot mean squared error and mean absolute error are adopted to evaluate the overall performance.yt and Root Mean Squared Error (RMSE): It is a standard way to measure a model in predicting quantitative data. Mean Absolute Error (MAE): It measures the average magnitude of the errors in a set of predictions:Some machine learning methods are chosen as the baselines, including several regression algorithms: Linear Regression, Ridge regression, LASSO regression, Elastic Net regression, Decision Tree regression and Xgboost model. And we also use LSTM as a baseline.Our proposed CVD-EDF significantly outperform other baseline models at all time intervals for both RMSE and MAE in steam turbine output power prediction. Compared with LSTM, CVD-EDF achieves an improvement of 32.2% RMSE and an improvement of 37.0% MAE at one-minute intervals. It proves the effectiveness of our proposed CVD-EDF and the reason for the improvement is that CVD-EDF takes into the vacuum degree information when predicting the output power of the steam turbine. It also demonstrates that taking into account the coupling relationship with the condenser is helpful for the output power prediction of the steam turbine.When the time interval is longer, the advantages of our proposed CVD-EDF are more obvious. it achieves an improvement of 63.7% RMSE and an improvement of 68.8% MAE at one-day intervals compared to LSTM.Regarding baseline models, LSTM achieves the best performance both on RMSE and MAE at one-minute and one-hour intervals while Decision tree regression performs best at one-hour intervals. Among the regression algorithms, ridge regression and linear regression have similar performance and they perform better than other algorithms, but they still fall short of our proposed CVD-EDF.our proposed CVD-EDF model is more consistent with the real steam turbine output power compared to decision tree regression, ridge regression and LSTM.All the approaches have the ability to track the trend of the actual output power. However, the LSTM predictions are much lower than the actual steam turbine power almost all the time and the decision tree regression is not able to predict accurately during the time periods when the output power varies drastically. The ridge regression performs better than decision tree regression and LSTM, but the predictions are still inaccurate and more volatile. Our proposed CVD-EDF predictions are more accurate and informative.To further evaluate the effectiveness of each component, we conduct some ablation experiments on our model at one-minute intervals. We performed three ablation experiments:The input of the decoder does not contain the global features captured by CNN .The input of the decoder does not contain the local features captured by the attention mechanism .The input of the decoder is the historical data sequence of the steam turbine operating conditions without the local features and global features. The initial hidden states and cell states of the decoder LSTM are initialized to zero. Therefore, no condenser vacuum information is introduced from the encoder to the decoder and the model degenerates to a simple LSTM model. .The results are shown in Compared with CVD-EDF w/o Attention and CNN, CVD-EDF w/o Attention achieves an improvement of 24.6% RMSE and an improvement of 34.1% MAE while CVD-EDF w/o CNN achieves an improvement of 29.7% RMSE and an improvement of 34.4% MAE. It demonstrates that both the local feature and the global feature are beneficial for the steam turbine output power prediction.Compared with CVD-EDF, the RMSE and MAE for CVD-EDF w/o CNN, CVD-EDF w/o Attention drop a lot. It means that the CNN and the attention mechanism are able to capture different aspects of vacuum degree information, and the model performs best when the two are combined.Compared with CVD-EDF w/o Attention, CVD-EDF w/o CNN achieves an improvement of 6.9% RMSE and an improvement of 0.5% MAE. It means that the local features are relatively more important than the global features. The reason is that, at each step of the decoding process, the attention mechanism is able to dynamically determine different parts of the hidden states of the encoder. In contrast to global features, it is able to filter some irrelevant information and keep the most important information as input to the decoder.To further illustrate the performance of our approach, CVD-EDF effectively tracks the true steam turbine power trend and the error of CVD-EDF is smaller than other baselines both at one-minute intervals and one-hour intervals. The reason is that our approach leverages the encoder-decoder framework to introduce vacuum degree information in the steam turbine output power prediction.LSTM, decision tree regression and ridge regression can track the trend of the actual steam turbine power. However, LSTM outputs much lower predictions than the real steam turbine output power almost all the time, while the ridge regression outputs much higher predictions than the real steam turbine output power most of the time. The decision tree regression predictions have large errors at some time steps. Therefore, the predictions of LSTM, decision tree regression and ridge regression are unreliable in a practical scenario.In this paper, we propose a novel approach for steam turbine power prediction based on the encode-decoder framework guided by the condenser vacuum degree (CVD-EDF), which for the first time explores the information on the coupling relationship between the steam turbine and the condenser. The condenser and steam turbine are modelled separately in the encoder part and the decoder part. In addition, a connection module which is composed of the attention mechanism and the CNN is proposed to capture the local and global information from the encoder. All of the information is introduced into the decoding process for accurate power prediction of the steam turbine. Experimental results on the real data collected from the power plant in Jiangsu Province of China show that the proposed method outperforms other competitive baselines. In the future, we will consider taking connected components of the thermal power generation system into account in the steam turbine power forecast, such as circulating water pumps.


