This paper presents an automatic classification of plastic material’s inorganic pigment using terahertz spectroscopy and convolutional neural networks (CNN). The plastic materials were placed between the THz transmitter and receiver, and the acquired THz signals were classified using a supervised learning approach. A THz frequency band between 0.1–1.2 THz produced a one-dimensional (1D) vector that is almost impossible to classify directly using supervised learning. This paper proposes a novel pre-processing of 1D THz data that transforms 1D data into 2D data, which are processed efficiently using a convolutional neural network. The proposed pre-processing algorithm consists of four steps: peak detection, envelope extraction, and a down-sampling procedure. The last main step introduces the windowing with spectrum dilatation that reorders 1D data into 2D data that can be considered as an image. The spectrum dilation techniques ensure the classifier’s robustness by suppressing measurement bias, reducing the complexity of the THz dataset with negligible loss of accuracy, and speeding up the network classification. The experimental results showed that the proposed approach achieved high accuracy using a CNN classifier, and outperforms 1D classification of THz data using support vector machine, naive Bayes, and other popular classification algorithms. The electronic circuit’s limitation and the excessive capability of optical devices to excite terahertz (THz) waves in the past have inhibited the THz spectrum exploitation in various industrial and laboratory applications. THz is electromagnetic radiation between the microwave and the infrared region within the frequency domain of 0.1–10 THz ,3. NowadRegarding the overview of the latest scientific publications, most THz applications are limited strictly to scientific research with the minority on engineering applications. The main reason stems from the fact that the engineers’ community is not aware of the technology, and there is a lack of ‘off-the-shelf appliances’. Many THz systems contain expensive elements such as photoconductive antennas, laser sources, low noise, and sensitive amplifiers to acquire the emitted radiation. With continued research on the physical principles of radiation, excitation, and probing, THz applications can be a breakthrough technology in the new upcoming industrial era. They can be used in various industrial applications and can offer valuable upgrades or replacements for standard devices. This paper presents an automated inorganic pigments (IP) classification of plastic materials using terahertz frequency domain spectroscopy (THz-FDS) as a source of data. The vast majority of the THz spectroscopy is done with the time domain spectroscopy (TDS) technique, which is robust and flexible. The TDS uses short pulses and coherent detection, which consequently disables standing waves and simplifies the data analysis. The frequency resolution is in the span of 1–5 GHz . RegardiPlastic materials are used widely in many fabrication processes, assembly, and design. The pigments’ classification of the processed workpiece is essential in quality control, material characterization, and fabrication validation ,7. The pMost of the data analysis in THz-FDS is undertaken by determining the attenuation and the phase shift of THz waves spread through the medium . The detBy reviewing the literature, we found that the classification of materials using a THz sensor is extremely difficult due to the wide THz spectrum, and the measurement can be contaminated with different uncertainties, such as DFLD non-linearities, measurement bias, and environmental impact. All the influences are known, but cannot be determined and compensated precisely in the measurement. The supervised learning procedure of the machine learning (ML) algorithm can be used according to the known indeterminate uncertainties. Supervised learning ensures robust mapping of ML over a set of input-output data pairs. All data pairs contain uncertainty, which can be suppressed efficiently under the assumption that the significant futures are preserved and hidden. Many different algorithms of ML exist, which can be deployed for IP classification. After extensive research and comparison between different ML strategies, the convolution neural network (CNN) gives beneficial results. The CNN algorithm is a subset of deep neural networks and deep learning paradigms , and hasThe paper proposes an automated IP classification using a novel preprocessing algorithm suitable for processing with CNN classification methods , which cThe paper has six sections. The Section 6The TeraScan 1550 from Toptica Photonics, Munich, Germany, was used for the THz-FDS experiment. The Tarascan 1550 can generate THz waves in the span of 0.03–1.21 THz. It has high THz power and a wide dynamic range. It utilizes mixing or beating the optical signals excited from the two DFB laser diodes with different wavelengths and a PCA emitter and detector. The experimental setup is presented in The experiment involved five different categories of samples with individual inorganic pigments, which differed in five colors, white, blue, green, yellow, and black. The geometrical parameters of the samples were equal in all the used batches. The samples with different pigments are presented in The sample organization for the deep learning algorithm will be discussed in section five. Only the essential operation principle of the Terascan 1550 is presented for a better overview of the approach and the general understanding of the readers. The system comprises two independent laser sources for signal modulation and two PCAs for emitting and detecting THz radiation, depicted in Two tunable DFB lasers were utilized for generating optical signals with different wavelengths,  heating ,32.The optical source was coupled with the PCAs. The PCA emitter acts as a capacitor with the charge  not lit . The pho not lit . The THz, In a transmission-based FDS system, such as that shown in The measured photocurrent in the detector PCA’s gap will depend on the emitted frequency and the absorption and dispersion in the medium. The THz far-field will also drop with the square of the distance between the emitter and detector PCA. Nevertheless, the distance between the PCA emitter and detector should be considered a phase shift, as shown in In the presented setup shown in The amplitude and phase can be extracted from the measured phase fringes in THz spectroscopy creates an immense amount of data which need to be processed. The experiment’s main idea and the presented methodology would confirm that the IP components mitigate and affect the THz microwaves in the unique futures. All the spectral futures in the measurement are surreptitious, and are not expressed clearly. The theory can confirm the hypothesis, but automatic classification needs in-depth analysis and the employment of a reliable processing technique.As mentioned, the acquired spectroscopic data contain profound information about the IP components, the pigment’s color and the prepared pigmented material’s quality. The prepared material’s quality is classified as a dye’s spatial density through the medium and the fabricated material’s quality. The preprocessing algorithm needs to be employed before the efficient classification with the CNN proceeds.The preprocessing of the acquired data involves the transformation of the series in a 2D single-channel image. The series can be transformed in different ways. The most common way to convert the sequence to a 2D array is presented in a data-driven approach (DDA). The DDA is often used for series forecasting ,36, and Regarding the DDA methodology, the acquired THz data can be organized similarly. The main drawback of DDA with THz-data series is the significant rise of the computational burden, which demands a complex CNN structure with an extended processing and learning phase. The DDA does not ensure sufficient robustness to the measurement uncertainty, which limits classification efficiency and reliability significantly. The 2D transformation procedure, which is based on four steps, is detailed in the presented work. The first three algorithms start with peak detection, envelope extraction, and the sub-sampling procedure. The last step introduced a windowing spectrum dilation (WSD) algorithm, which transforms 1D data into 2D data. The first three algorithms are used for WSD to lower the complexity of the data and the CNN structure. The proposed WSD is crucial for further classification and computational complexity. All four steps introduce various setting parameters, which affect the CNN’s efficiency. It is important to mention that there are many different possible approaches to transform a series into an array. The paper describes the unique procedure for THz data, which gives more expedient results. All four steps are discussed hereafter.The first two steps of preprocessing are peak detection and envelope extraction. Envelope extraction is based on quadratic spline interpolation over peak values. The peak-find function (PFF) objective is to find the local maxima in raw measured data, with two main parameters: Threshold value and the peak neighborhood interval. The threshold value ensures extraction of the upper envelope and rejects small extremes, whereas the peak neighborhood reduces lower near lying extremes. The quadratic spline interpolation follows after the PFF processing. The quadratic spline interpolation is used to preserve the correct envelope shape to cover all significant envelop transitions. The spline interpolation is calculated regarding the piecewise splines’ selection with given measured knots. Each interval length is selected arbitrarily and is an accuracy concern of the processed envelope. The interval length is fixed for all measurements. The last step before WSD is the downsampling algorithm. The downsampling procedure reduces the data complexity, and will be discussed in the section with WSD.,,,,The last step to prepare THz data for CNN processing is transforming the downsampled envelope to a 2D image. Before the WSD is discussed, the set cut-technique (SetCT) will be presented briefly. SetCT is often used in DDA, where the data series is distributed equidistantly in the matrix column or row. The technique performs