The expansion of cracks in 3D printing concrete materials may lead to structural failure, so it is essential to monitor crack propagation development. Coda wave interferometry (CWI) has been proven to be sensitive to microcracks, however, the evolution pattern of ultrasonic coda waves during crack growth is still not clear. This paper reports a numerical study of the sensitivity and feasibility of CWI for monitoring microcrack growth in heterogeneous materials. A two-phase concrete model, which contains microcracks with different angles and lengths, was developed using the finite element analysis software ABAQUS. The relative velocity change  Three-dimensional printing is a rapid prototyping technology that uses three-dimensional digital models and mechanical equipment to form materials in piles ,2,3. ThrCoda wave interferometry (CWI) is a new method for micro damage identification in civil engineering. Ultrasound propagation in a multiple scattering medium is subject to strong scattering, resulting in the rapid attenuation of ultrasound energy in the medium . ConcretCoda waves analysis techniques have been actively studied and reported in recent decades due to the high sensitivity of coda waves in detecting early and small damages. R. Snieder  was the The coda waves observed in the laboratory tests are often affected by multiple factors, thus the parametric study of factors that may affect the change of coda waves during the crack propagation process is of significant meaning to understand. For this issue, using the numerical simulation method is a good research idea. Numerical simulations have been extensively studied in the simulation of ultrasonics ,17,18. NBefore studying the propagation process of ultrasonic waves in concrete, it is necessary to understand the properties of the concrete medium. Concrete is a heterogeneous multiphase composite condensate composed of hardened cement paste, sand, gravel, and other aggregates. In simple terms, concrete can be considered as a two-phase composite composed of cement mortar matrix and aggregate fillers. Ultrasound propagation in concrete is bound to be affected by heterogeneous scatterers such as aggregates. Therefore, it is crucial to establish a concrete model to simulate the wave propagation process more practically. This paper aims to deepen the understanding of the scattering wave propagation rule during damage development in heterogeneous media using numerical simulations.In this paper, a two-dimensional heterogeneous concrete model with damage, which is set to be cracks with different angles and lengths, was developed, and the evolution of the coda wave during crack propagation was observed. The relationship between the relative velocity change and decorrelation coefficient during the expansion of microcracks and the crack expansion was quantified. In addition, the effect of crack angles was analyzed. The arrangement of this paper is as follows: In CWI is a technique that detects small changes in a medium by using multiple scattering waves, allowing the determination of the relative velocity change of a diffuse wavefield measured from a fixed source and a fixed receiver at two different moments . UltrasoAccording to the path summation theory of CWI, the waveform in the reference wavefield le paths , the refCWI can be used to assess the correlation and relative velocity change of two signals. The stretching method is a powerful tool for estimating the difference between signals, which calculates the correlation ε that makes the maximum value of d single  or multid single .X defines the set of n randomly distributed perturbation locations within the inner kernel volume The total scattering coefficient g0  can be uo et al.  used difmaterial ,34 leadij are finite element position indices, The simulation of scattering waves is a stress wave propagation problem that should be dealt with using a dynamic solver. In this paper, the scattering waves are simulated using the ABAQUS/Explicit solver . Stress In this paper, concrete was simulated by considering it as a two-phase heterogeneous medium composed of coarse aggregate and cement mortar. The heterogeneity in the concrete causes a strong scattering of ultrasonic waves in the wavelength range of the aggregates. To make the scattering waves in concrete more realistic, concrete aggregates were materialized by multiple randomly distributed circular areas with different material properties, and local damage was achieved by inclusions with high aspect ratios. The technical route adopted in this paper is shown in the flowchart in  to void . The modf is the center frequency, n indicates the signal duration period, the larger the value of n, the narrower the frequency band, which is conducive to reducing the dispersion effect. The signal emission source was placed at  to simulate the ultrasonic waves emitted by the piezoelectric transducer. The acceleration within 0–4 ms at  was extracted as the receiving scattered signal. The ultrasound source and mesh division are shown in In the numerical simulation of elastic waves, especially in the field of seismic exploration, the source signal is mainly simulated using a Reckonic or Gaussian function and its first-order derivative; while in the numerical simulation of high-frequency ultrasonic waves, the excitation source signal often uses a convex cosine function to resemble more closely the impulse signal from the ultrasonic transducer, as in Equation (7) :(7)ft;n=5 steps [Finite element simulation of the stress wave propagation process was performed using an explicit solver. The explicit method achieves satisfactory accuracy provided that the time increments are small enough, and it is very effective in solving for a large number of time increments, which are typically upwards of 105 steps . The exp−8 s. The number of finite elements per wavelength is an essential factor while modeling wave propagation with the finite element method. According to Drozdz’s study , each waIn total, 77 configurations