well on the data set, where the normal distribution and standard deviation are slowly varying or constant. The SetCT transformation for the image The WSD method is presented due to the given facts. The WSD transforms the series so that each image row contains data from the beginning to the dilation factor. Each image row extended the spectrum stepwise with a dilation parameter. At the end of each consecutive row the original data belong to the higher measured specter. It follows that each subsequent row contains the data of the dilated specter, where the last row embraces the whole specter. The proposed approach reduces measurement uncertainty, and overcomes the problem with spectrum alignment. The WSD transformation for image The WSD transformation can be used on different data sets and preprocessing stages. Regarding the downsampling algorithm and WSD, the approach is practical for lowering image resolution while still preserving data characteristics. From the WSD adjacent image row’s observation, each further row contains the data set, which is extended in the measured spectrum, but downsampled regarding the data set. In other words, each new layer of WSD image represented the low-pass values of the data. This means that the adequately downsampled data preserves mainly the shape of the envelope in all image layers. The aforementioned confirms that the WSD image transformation is, consequently, more resilient to the uncertainties. The image does not contain sharp edges at the first rows of the image, as can be noticed in the SetCT method. Sharp edges are mainly a consequence of the image assembly with a fixed length, where the extracted envelope is decreasing monotonically. The preprocessing algorithm contains PFF, envelope extraction, the downsampling algorithm, and WSD transformation. The sequence of the preprocessing algorithms is presented in The CNN is used to classify different IP content in the plastic material. The significant advantage of CNN is the recognition’s robustness of spatially distributed futures, which mimics the eye’s natural phenomena of cortex vision ,36,37,38Regarding the preprocessing algorithms from In The CNN structure starts with the normalization function, which scales the input 2D data to an arbitrary span. Linear unit normalization was used in our work. The first layer starts with convolutional filters (CF) or kernels. The CF is a fixed valued moving frame with a preselected structure and stride. The repeated pattern extraction is allowed with the convolution operation over the inspection image with the kernel. If we represent the image as After the CNN structure selection with different convolution and pooling layers, the training process is associated with hyperparameters’ determination. The CNN training is based on cost function minimization, with appropriate adjustment of the kernel coefficient and fully connected layer weights. The network is trained with an ADAM optimizer, the performance of which has been confirmed by many scholars ,42,43. TEach of the CNN structures presented in The THz-FDS data for CNN training, validation, and testing were acquired from different batches with five different inorganic pigments see . Each baThe classification algorithms were tested on the THz-FDS data acquired with the TeraScan 1550 presented in The results in The obtained results of the CNN classification with SetCt and WSD are presented in The confusion matrix of CNN classification is presented in low. The comparisons with support vector machine, naive Bayes, classification tree, and discriminant analysis ,25,26 arlow, such a reduction cannot be applied. The full data set, without reduction, showed promising efficiency of the IP classification, whereby it requires longer training and operation time, full structure is more demanding and time-consuming than CNNlow, wherein kernel size, stride selection, and layer sequence require more attention and are more sensitive to parameter change than CNNlow. Comparing both reduced and full-data sets shows that the WSD is a more suitable technique than SetCT. The advantage of WSD is in the 2D data structuring and CNN convolution layer operation, where the claims discussed in low lowers CNN’s reliability, which means that the reduced 2D data after downsampling contained all the significant spectral futures of the measurement, and additional simplification inside the CNN eliminated them. Apparent differences can be seen from the confusion values in full and WSDlow, where the scattering of the SetCT scores is evident, which means the downsampling of SetCT deteriorated the results. The WSD confusion values in low, which is comparable with WSDfull. Comparing the classification algorithms in low. SVM and NB achieved results with an approximate accuracy of 70% and CT close to 50%. It needs to be mentioned that, with the use of unprocessed 1D data, the accuracy of SVM, NB, CT, DA was much lower than with the preprocessed data presented in The presented results in This paper presents an automated inorganic pigment classification in plastic material using THz spectroscopic data. The efficiency of 2D WSD transformation with CNN classification gave promising results, and outperformed similar classification techniques based on 1D input data. The 2D transformation of THz-FDS data showed a strong correlation with the classification accuracy, where the efficiency of WSD was confirmed with the experiment results. The WSD was more robust on uncertainties and bias, and enabled considerably more data simplification with insignificant reduction of the classification reliability. Data reduction speeds up the CNN processing, and can be appropriate for a real-time operation, where the accuracy is not the substantial trade-off between speed and accuracy. The work can be extended to additional pigments, and all the requirements can be preserved.The current state of the presented work shows additional possibilities for improvement. The first option could be exact determination of the THz span with higher pigment sensitivity. Such an approach will, additionally, reduce the data complexity and speed up classification. The second option would be regarding the WSD technique. The WSD can also be used in the opposite direction, starting at the end of the THz-FDS specter. In this case, the higher frequency data would be exploited notably, which could increase reliability. Finally, a very promising improvement, which should be worthy of investigation is the THz data transformation into the 2-channel 2D data. The two-channel 2D data can represent complex values, where extraction would be needed of the phase from the THz-FDS data.


In industry, ergonomists apply heuristic methods to determine workers’ exposure to ergonomic risks; however, current methods are limited to evaluating postures or measuring the duration and frequency of professional tasks. The work described here aims to deepen ergonomic analysis by using joint angles computed from inertial sensors to model the dynamics of professional movements and the collaboration between joints. This work is based on the hypothesis that with these models, it is possible to forecast workers’ posture and identify the joints contributing to the motion, which can later be used for ergonomic risk prevention. The modeling was based on the Gesture Operational Model, which uses autoregressive models to learn the dynamics of the joints by assuming associations between them. Euler angles were used for training to avoid forecasting errors such as bone stretching and invalid skeleton configurations, which commonly occur with models trained with joint positions. The statistical significance of the assumptions of each model was computed to determine the joints most involved in the movements. The forecasting performance of the models was evaluated, and the selection of joints was validated, by achieving a high gesture recognition performance. Finally, a sensitivity analysis was conducted to investigate the response of the system to disturbances and their effect on the posture. To fulfill market demands within specific time limits, job specifications and budget restrictions, the tasks performed by manual laborers in the industrial sector are becoming more challenging and complex. The tasks demanded of them require workers to go sometimes beyond their natural physical limitations, performing repetitive tasks for long periods of time. Being subjected to such constant physical strain leads to work-related musculoskeletal disorders (WMSDs) . WMSDs cThe ability to record accurate measurements for ergonomic analysis is essential as it provides ergonomists with quantitative measures of workers’ performance. This represents an added value in preventing ergonomic risk. Risk factors such as assuming awkward postures and performing highly repetitive or physically demanding tasks are often associated with WMSDs , mostly While methods based on self-assessment or visual observation, are quick and straightforward ways to evaluate, they are not always accurate and precise. They are quite subjective, since they are dependant on the worker’s feelings or sensations, or on the powers of observation of the ergonomist, leading, quite possibly, to low accuracy and high intra- and inter-observer variability . For metThe work presented in this paper aims to further expand the scope of the analysis conducted in current ergonomic evaluations by modeling professional movements. The hypothesis formulated here is that by modeling the workers’ dynamics, it is possible to extract information about the contribution of body joint movements to various ergonomic risks. Moreover, with the learned models, it is possible to predict the motion trajectory of body joints and thus detect any possible future exposure to postural ergonomic risk.For the purposes of this research, human motion modeling and trajectory predictions were made using a Gesture Operational Model (GOM) , which cIn In the past, biomechanical, stochastic, and hybrid models have been used to represent human motion and these models were then used to study the coordinated mechanical interaction between bones, muscles, and joints within the musculoskeletal system. The modeling of human movements, and their changes, caused by internal and external action forces has generally been addressed with