with different crack angles and lengths were numerically investigated. The variation of lengths indicates the process of crack expansion. The exemplary waveforms of The stretching method was applied to all 77 signals, and the selection of the time window in the calculation process of this method is crucial. The longer the time window, the higher the cost of CWI calculation, the relative velocity change tends to stabilize when the window centroid is sufficiently late, improving the robustness of CWI ,47,48. AThe propagation path of the incident wave into the interior of the medium is affected by the scatterer, resulting in a change in propagation direction and redistribution of energy. The change in the scatterer cross-section causes a complex path transition inside, resulting in a change in the According to Equations (11) and (12), the values of n et al.  in theirIn this paper, a two-dimensional heterogeneous medium has been modeled by the finite element method, and the quantitative assessment of material damage by the CWI technique is investigated. The variation of This paper reveals the change rule of


Deep learning models are effectively employed to transfer learning to adopt learning from other areas. This research utilizes several neural structures to interpret the electroencephalogram images (EEG) of brain-injured cases to plan operative imagery-computerized interface models for controlling left and right hand movements. This research proposed a model parameter tuning with less training time using transfer learning techniques. The precision of the proposed model is assessed by the aptitudes of motor imagery detection. The experiments depict that the best performance is attained with the incorporation of the proposed EEG-DenseNet and the transfer model. The prediction accuracy of the model reached 96.5% with reduced time computational cost. These high performance proves that the EEG-DenseNet model has high prospective for motor imagery brain-injured therapy systems. It also productively exhibited the effectiveness of transfer learning techniques for enhancing the accuracy of electroencephalogram brain-injured therapy models. The brain signal acquisition model (BSA) is a message model that can learn brain actions connected to patients' objectives and transform them into control motion. BSA models are widely utilized in therapy of brain-injured cases. EEG signals deliver a noninvasive resolution for the BSA model and are utilized in most brain signal systems. BSA systems have the following phases: signal reading, image analysis, controller apparatus, and signal forwarding –4. The bDeep learning techniques are used for BSA systems, the EEG feature selection, prediction, and detection models –16. The Proposing a deep learning models with several extensions and parameter fine tuning using deep processEnhancing the accuracy of the BSA model for the recuperation of brain-injured patients, this research employs these deep model to process the EEG signals of brain-injured casesThe presented model is to incorporate fine tuning with neural and transfer learning leading to the proposed EEG-DenseNet model to detect motor imagery settingsThis research approves the model within the cases to assess the accuracy of all other models. By evaluation the experiments of compared models, it can be proven that EEG-DenseNet outperforms other transfer learning models in all other platforms. The expected performance of our model scopes 96.5% accuracy for both left hands and right handsIn this research, the paper's contribution is as follows:The remainder of this paper is organized as follows: The EEG signal is captured from 100 cases . The simulation utilizes 64-channel EEG reading device to gather data from brain-injured cases . Each experiment proceeds for 0.5 minutes; the details of one experiment is depicted in These data items are recorded and labeled in a public dataset that we utilized for our experiments . The staN, M, and S). N is defined as the count of recordings trials and is set to a constant equal to 3 which is usually enough. M defines the channels number; S defines the count of sampling items per channel. This research utilizes the brain-injured patients' EEG that has motor imagery containing hand movement. The data recorded for each case is divided into three data subsets namely: training, validation, and testing. There is a 100 trial recording for each case including motor imagery task. For each case, 90 data items are utilized for training, 15 for validation and 15 for testing. Randomly, K cross-validation is used where K = 12, to compute the expected accuracy of each case. We have 40 healthy people and 60 brain-injured patients.Preprocessing task comprises cleansing and downsizing. In this research, a 12–32 Hz low pass filter is utilized to remove noises , and theM and S): M defines the channels number, and S defines the count of sampling items per channel. This research utilizes the Adam optimizer [EEG-DenseNet is a dense CNN model for handling EEG signals. It is trained with small-sized dataset, and it can yield a neuro instruction. ptimizer , 20, 21 Adjust the factors of the output layer. Our model uses transfer learning for the initial layers, and adjust the classifier parametersModify the model CL parameters to fittingly diminish the learning level and epochs count. The learning level is comparatively low because the operative system weights are utilized for model tuning. If the learning level is elevated, the system can be modified rapidly and dismisses the initial weights. After tuning, this research selects to update all parameters , 38. TheBegin the training stage and compute the parameters of the transfer learning modelThe efficiency of transfer learning is influenced by many parameters. One of these parameters is correspondence among the training source input and the destination signals. The greater the correspondence, the greater the fine tunings outcome. The factors attained by the initial input and CL of the EEG-DenseNet are the elementary factors  –36. The The proposed model utilizes both the EEG feature selection captured in the transfer learning model and fine model tuning. This creates a robust adaptive model with parameter tuning for better motor imagery recognition.Fine tuning randomly set the weights of the pretrained network. Different datasets are utilized in the neural convolution for relearning. Also, weights are utilized on the preceding convolutional layers, and the preliminary weights on the preceding layers are set. The experiments produce better grouping of the unused layers and the fine-tuned layers, as depicted in t-test of the prediction performance linking to the 100 cases using the support vector machine, latent Dirichlet allocation, and our EEG-DenseNet model, with p value of 5.19 × 2−10. It is depicted that pr is about 0.055, and the prediction precision has shown momentous variances. It implicates that enhancement of our model is higher. For the input signals, prediction accuracy of the support vector machine, latent Dirichlet allocation classifier, and our model are investigated, and the experiments are depicted in Our research uses the training, validation, and testing datasets of each case into the deep learning models under comparison along with our model. We used the following definitions for the accuracy, sensitivity, and specificity:Three extension models have been developed on the proposed EEG-DenseNet method. The starting model is to statistically set the parameter of the deep learning model; then, a different subset is fed in for model (the extension method is named EEG-DenseNet_E1).The second extension model is to halt the variation of the weights in the block 1 of the proposed CNN in the transfer learning model and start retraining the other layers such that different weights can be attained .The third extension model is analogous to the second extension model, with the weights of both blocks in the proposed CNN unchanged .The three extension systems are compared. The expected prediction accuracies of 11 cases are depicted in It can be depicted from As a final note, the experimental results realized from the EEG-DenseNet_E2 model are as shown: in the EEG-DenseNet method, it is proven that deep factors are selected in the first one, while more specified features are extracted in the second block of the CNN structure.Performance comparison between our model and other models in terms of accuracy is depicted in In deep learning model, time complexity measurement is one of the norms for computing the model performance. The novelties of several models are established using the time computational complexity. To minimize time complexity, we substitute the multiplication operations into parallel addition operations. In this article, the research computed the floating-point operations per second (FLOPs) of all models to compute the time and space computational load of the proposed model .The count of the FLOP operations controls the training time and the classification time of the proposed model. If the CPU time are getting time consuming, it can affect the model training and classification CPU time to need a high time complexity, and it is impossible to achieve the real-time requirements.Memory requirements (MEM) are also important to compute the separability of the hardware modules of the module functionality. MEM computes the count of factors for optimizing the method. In memory restriction, the higher the number of the method parameters, the greater the quantity of input needed for model training. The size of the input in actual situations is typically not too high that deems the system modeling overfits.In this article, floating-point operations per second and MEM of all the methods are utilized to compute the time and space computational load, as depicted in In In Tables In this article, the research was to determine if the proposed EEG-DenseNet model that incorporated fine parameter tuning with deep learning can be efficiently utilized for small-sized input. The presented model is primarily used for the input signals of normal cases and brain-injured cases. The simulation can prove that the total prediction results of healthy cases are higher than brain-injured cases. It is more difficult to capture EEG signals from brain-injured cases as well as very costly. Brain-injured patients have hard time staying motionless without eye blinking or involuntary movements that frequently infect the captured EEG signals. Moreover, brain damage can extremely alter the lively properties of the EEG data, thus aggregating the uncertainty of the EEG distribution. It is an important issue to attain a large size and better quality EEG signals from brain-injured cases. It should be noted that the accuracy of patients' EEG actions may not accomplish the required effect, which are prospective parameters impacting the final performance results.This article investigated the input data of the whole cases both healthy and brain-injured patients to clarify the efficacy of the data communication of models with transfer learning. These neural structure models are motivated by image processing and feature extraction. The universal features of all cases can be learned through the initial CL model layers. Deeper more specific features are extracted from experimental training. This research can use small-sized databases by ceasing the prior layers of transfer learning and diminishes the count of model parameters that has to be maximized. This research indicates that the presented model can transfer learning for the same pattern. The experimental results depict that transfer learning should be incorporated in the paradigm of EEG processing. The EEG-DenseNet outperforms other state-of-the-art neural deep learning models in motor imagery detection. The experiments prove that we can utilize small-sized dataset for training. The learning process are efficiently done on the EEG signals of brain-injured cases via transfer learning modeling.