biomechanical models. These models represent the human body as a set of articulated links in a kinetic chain where joint torques and forces are calculated using anthropometric, postural, and hand load data . InertiaIn previous research, statistical modeling has been used to learn the stochastic behavior of human motion. These models capture the variance information of body motion trajectories and have been used both to estimate human intentions and label human activities. In order to infer intentions from observed human movements in real-time, Wang  presenteT-tests revealed the biomechanical characteristics of the simulated injury trials. Donnell [Hybrid methodologies that take into consideration human biomechanical structure and the stochastics of motion have been developed to improve the analysis of the random outcomes of movement. A hybrid model, designed to predict the probability of injury and identify factors contributing to the risk of non-contact anterior cruciate ligament (ACL) injuries, has been proposed by Lin . A biome Donnell  used a t Donnell  proposedThe problem of human motion trajectory prediction has been researched extensively in the past. There is a growing interest, in the industrial sector, in implementing systems that allow prediction of how workers’ motion descriptors will unfold over time, and to incorporate this knowledge in a pro-active manner e.g., to facilitate human-robot collaboration or risk prevention. There are three prediction approaches, which are based on how human motion is represented and how the behavior pattern is formulated. Physics-based models are explicitly defined dynamic models explicitly defined and follow Newton’s Law of Motion. Pattern-based models, on the other hand, learn statistical behavioral patterns that emerge, based on the observed motion trajectories. Plan-based models are concerned with reasoning about the intention behind the movement and the goal of the performer.f). This model follows the form of a state-space representation:Physics-based models predict future human motions according to a defined dynamic model (ity (CV) , constanity (CV) , and cooity (CV) . These mFor the prediction of pedestrians’ position trajectories, previous studies have applied Kalman Filters (KFs), with kinematic models such as CV and CA ,28. The For the prediction of movements with a high level of uncertainty, previous studies have used multi-model (MM) methods. These methods fuse different motion modes  to describe complex motions , where a dynamic model represents each mode. Pool  applied by Kooij , which uby Kooij  adequatePhysics-based approaches are appropriate, where an explicit transition function can be defined for modeling the agent’s motion dynamics, as well as the influence of other agents and of their surroundings on it. The main drawback of using physics-based approaches is that they do not perform well for very complex situations . Moreover, their use is commonly limited to short-term predictions and obstacle-free environments.Pattern-based approaches, unlike physics-based approaches, learn human motion behaviors by fitting models to data. For the prediction of pedestrian trajectories, Quintero  presenteNeural Networks have achieved promising performances for time-series prediction ,35,36. AFor the capturing of more complex unknown dynamics, it has to be admitted that pattern-based approaches have outperformed physics-based approaches; however, they require a large amount of data to train the model to avoid generalization issues. To improve the prediction performance, pattern-based and physics-based approaches have benefited from integrating context information into their observations. The studies that included information about the shape and structure of the environment, together with the external forces that the person or object is exposed to, or information about their interaction with other agents  produce more precise predictions in numerous cases .The third prediction approach employs Planning-based models. Unlike the previous approaches, these assume rationality, in the case of tracked human movements and their long-term motion goals. This approach computes path hypotheses that allow the agent to reach their motion goals by considering the impact of current actions on future motions. The prediction is made using a predefined cost function, based on intended motion goals or inferred cost function, according to the observed trajectories. Best and Fitch  have proIn order to use planning-based approaches, the goals that the agents under analysis are trying to achieve must first be explicitly defined, and the context information about the environment surrounding the agent must be provided for the model. Planning-based approaches usually perform better for long-term predictions than do physics-based approaches and also tend to have less generalization issues than Pattern-based approaches. The downside of these approaches is that as the complexity of the prediction problem increases , so does the running time for training the models.Ergonomic evaluations have been conducted by identifying the risks involved in work-related motions, using gesture recognition (GR) techniques to recognize professional motions and estimate their frequency and duration on the workers’ shift. Peppoloni  developeHidden Markov Models (HMMs) have been widely used for the modeling and recognition of human gestures. HMMs model the dynamic behavior of gestural time series based on a probabilistic interpretation of the gesture samples. The HMMs assume that a hidden state sequence causes the observed sequence (gesture samples). HMMs capture the motion patterns presented in the training set’s gestures, meaning that they will not recognize other variations from these patterns that could emerge during the movement performance, after the training. To address this issue, Caramiaux  proposedDespite the fact that ergonomic evaluation based on GR adds factors such as the frequency and duration of activities into the analysis, basing the ergonomic evaluation on only these two factors could lead to the oversight of other risk factors in the motions that could cause the development of WMSDs.Due to the nature of the hypothesis defined for this study, a physics-based approach was selected to model the dynamics of professional movements. Physics-based approaches have proved to be capable of handling joint predictions efficiently and because of the use of a transition function, they perform well with observations obtained from different environments and subjects, without extensive training datasets. This generalization capability is essential if workers from various industrial sectors are to be monitored. Moreover, by using a physics-based model, information could be extracted regarding the human dynamics and their response to risk factors, by examining the resulting trained models.In this study, human motion was represented as a sequence of human poses, where each pose was described through 3D-joint angles. The modeling of each gesture was done using the Gesture Operational Model methodology , which wThe statistical significance of the assumptions of each model was computed to determine the body joints contributing the most to the professional movements. The selected joint angles were validated by comparing their gesture recognition performance with another two sensor configurations, the first using all joint angles for training, and the second using only a small set of two hand-picked sensors. Finally, the forecasting ability of the models was evaluated, and a sensitivity analysis was conducted to analyze the stability and behavior of the system when external forces affect system response, meaning a change in the posture and ergonomic risk level of the motion.Due to the advantages of using motion capture (MoCap) technologies, based on inertial sensors for the MoCap of industrial workers and the subjects’ movements, the BioMed bundle motion capture system from Nansense Inc.  was used. This system consisted of a full-body suit composed of 52 IMUs placed throughout the body and hands. The sensors allowed the orientation and acceleration of body segments on the articulated spine chain, shoulders, limbs and fingertips to be measured at a rate of 90 frames per second. Those 52 rotations were combined to create a kinematic skeleton that included the body segments measured. The Euler local joint angles on three axes X, Y, and Z were computed through the inverse kinematics solver provided by Nansense Studio (suit software). The joint angles per time frame were then exported to Biovision Hierarchy (BVH) files. Before the analysis, an offline pre-processing procedure of the data was followed. The motion data was low pass filtered to mitigate noise, and the common zero velocity update algorithm was applied to remove the drifting caused by electromagnetic interference.Industrial workers from television (TV) production, airplane manufacturing, and glassblowing sectors were recorded under real conditions in their respective factories. 1,1, G1,2, or G2,2 were mostly manipulating tools or objects, where the subject grabbed an object or prepared it for later use. The iterations for these gestures had a high intra-class variance since their motion was not restricted, nor was high precision or dexterity required. On the contrary, for the gesture vocabulary G3 and gestures G1,3, G2,1, and G2,3, the subjects needed to be more precise since they placed the objects in a specific position. It has to be considered that human factors such as level of experience, fatigue, or mental stress affected how the subjects’ bodies performed the gestures. Although this did not apply for the G3, high dexterity and technicity were required to execute the gestures effectively. The gestures from G3 were recorded from a glassblowing expert, who performed the gestures with high repeatability and low spatial and temporal variations between iterations, to produce a carafe four times with the same specifications. Regarding G4, there was a low intra-class variation in this dataset since the performance of each movement was controlled. Feedback was provided to subjects in order to perform the gesture demanded correctly. On the other hand, the inter-class variation was intended to be low, where there were only a few variations in the postures assumed in each gesture. The end in using this last dataset was to test whether the proposed methodology was able to identify the small variations between motions and provide an accurate estimate of the joints that most contributed to the execution of the 28 motions.The professional gestures from each gesture vocabulary presented essential differences in their execution due to the different contexts in which they were recorded. For instance, G3,3 and G3,4). An ergonomic analysis of these gesture vocabularies could therefore aid in improving ergonomically how the professional gestures were executed without affecting manual dexterity.From an ergonomic point of view, these four datasets could assist in the evaluation of human motions in industrial settings. The modeling of these motions could help in evaluating the subjects’ manual dexterity in relation to the gesture’s ergonomic risk. For example, as mentioned, the glassblower had a high level of dexterity for glassblowing gestures, but some observational methods could recognize that the gestures executed were ergonomically risky  and indirectly connected with the shoulder . These assumptions are represented in The Gesture Operational Model was composed of auto-regressive models that learn the dynamics of each body part. Each representation had different assumptions of the dynamic association between body parts. These assumptions consisedt of the intra-joint association (H1), inter-limb synergies (H2), serial (H3.1) and non-serial intra-limb mediations (H3.2), and transitioning over time (H4), . For theIntra-joint association: All body parts included it in their representation.Inter-limb synergies: Only the body parts representing joint angles from arm and leg parts included this assumption.Intra-limb serial and non-serial mediation: The assumptions included in each representation depended on the body part location within the body. The joint angles related to the spine only included in their equation joint angles of other spine parts with which it had serial or non-serial mediation. The serial and non-serial mediations from angles related to the spine are illustrated in The number of representations was equal to the number of associated dimensions for a given body part, multiplied by the number of body parts defined in the GOM. The representation of each body part had different assumptions depending on its location within the body:The transitioning assumptions corresponded to the lagged endogenous variables, where lag depended on the order given to the model. For this work, second-order autoregressive models were selected. The order was selected according to the correlation between lag values in the time series (auto-correlation). If the observations had positive auto-correlations with a certain number of lags, then it was better to have a higher order of differencing until the auto-correlation was negative and more than −0.5, to avoid overdifferencing .X-axis ,,6), the t, given previous observations. This probability distribution is considered Gaussian, as shown in the following equation:The full body modeling consisted of three sets of equations for each body part, one for each dimension X, Y, and Z. Hence, by discarding the body parts from the fingers, the GOM consisted of 84 equations per gesture. The coefficients of the equation system were estimated using the Maximum Likelihood Estimation (MLE) via Kalman filtering . For theequation:PO0:k|ψ=∏The Kalman filtering consisted of two steps which were repeated until obtaining the maximum likelihood. These are known as the prediction and update steps. Initial values were set, then the log-likelihood was computed for the evaluation in the prediction step. Next, in the update step, the variance and mean were updated according to the Kalman gain  for gesture recognition using an “all-shots” approach. The motion data of sensors that provided at least one of the top three joint angles contributing the most in the response for the spine, arms, and legs parts motion was used for gesture recognition. Since one sensor provided three angles of one joint, all the joint angles of the sensor were used for training. The first combination to test for gesture recognition consisted of a minimal sensor configuration: the best sensor to measure the spine, another which was the best to measure the arms, and a third for the legs. If the recognition performance was low, an extra sensor was added to the configuration to improve the performance, or it was replaced by another of the top three sensors selected to measure its corresponding body location . The configuration that achieved the best performance was compared with the recognition performance obtained using all the joint angles of the sensors. The recognition performance, by using only a minimal set of two sensors, was also computed for comparison. This minimal set consisted of two hand-picked sensors, which provided the Euler joint angles of the right forearm (RFA) and hips (H). The sensor placed on the right forearm was chosen since most of the subjects in all datasets were right-handed, and the hips sensor was chosen because the origin of all movement of the spine starts from the hips.v,c, where v, is associated to an HMM. The set of models for all gestures for every gesture vocabulary is To determine the best HMM setting for each gesture vocabulary, both ergodic and left-right topologies were tested, in addition to a different number of hidden states. The performance metric used consisted of the F-score. In the training phase of HMM, each professional gesture GB), variance proportion (UV), and covariance proportion (UC).For evaluating the forecasting performance of the GOM models, the joint angle sequences of each gesture were simulated by solving the simultaneous equation system of the GOM. The models forecasted one time frame per iteration, then, after forecasting all the time frames of the gesture, the simulated gesture was compared with the original for evaluation. Consequently, their forecasting ability was evaluated by computing Theil’s inequality coefficient (U) along with its decompositions: bias proportion  for the joint angle Here, an example of a joint angle motion equation for one gesture from each vocabulary will be provided. These examples are offered to enable visualization of the coefficients and p-values show intra-limb serial mediations with the joint angles on the Y and Z-axis of the left shoulder  for the joint angle of the neck on the X-axis  for the joint angle of the left shoulder on the X-axis, representing the motion of the left clavicle , average precision (91.90%) and recall (92.33%), over the minimized set of two sensors and the set with all sensors. The minimized set achieved an average precision of 74.16% and an average recall of 77.31%. By using all the sensors for the recognition, an average precision of 84.76% and an average recall of 86.46% was achieved.G1 in the execution of the three gestures was proven due to the high recognition performance achieved. By observing the results for G1 in 1,2 and G1,3. In the case of G1,1, the two sensors configuration had the best recall. Overall, the selected sensors had the best performance, with at least +1.2%. From the four gesture vocabularies, G1 had the best performance for gesture recognition, which could be due to the low inter-class variance between the three gestures.The relevance of the sensors selected for G2 was the gesture vocabulary with the highest number of sensors selected. The reason could be because the gestures in this vocabulary were more complex and more prolonged. The most complicated gesture to model and recognize was G2,2, which was expected since it is the gesture that could vary the most in its execution (high intraclass variance) from among the three gestures. The subject did not prepare the material in exactly the same way for each iteration. The subject was slower in some iterations than others since he required more time to adjust the pneumatic hammer or needed to prepare more rivets. The low intra-class variance could be because of the way the gestures were executed, which depended on the locations where the worker was going to fasten the metal plate with the rivets. For the recording of G2, there was only one airplane structure to build, and there were no iterations where the subject placed the pneumatic hammer in the same location more than once.G3 were validated by achieving a high recognition performance of the five gestures. By analyzing the results in 3,4 and G3,5, but then it decreased in comparison with the minimized set for G3,1. This could be because the information provided by the sensors S3 and LSH2 generated similar patterns between the gestures G3,1 and G3,3. The minimized set had the worse precision and recall for G3,4. The reason could be because of the lack of information on the motion of the shoulders. According to GOM, the shoulders contribute most to executing this gesture. Four out of the five gestures in this vocabulary generated similar patterns on the shoulder and arms. Still, there was low intra-class variation because of the high level of the subject’s dexterity, as an expert in glassblowing. In addition, the subject used a metal structure for shaping the carafe that limited any potential freedom in the gesture performances. Finally, for G4, a maximum F-score of 91.77% was recorded for the recognition of 28 motion primitives, using the selected sensors S2, LA, RS1, RUL and LFA. The poor performance of the minimized set was due to its failure to discriminate between motions that vary only with regard to posture of the legs, while the poor performance using all the sensors can be explained by the multiple dimensions of the data.The sensors selected for recognition of gestures from GThis section presents the results of the trajectory prediction and sensitivity analysis. X for the gesture of raising the hands above the shoulder level. In Y when the subjects rotated their torso to the left. The shock in this case was applied to the joint angles of H. It can be seen from the figure that the model was able to adapt fast and, indeed, in less than 1 s (90 frames), which indicated low sensitivity of the model to external disturbance. However, there was still a small variation in the forecasting if compared to the