Many studies have shown that using a computer-aided detection (CAD) system does not significantly improve diagnostic accuracy in radiology, possibly because radiologists fail to interpret the CAD results properly. We tested this possibility using screening mammography as an illustrative example. We carried out two experiments, one using 28 practicing radiologists, and a second one using 25 non-professional subjects. During each trial, subjects were shown the following four pieces of information necessary for evaluating the actual probability of cancer in a given unseen mammogram: the binary decision of the CAD system as to whether the mammogram was positive for cancer, the true-positive and false-positive rates of the system, and the prevalence of breast cancer in the relevant patient population. Based only on this information, the subjects had to estimate the probability that the unseen mammogram in question was positive for cancer. Additionally, the non-professional subjects also had to decide, based on the same information, whether to recall the patients for additional testing. Both groups of subjects similarly (and significantly) overestimated the cancer probability regardless of the categorical CAD decision, suggesting that this effect is not peculiar to either group. The misestimations were not fully attributable to causes well-known in other contexts, such as base rate neglect or inverse fallacy. Non-professional subjects tended to recall the patients at high rates, even when the actual probably of cancer was at or near zero. Moreover, the recall rates closely reflected the subjects’ estimations of cancer probability. Together, our results show that subjects interpret CAD system output poorly when only the probabilistic information about the underlying decision parameters is available to them. Our results also highlight the need for making the output of CAD systems more readily interpretable, and for providing training and assistance to radiologists in evaluating the output.The online version contains supplementary material available at 10.1186/s41235-022-00375-9. Computer-aided detection (CAD) technology was first approved for clinical use—specifically for breast cancer detection—by the US Food and Drug Administration (FDA) in 1998. Since then, the use of CAD technology has increased dramatically, especially in clinical fields in which medical images play a central role, such as radiology, pathology, neurology, and surgery Doi, .The leading sub-specialty for CAD use in radiology is screening mammography, or periodic screening of asymptomatic women for breast cancer  in a given medical image. It is also because radiologists typically evaluate the CAD output together with the relevant medical images of, and other clinical information about, the patient  in Chicago, IL, USA.Twenty-eight practicing radiologists (six of whom were women and 22 of whom were from the US) participated in Experiment 1. Three of the subjects were practicing mammography specialists with an average of 29.3 years  of experience. The remaining 25 subjects were attending radiologists who specialized in another subspecialty of radiology, and/or were trainees (residents or fellows) who had completed an average of 10.9 years  of radiological experience. Since these demographic variables did not significantly affect the responses of the subjects (data not shown), we pooled the data across all 28 subjects.The radiologists volunteered for this study and provided informed consent with the proviso that each could only spare a few minutes of participation. This practical, unavoidable constraint limited the total number of trials each radiologist could perform to 20.i.p(C) in Eqs. the base rate of breast cancer in the given cohort of patients . No mammogram was shown (see below for caveats).the categorical  decision of the system as to whether the given mammogram was positive or negative for cancer  threaten the quality of medical care”, and that “the power of formal probabilistic reasoning provides great opportunities for improving the quality and effectiveness of medical care”. Many subsequent studies of CAD use have echoed this sentiment  the neglect of the prevalence of breast cancer, (2) overweighting of the binary decision of the CAD system in each individual case , and (3) the binary decision-dependent neglect of the false alarm rate. Note that the latter two factors pertain to the CAD system per se . Somewhat surprisingly, non-professional subjects were better at accounting for the base rate, although their overestimations were significantly worse. The reasons for these differences remain to be established.Importantly, our results empirically demonstrate that observed estimation errors were not fully attributable to well-known effects such as base rate neglect or overweighting of the individuating information by either group of subjects. That is, while these effects did contribute to the observed cancer probability estimates, they did not fully account for them. Incidentally, this