simulated gesture forecast without shocks.In Figure three examples of shocks applied to different variables for the sensitivity analysis are illustrated. This paper evaluates GOM’s feasibility to model industrial workers and subjects’ dynamics and select the joint angles that best represent the gesture vocabulary, and predict their joint angles’ trajectory. The statistical analysis conducted on the GOM models permitted identification of the joint angles that contributed most to the execution of the gestures of each vocabulary. For validation, these joint angles were then used for gesture recognition. These results demonstrate the potential of the selected set of sensors for a posture-based ergonomic analysis. By only using the data of the selected sensors, it was possible to discriminate accurately between different professional gestures and motion primitives where various postures of the spine, arms, and legs were assumed. The recognition of these changes in posture are clearly useful for ergonomic analysis of professional gestures. By applying a whole gesture to the trained HMMs from this vocabulary, the models could tell whether an awkward motion primitive is performed and which body part causes this ergonomic risk .By solving the simultaneous equations that compose the GOM, it was possible to accurately forecast the modeled gesture, using Euler joint angles as input. Moreover, the models are tolerant to small variations in the gestures and offsets between same class gestures, which could be due to different recording conditions (different subjects or different recording days). Regarding the sensitivity analysis, the models showed low sensitivity to external disturbances, with only a small variation in the forecasting from that of a simulation without shocks. The response of the models to the shocks applied on different variables could be useful for detecting any physical strain  or a load that affects the workers’ performance and increases the ergonomic risk of the motion.The industry has used ergonomic evaluations based on joint angle thresholds widely, due to their practicality. Previous studies have applied these evaluations in their analysis, where their only contribution was to fill them automatically using motion capture technologies ,11,12,13In this study, GOM was proved to be useful for ergonomic analysis of professional motions. In comparison with the approach taken in the previous study by Manitsaris , a more 2 and G3, workers used plastic gloves or did not wear the gloves that come with the inertial suit in order to avoid disturbances in the measurements. For this study, the motion data needed to be pre-processed after the recording to remove disturbances and drifts that could affect the results of the method.Despite the good performances and contributions achieved, this study highlighted some limitations regarding the use of inertial sensors in real workplace scenarios. Inertial sensors can offer precise and reliable measurements to study human motion; however, the degree of this precision and reliability depends on the site, movements, and tools handled during the performance. For instance, in the recording for the gesture vocabularies GFrom the literature reviewed, most of the studies used inertial sensors for quantifying the intensity, repetition, and duration of extreme motions and postures. The ability to extract information about work content from kinematics data is underutilized. Industrial workers perform complex professional gestures that contain crucial information about ergonomic risks. In this paper, not only was the contribution of every body joint in the execution of a specific professional gesture statistically estimated, but how they all operationally cooperate was modeled using GOM, and, in addition, their motion trajectories were accurately predicted using the trained models. GOM is based on state-space representations and consists of a simultaneous equation system of differential equations for all body body parts. The most significant joint motions for each gesture vocabulary were selected based on their statistical significance in the GOM models. The selection was then validated by achieving a high recognition performance of gestural time-series, which was modeled using continuous HMM. Four datasets were created for this work that contain professional gestures recorded under real conditions in factories and in a laboratory environment. The forecasting performance of the models was evaluated by comparing the simulated gestures with their original values. According to the Theil inequality coefficient and its decompositions, the performance of the models can be considered accurate.Analyzing the response of the models to external disturbances and identifying the body joints to enable tracking for ergonomic monitoring could be useful for faster and more efficient evaluation of workers’ gestures. Furthermore, the models could be used for ergonomic risk prevention. They could detect patterns in the motion trajectories that imply exposure to an ergonomic risk factor .Lastly, using a full-body mocap suit in an industrial context has several difficulties. This study contributes to the literature by identifying the minimum motion descriptors to measure. This allows for the use of less intrusive technologies, such as smartphones and smartwatches, to measure these same motion descriptors. Future work will consist of adding kinetic measures to the assumptions that GOM models are composed of , to complement the kinematic information, and will consider the effect of loads on the kinetic variables, which could indicate worker exposure to higher ergonomic risk.


Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. However, mammography images suffer from low contrast, background noise as well as contrast as non-coherency among the regions, and these factors makes breast cancer diagnosis challenging. These problems can be overcome by using a new image enhancement technique. The objective of this research work is to enhance mammography images to improve the overall process of segmentation and classification of breast cancer diagnosis. We proposed the image enhancement for mammogram images, as well as the ablation of the pectoral muscle. The image enhancement technique involves several steps. In the first step, we process the mammography images in three channels , the second step is based on the uniformity of the background on morphological operations, and the third step is to obtain a well-contrasted image using principal component analysis (PCA). The fourth step is based on the removal of the pectoral muscle using a seed-based region growth technique, and the last step contains the coherence of the different regions of the image using a second order Gaussian Laplacian (LoG) and an oriented diffusion filter to obtain a much-improved contrast image. The proposed image enhancement technique is tested with our data collected from different hospitals in Qassim health cluster Qassim province Saudi Arabia, and it contains the five Breast Imaging and Reporting System (BI-RADS) categories and this database contained 11,194 images  view and mediolateral oblique(MLO) view of mammography images), and we used approximately 700 images to validate our database. We have achieved improved performance in terms of peak signal-to-noise ratio, contrast, and effective measurement of enhancement (EME) as well as our proposed image enhancement technique outperforms existing image enhancement methods. This performance of our proposed method demonstrates the ability to improve the diagnostic performance of the computerized breast cancer detection method. Breast cancer is one of the most invasive and deadly cancers in women around the world. According to the World Health Organization (WHO), 2.3 million women will have breast cancer in 2020, and there have been 685,000 deaths worldwide from breast cancer . AccordiMammogram images are scanned for analysis of breast cancer, and it plays an essential role in detecting breast cancer early and helps reduce the death rate. But many factors affect the viewing of mammography images, and it is difficult for the radiologist to make a proper diagnosis . These fBI-RADS (Breast Imaging Reporting and Data System) is the mammographic standardization and quality assurance lexicon report represented by the American College of Radiology (ACR). The main objective of BI-RADS is to organize mammography report among radiologists and make it homogenize for clinicians . BI-RADSDepending on the biological structure of the breast, cancer cells spread to the lymph nodes and affect other parts of the body, such as the lungs. Many researchers also found that hormonal lifestyle and environmental changes also lead to increased breast cancer . The breThere are two factors for the detection of breast cancer at an early stage: the proper acquisition process to obtain mammogram images and, secondly, accurate analysis of the mammogram images for the diagnosis of breast cancer. The manual process takes time and could delay the processing process. Accurate viewing of mammogram images is always a difficult task, especially with a large number of databases. This challenge can be overcome by using a computational method such as image processing techniques or the breast cancer analysis algorithm. These algorithms lead to rapid analysis and reduce the workload of medical experts. However, it is essential to study the nature of mammography images before using the algorithm based on the image process to detect breast cancer .Computerized analysis of mammography is also challenging because one of the most important challenges is observing the pectoral muscles. The geometric shape of the pectoral muscles and their location depend on the mammographic images’ specific view ,13. TherImage enhancement techniques are generally divided into three categories. These categories are spatial domain, frequency domain and a combination of these two . FurtherImplementation of novel image enhancement techniques for mammogram images and improves the overall quality of appearance.This image enhancement technique can be used with post-processing steps for the early detection of breast cancer.The contrast analysis for observation of abnormal lesions improves the segmentation and helps to diagnose the progress of breast cancer.These pre-processing steps have improved the performance of existing methods based on machine learning.The image enhancement may improve the training processing of an extensive database in supervised methods for breast cancer detection.In this research work, we proposed an image enhancement technique for mammography images for early detection of breast cancer. The image enhancement technique we propose has four steps. In the first and second steps, we will process the image and categorize it into three channels as mammography images are in jpeg format in almost all databases. We remove uneven illumination using morphological techniques to achieve a uniform background image. The third step contains obtaining a well-contrasted or grayscale image that gives us a good breast image with more observation of the abnormal region. The fourth step is to remove the pectoral muscle from the mammogram images, as the mammography images contain a lot of the background. We used a technique based on the seeds region points of the image depending on the orientation of the muscle. This four-step module is known as the Breast Cancer Image Enhancement Technique, and it gives us a computerized process of early detection of breast cancer. However, the main challenging task is the impact of the image enhancement technique on the segmentation and of the abnormal region. We have proposed a post-processing module based on second-order Gaussian filtering, diffusion filtering, and K-means for the segmentation of cancer regions to detect breast cancer. The main contributions of this research work are:The article is organized as follows. Mendez et al.  implemenRaba et al.  developeMaitra et al.  implemenA few contrast enhancement techniques are useful for mammogram images to improve the performance of segmentation method, but most convention enhancement techniques are not useful for enhancing the contrast of mammogram images. In the last 21 years, many enhancement techniques have been implemented to enhance the low and varying contrast of mammogram images. Many detailed review papers are presented based on contrast enhancement techniques for mammogram images. Cheng et al.  discusseOur proposed image contrast enhancement technique is shown in Mammogram images are processed as input images in our image enhancement technique to obtain the much-enhanced image. There are two types of mammography: film screen mammography and digital mammography. We used digital mammography, also known as full field digital mammography (FFDM) . These dWe process all three channels of mammography images. From our first step of the analysis, the green channel gives better contrast and more detail on the pectoral muscle than the other two channels. It has better histogram images that show good contrast distribution. However, there is still noise and uneven illumination due to the image acquisition process. Still, we cannot eliminate the analysis of the other two channels because we convert the three channels into a single grayscale image using the principal component analysis. It is the next step after removing uneven illumination and noise problem. We dealt with uneven illumination and noise using morphological techniques. We used basic morphological tactics called bottom hat and top hat operations to remove background noise and make the image contrast uniform. It is observed that there are variations of intensities in the image, and especially the region of the pectoral muscles, as shown in The • shows the close operation. Then, we applied the top hat operation to increase the contrast and control the change in contrast of the pectoral muscle or the cancerous region. The mathematical representation of the top hat operation is defined in the Equation . The ∘ sAfter the background uniformity of the channels and eliminating most of the non-uniform regions of the image, the next most important task is to combine all the channels into a single grayscale image. It is necessary to remove the amount of data to be processed by the later stages of breast cancer detection. Many types of research have selected the green channel, instead of selecting the green channel only for its possible grayscale representation as adopted by many earlier researchers in this area , we prefColor-to-gray conversion is adopted to combine all previously processed tricolor images with their respective non-uniform removal process. We used the PCA technique for converting three channels to a grayscale image. The first step involves forming a vector color image  to normalize the set of mammography images as the output is shown in Calculate the second moment matrix for each pixel.Create the diffusion matrix for each pixel.D is I is the image entered in the process.Calculate the change in intensity for each pixel as Update the image using the diffusion equation as follows:The more coherent images lead to more precise segmentation of the cancerous region from the mammography images. We apply the oriented diffusion filter  for coheThe diffusion process is an iterative algorithm that processes the pixels from the initial mammography images to develop a smoother structure with each step . The finWe collected the data from different hospitals in Qassim health cluster, Qassim province, Saudi Arabia hospital, and the database contains 11,194 mammogram images, and the database is categorized according to the Breast Imaging Data and Reporting System (BI-RADS). The number of images in the database is shown in the The Mammographic Image Analysis Society (MIAS) was created by British research groups with the aim of understanding images from mammograms. Mammography images are from the UK National Breast Screening Programme. The images are digitized at 50 microns of pixel edge with a Joyce-Loebl scanning microdensitometer, a linear device in the 0–3.2 optical density range and representing each pixel with an 8-bit word. The MIAS database contains 322 digitized images of 161 pairs. The database has been reduced to a 200 micron pixel edge and padded so that all images are (1)Peak signal to noise ratio (PSNR);(2)Image contrast;(3)Effective measure of enhancement (EME).The proposed image enhancement technique is evaluated based on contrast enhancement, noise reduction and unwanted artifacts, and more details. We calculated the three parameters to know the effectiveness of our proposed image enhancement technique for mammography images:L is the maximum pixels value of image, and it would be 255 of 8-bit pixels image There are many processes to calculate PSNR, but we figured the value of PSNR based on the mean square estimation (MSE). First, we will calculate the MSE (as shown in the Equation ) of the M and N represent the width and height of the image, respectively. The higher the contrast value, the better the image information. Taking the logarithm of contrast is converting it to decibel unit (dB), as shown in the Equation CconThe EME is the quantitative measure of image enhancement, and it gives information about the contrast of each image block and is calculated using the Equation . Where KWe also measured the PSNR, the image contrast, and the EME of each category of databases, as we analyzed the image in terms of visual observation. The We compare the performance of our proposed image enhancement technique with other techniques such as Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Histogram Modified-Local Contrast Enhancement (HM-LCM), Bi-level Histogram Modification-Adaptive Nonlinear Filter (BHM-ANF) and Retinex. It can be observed from We used the Kmeans as tested the capability of our proposed image enhancement technique. We have analyzed Kmeans with cluster parameters range from 0 to 8. We got the best output of Kmeans from cluster of 2, 4, 6 and 8. It is clearly observed in We measured the accuracy, sensitivity and specificity of our post-processing output as shown in the The output image is considered to be an enhanced image compared to the original image by evaluation of the measurement parameters, as well as by visual observation. In this research work, the new image enhancement method is proposed and tested on all BI-RADS categories for the detection of breast cancer. The performance of the proposed method is analyzed on the basis of visual perception and quantitative measurements. We obtained the best enhanced image, and the performance of the proposed method is compared to state of the art contrast enhancement techniques such as HE, CLAHE and BBHE. Our proposed image enhancement technique is validated against a large database that contains approximately 11,000 images, but we have used approximately 700 images to validate this database. As in the basis on state-of-the-art, previous methods for breast cancer, whether enhancement-, segmentation-, or classification-based methods, did not use all categories of BI-RADS, but we have validated our technique from negative (BI-RADS-1) to malignant (BI-RADS-5) images.Our proposed image enhancement technique contains the different steps to improve the contrast of the image in terms of visual perception, noise reduction as well as contrast enhancement. We used new imaging techniques to obtain a well-contrasted image as well as coherence filtering to obtain a well-normalized image. The main objective of the image enhancement technique is to aid the post processing steps of any image processing method or machine learning method. There is still room to improve the image enhancement technique as it gives an improved performance on all categories, but it decreases performance on the higher level of BI-RADS. Therefore, the tuning parameters-based filters like scaled normalized Gaussian filtering can be used to set parameters according to the properties of the image in order to obtain a well contrasted image. This image enhancement technique can also be used to improve the performance of a deep learning-based method for breast cancer, as it can be used as an input technique for the training process. Because good training of the database provides the best segmented output, and this is one of the contributions that we propose. This image enhancement technique can improve the training process of the machine learning method and it also improves the performance of traditional methods when used as a pre-treatment module.