finding highlights the importance of having empirically measured these errors in the specific context of interpreting CAD results, because our results could not have been predicted by simply extrapolating from the previous studies of the underlying estimation problem in other contexts .conditional neglect of the false alarm rate in any decision-making context.Fourth, our study identifies a novel effect that significantly contributes to the estimation errors by both radiologists and non-professional subjects, namely the conditional neglect of false alarm rates. The neglect of the false alarm rate has been previously reported in the context of legal decision-making . On the other hand, if the CAD system determines that the given mammogram is negative for cancer, the subjects ignore the false alarm rate, presumably because the false positive rate is moot when the system’s finding is negative to begin with. There is a certain intuitive logic to this.This effect is intriguing, because it means that subjects take the system’s false alarm rates into account if the system decides that the given mammogram is positive for cancer, and neglect the false alarm rates otherwise. One possible explanation of this is that if the CAD system reports that the given mammogram is positive for cancer, then the subjects consider the false alarm rate in order to help them determine whether or not the given positive report is a Note, incidentally, that the fact that the subjects do take false alarm rates into account does not necessarily mean that the subjects attach the correct weight to the rates. Indeed, our results indicate that the radiologists do not attach proper weight to this factor . Further studies are needed to fully characterize this effect.Finally, another significance of our study is that we quantitatively estimate the effect of each of the aforementioned contributing factors in both estimation and recall tasks. These estimates also confirm that CAD estimation errors reflect a unique weighted combination of the underlying contributing factors. This also suggests that there is no unifying explanation that a priori accounts for all such phenomena. The closest one can come to a unifying framework of explanation is that there is a finite set of potential contributing factors that potentially apply to all problems of this type, but the observed results in any given problem scenario depend on the relative weights of the various factors in that problem scenario. Since there is no known way of predicting these weights a priori, one must empirically estimate these weights  of the various factors in each case. Again, this vindicates our empirical approach.A corollary of this is that it can be misleading and counterproductive to attribute such errors a priori to a well-known cause, such as base rate neglect, or overweighting of the individuating information.conditional neglect of the false alarm rate in any decision-making context.Neglect of the base rate and the overweighting of the individuating information have been shown to cause estimation errors in other contexts (Fischhoff & Bar-Hillel, Some previous studies in other contexts have shown that other approaches, such as appropriately modifying the statement of the problem and decision-work flow, can also reduce estimation errors (Hoffrage & Gigerenzer, In addition to the various caveats specific to our study noted in context throughout this report, it is worth noting one general caveat that applies to our study and more broadly to all laboratory studies of clinical phenomena: Such studies simplify, out of both practical and scientific necessity, the underlying clinical problems. We have noted throughout this report the various ways in which our study does this. However, it is worth noting, without glossing over the limitations of our study, that our results would have been essentially uninterpretable if we had not simplified the study design by removing these potential confounding variables. For instance, if we had presented the actual mammograms to the subjects, we would not have been able to ascertain the contribution of the aforementioned four pieces of probabilistic information about the CAD system to the subjects’ responses, because of the potentially confounding contributions of the mammogram and how the subjects perceived it. In removing these confounds and presenting the results along with the applicable caveats as we do, our study adopted this standard tradeoff: Seek to clarify by simplifying.Much future work is needed to address the many questions raised by our study. These include, but are not limited to, ascertaining whether and to what extent our results, especially from non-professional subjects, are generalizable to actual clinical conditions.An important practical implication of our results is that that training subjects to properly interpret the output of CAD systems may help improve the efficacy of CAD systems in mammography (Yanase & Triantaphyllou, Additional file 1. Additional results from Experiments 1 and 2.