The relationship between sleep posture and sleep quality has been studied comprehensively. Over 70% of chronic diseases are highly correlated with sleep problems. However, sleep posture monitoring requires professional devices and trained nursing staff in a medical center. This paper proposes a contactless sleep-monitoring Internet of Things (IoT) system that is equipped with a Raspberry Pi 4 Model B; radio-frequency identification (RFID) tags are embedded in bed sheets as part of a low-cost and low-power microsystem. Random forest classification (RFC) is used to recognize sleep postures, which are then uploaded to the server database via Wi-Fi and displayed on a terminal. The experimental results obtained using RFC were compared to those obtained via the support vector machine (SVM) method and the multilayer perceptron (MLP) algorithm to validate the performance of the proposed system. The developed system can be also applied for sleep self-management at home and wireless sleep monitoring in medical wards. Most people spend one third of their time sleeping, and the quality of their sleep is closely related to their health. Sleep quality is affected by many sleep disorders, such as sleep apnea, insomnia, and snoring. According to , over 70In recent years, many kinds of hardware for sleep posture monitoring have been used. Pressure sensor pads were employed to monitor the pressure changes exerted by a body on a mattress ,3; noncoAdditionally, there are three popular classification algorithms for sleeping posture monitoring: the multilayer perceptron (MLP) algorithm ,12,15, tSVM is a supervised machine-learning model that analyzes data for classification and regression analysis; based on statistical learning frameworks, it is among the most robust prediction methods. SVMs can be used to solve various real-world problems, such as text and hypertext categorization, images classification, satellite data classification, hand-written character recognition, and biological sciences.The RFC method is an ensemble-based technique that can be used to perform classification tasks. RF method utilizes a multitude of classification trees randomly and then generates a final result. These trees are randomly created by extracting samples from different subsets of the same dataset, and different types of features are employed each time to create the trees. Consequently, the RFC model does not overfit the data during the decision tree process . Once thReferring to ,13 and cThree contributions/advantages of this system are summarized as follows:(1) The AIOT system with a Raspberry Pi microprocessor and nonintrusive passive RFID tags embedded in the bed sheets provides safe, comfortable, low-cost, and privacy-protected monitoring so that users can maintain their normal sleeping habits.(2) This system can be applied in nursing homes and in hospitals to monitor a patient’s prone position and to deliver reminders, reducing the workload of the nursing staff. The system also provides a data collection function to allow users and doctors to view historical records online.(3) The RFC requires only 25 RFID tags and approximately 4000 data points to achieve an accuracy of 88.9%. The results demonstrate that RFID has great potential as a monitoring sleep posture method.In this paper, the system description and methods are discussed in This sleep posture monitoring system is largely based on the Raspberry Pi 4 Model B microprocessor combined with a high-performance, ultra-high-frequency (UHF) RFID Reader (QBG12X) and its tags; these components were used to construct an Internet of Things system with integrated artificial intelligence (AI) for sleeping posture recognition and monitoring, as depicted in The experimental sample data were obtained by monitoring the sleeping position of a single person on a bed with RFID tags. The experimental bed at the bottom left in A simple neural network was also previously employed for sleep posture classification ,12. HoweThe SVM method was combined with either the quadratic kernel to recognize different sleep postures  or the rThe RFC algorithm has the following advantages : (1) it In the recursive process of building the classification tree, the classification and regression tree (CART) consistently selects the feature with the smallest Gini information gain in the current dataset as the node partition decision tree. The dichotomy of the CART algorithm can simplify the scale of the decision tree and improve the efficiency at which the trees can be created. To avoid overfitting, the CART decision tree must be pruned.The operational process of RF classification can be described as follows :N samples are presented, each sample has M variables (features), and n training data are created through random sampling with replacement.(1) RF first uses bootstrap sampling to create the training data. If iθ is then generated for each training datum through the random selection of m (m < M) variables and by attempting to split each variable. When the minimum Gini coefficient is reached, the tree is split to create the CART.(2) A different random vector (3) The resultant tree grows without pruning.n trees are combined, and the majority voting method is used to cast the predicted value.(4) The results of GI(t) is expressed in the following equation:i at a certain node t. If the variables have multiple collinearity or unbalanced data, the RF algorithm can be used [The Gini coefficient  be used .K-fold cross-validation, where the training set is first divided into K subsets of equal size, and then one subset is sequentially tested using the classifier trained on the remaining K − 1 subsets. Thus, each instance of the whole training set is predicted once, and the cross-validation accuracy is the percentage of data that are correctly classified. In addition, the grid-search method is recommended for the cross-validation procedure to prevent overfitting. The grid search screens all candidate parameter selections and tests every possibility until the best-performing parameter is identified. The principle is similar to that of determining the maximum value in an array.A common strategy used to verify whether the classifier can accurately predict unknown data (testing data) is to separate the dataset into two parts, one of which is considered unknown. The prediction accuracy obtained from the unknown set precisely reflects the classification performance on an independent dataset. An improved version of this procedure is called The Raspberry Pi 4 Model B processor  consistsThe RFID principle for sleeping posture monitoring is that RFID electromagnetic waves cannot penetrate human tissues, so the tags covered by human tissues will not be read by the reader. There are 5 × 5 tags that are evenly set up on the bed sheet, so each posture has one received signal strength indicator (RSSI) value corresponding to a set of 25 tags. The sleeping posture is labeled corresponding to each set of data as follows: supine posture as 1, lying right as 2, lying left as 3, and empty bed status as 0. This process generates the dataset.A total of 41 participants were enrolled in the experiment, and each participant performed his own version of three sleeping postures (120 sample data points) with slight posture changes in each sleeping position. The participants were 20 to 30 years old, weighed 40 to 110 kg, were between 150 and 185 cm in height, and were healthy. Deleting some duplicate data to ensure the diversity of the information, 3854 samples were obtained from the total of 4920. At the end of the experimental stage, the 3854 samples will be divided, 70% from 29 participants being used for training and the remaining 30% from the other 12 participants being used for testing.The proposed RFC-based sleep posture monitoring system was verified using the aforementioned hardware system and datasets. In addition, the experimental results based on RFC were compared to those obtained using the SVM method and MLP algorithm to demonstrate the superior performance of the proposed system. The Scikit-learn function library , a free N that had the greatest effect on the classifier; the number of maximum features M that can complicate or simplify the model; the minimum number of samples for the branch nodes of the tree; and the maximum depth of the decision tree, which can be generally adjusted according to the size of the sample data. The optimized parameter set obtained through the grid search was . For the MLP, three parameters were adjusted: the size of the hidden layer and the crucial hyperparameter of the MLP that represents the number of layers and number of neurons in the hidden layer; the number of maximum iterations of the classifier; and the solver function used for weight optimization, which includes the limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm, stochastic gradient decent, and adaptive moment estimation. The BFGS algorithm is an iterative method that can be used to solve unconstrained nonlinear optimization problems and that determines the descent direction by preconditioning the gradient with curvature information. The computational complexity of the update of the BFGS matrix without requiring matrix inversion is only For RFC, four parameters were adjusted: the number of subtrees K-fold cross-validation on the training samples was performed, and the recognition accuracy of the three classifiers was compared . Users who query the real-time sleeping posture and sleeping posture history data in the cloud database are verified by the system for anti-counterfeiting purposes at login. In addition, all of the monitored objects to be checked are handled in accordance with the relevant regulations of the country in terms of the disclosure of medical information. The user at the front end of the system is only able to know the tag numbers of the RFID. Only the authorized back-end cloud manager will know the person who uses the tags, that is, the relation between tag numbers of the RFID and bed number.Comparing three RFID-based sleep monitor systems ,13 and tThe proposed system incorporates the Raspberry Pi 4 and RFID Reader and its tags to construct an Internet of Things system with integrated RFC for sleeping posture recognition and monitoring and that avoids sensing foreign bodies and protects user privacy. The experimental results demonstrated that the proposed RFC-based system exhibited the best performance compared with those using SVM and MLP, achieving an accuracy of 88.9%. In a hospital or nursing home scenario, when a patient maintains a sleeping position for a prolonged period of time, the proposed system notifies the nurses are that they should turn the patient over.With the technological innovation of RFID, the accuracy of this system can be increasingly improved to provide more functions, such as those measuring user temperature and the humidity and moisture in the bed. These tags can also be integrated into clothing to monitor the movement and speed of the wearer in the room. In addition, our future work will determine advanced modifications that can be made to algorithm, and state-of-the-art algorithms will be adopted for the designed system to improve its performance.


Gastric cancer is a common malignant tumor of the digestive system with no specific symptoms. Due to the limited knowledge of pathogenesis, patients are usually diagnosed in advanced stage and do not have effective treatment methods. Proteome has unique tissue and time specificity and can reflect the influence of external factors that has become a potential biomarker for early diagnosis. Therefore, discovering gastric cancer-related proteins could greatly help researchers design drugs and develop an early diagnosis kit. However, identifying gastric cancer-related proteins by biological experiments is time- and money-consuming. With the high speed increase of data, it has become a hot issue to mine the knowledge of proteomics data on a large scale through computational methods. Based on the hypothesis that the stronger the association between the two proteins, the more likely they are to be associated with the same disease, in this paper, we constructed both disease similarity network and protein interaction network. Then, Graph Convolutional Networks (GCN) was applied to extract topological features of these networks. Finally, Xgboost was used to identify the relationship between proteins and gastric cancer. Results of 10-cross validation experiments show high area under the curve (AUC) (0.85) and area under the precision recall (AUPR) curve (0.76) of our method, which proves the effectiveness of our method. Gastric cancer is a worldwide disease with high incidence rate and mortality rate, especially in East Asia . AccordiAlthough many researchers tend to reveal diseases pathogenic mechanism by genomics , changesThere are still few proteins known to be related to gastric cancer. With the explosive growth of various types of omics data ,2021a, cThere are four steps to implement GXGCP. Step 1 is to construct disease similarity network and protein interaction network. Step 2 is using GCN to extract topological features of disease similarity network and protein interaction network, respectively. Step 3 is to reduce the dimension of protein and gastric cancer features by principal component analysis (PCA). Step 4 is to identify gastric cancer-related proteins based on the features of protein and gastric cancer by Xgboost. The workflow of GXGCP is shown in We used SemFunsim  to obtaiWe downloaded protein interaction information from Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) . Based oTo fully extract topological features of protein and disease network, GCN was applied . The aimH(0) is node’s feature in the network.where First, Laplace transform should be done on the network:where D is the degree matrix of the network, and A is the adjacency matrix.Then, normalization should be implemented on the Laplacian matrix:Lsym is defined as:f can reduce the loss of classification after the convolution transformation of the convolution kernel. The core of the machine learning task on the graph is to find a convolution kernel that can reduce the loss, regard h(λ1),…h(λn) as the parameters of the model, and apply the gradient descent method to update these parameters.With the Laplacian matrix, we can perform spectral convolution on the network. We need to find a suitable convolution kernel so that The final formula of GCN would be:Wl) as the final predicted value.Assuming that a given sample set has n samples and m features, thenxi represents the i-th sample, yi represents the i-th category label, and the space F of the regression tree (CART tree) is:where where q represents the structure of each tree, it maps the sample to the corresponding leaf node; T is the number of leaf nodes of the corresponding tree; f(x) corresponds to the structure q of the tree and the leaf node weight w. Therefore, the predicted value of Xgboost is the sum of the values of the leaf nodes corresponding to each tree.Our goal is to learn these k trees, so we minimize the following objective function with regular terms:where We implemented 10-cross validation experiments to test the performance of GXGCP. We divided our data into 10 groups. We used nine of 10 groups’ data to train the model and the data of the remaining one to test the model. After repeating this process 10 times, each group has been tested once. To show the accuracy of our model, we compared GXGCP with several other methods such as RWXGCP, GXGCP without PCA, GSVMCP, GANNCP, and GCNNCP. RWXGCP replaces the GCN part of GXGCP with random walk (RW). GSVMCP replaces the Xgboost part of GXGCP with support vector machine (SVM). GANNCP replaces the Xgboost part of GXGCP with artificial neural network (ANN). GCNNCP replaces the Xgboost part of GXGCP with convolutional neural network (CNN).The area under the curve (AUC) and area under the precision recall (AUPR) curve of GXGCP are shown in As shown in Protein is the main executor of life activities. To decrypt the genome, you must first systematically understand the proteome. Identifying gastric cancer-related proteins can greatly help develop screening or testing tools for tumor detection, early diagnosis or differential diagnosis, prognostic analysis, efficacy evaluation, etc. Due to the high cost of biological experiments, we proposed GXGCP that fuses GCN, Xgboost, and PCA to identify gastric cancer-related proteins. To verify the accuracy of our method, we did 10-cross validation experiments. The results show that the AUC of GXGCP reached 0.85 and AUPR reached 0.76. To show the superiority of GXGCP, we compared it with several other methods, and GXGCP performed best. Overall, we propose a novel, efficient, and accurate method for large-scale identification of gastric cancer-related proteins, which would greatly benefit the study of the pathogenic mechanism and clinical research of gastric cancer.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.HZ and RX wrote this manuscript and did experiments. MD and YZ provided important ideas. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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In recent years, much more emphasis than before has been placed on fire safety regulations by the local and central authorities of China, which makes fire risk assessments more important. In this paper we propose a new fire risk assessment approach for large-scale commercial and high-rise buildings that aims to evaluate the performances of their fire safety systems; this should improve the fire risk management and public safety in those buildings. According to the features of large-scale commercial and high-rise buildings, a fire-risk indexing system was built, and based on it we established a scientific fire risk evaluation system. To this end, the fuzzy analytic hierarchy process (FAHP) was used to assign a reasonable weight to each fire risk factor in the evaluation system. In addition, we revised the original scores by analyzing the coupling relationships among the fire risk factors. To validate our system, we selected 11 buildings in Shandong province and collected their fire safety data. Then, we calculated the final scores for the fire safety management of those buildings, and the results show that: (1) our fire risk evaluation system can assign reasonable weights; (2) the proposed evaluation system is comprehensive and has strong interpretability, since it exploits the coupling relationships among the risk factors. The novelty of the proposed approach lies in that it integrates opinions from multiple experts and utilizes coupling relationships among the factors. Further, the feedback from the approach can find not only the weaknesses in fire risk management, but also the potential causes of fires. As a result, the feedback from our assessment can assist the safety chiefs and inspectors with improving fire risk management. Fire is a common risk in our daily lives. In recent years, the annual average number of fires reported in China has exceeded 100,000, causing more than 2000 deaths and over one billion US dollars in losses each year ,4. In orThe current assessment of fire safety management relies mostly on professional fire maintenance staff and manually-conducted statistics, analysis and inspection . In suchTo tackle the problems mentioned above, we propose a novel fire risk assessment approach based on the policy documents published by the Chinese Ministry of Emergency Management. The major goal of this work was to design a comprehensive, reliable and expert-knowledge-based fire risk evaluation system for large-scale commercial and high-rise buildings. To that end, this paper first investigated the national standards of fire safety management, the monthly maintenance reports and the annual fire assessment reports. Based on those documents, the assessment considered multiple perspectives, such as the basic features of those two types of buildings, fire emergency rescue capabilities and the maintenance of firefighting facilities. We present our fire risk evaluation system based on the proposed approach, and there are two highlights of this evaluation system. First, among many multiple-criteria decision analysis (MCDA) methods which can be applied to assign weights in risk assessment, such as the analytic hierarchy process (AHP) , the tecThe main contribution of this work is to propose a novel fire risk assessment approach for large-scale commercial and high-rise buildings. The proposed approach uses the FAHP to assign the weights for categories and factors during the fire risk assessment by integrating knowledge of multiple experts. Therefore, the FAHP makes the weights more scientific and reliable. Moreover, the proposed approach revises the scores of factors based on the coupling relationships between the factors. By considering not only the factors with low scores but also those coupled factors with awful performance, the assessment result can help the safety chiefs and inspectors efficiently identify the most urgent risk factors to improve the fire safety management. One innovation of this work is to provide a way to make the fire risk assessment more objective by combining the knowledge and experience of multiple experts. The other innovation is to consider the cause and effect coupling relationship between each pair of factors in a fire-risk indexing system such that both the safety chiefs and the inspectors can efficiently improve the weaknesses in the fire safety management of a building. For the follow-up development of a fire risk assessment, an important topic in the public health discipline, our work provides assistance. To be specific, the proposed work offers convincing weights assigned by multiple experts, and the coupling relationships find out all the weaknesses in fire safety management to reduce the potential financial losses and deaths resulting from fires. Regarding the risk assessment, this approach provides a meaningful way to assign weights based on a risk indexing system. This work also points out that people should consider the cause and effect relationships in the risk indexing system. Such relationships usually exist in the risk assessment and the real world. However, the relationships are usually ignored, and some factors leading to potential problems are not rectified after the risk assessment. After some time, those factors will cause other factors to become significant problems as well. Therefore, the coupling relationship should be considered in risk assessment to solve the above problem. The novelty of our paper is that to the best of the authors’ knowledge, the proposed approach is the first to originally utilize both the FAHP method and the coupling relationships in the fire-risk indexing system. Exploiting the coupling relationships in the fire-risk indexing system is our original scientific concept. Based on this original concept, the coupling revision method is proposed to revise the scores to make fire risk assessments much more scientific and reasonable. Moreover, the feedback of this approach can find not only the weaknesses in fire risk management, but also the potential causes of fires. Thus, it can help the safety chiefs and inspectors to improve the fire risk management of buildings.The rest of the paper is organized as follows. We introduce the related works in Fire risk assessments of buildings are essential for fire prevention and control, and scholars have made significant efforts in this direction. In the literature, one of the most fundamental and commonly used approaches is fire risk indexing (FRI) ,18. ThisBesides the analysis of indexing systems, other studies considering weight assignment and score determination in FRI are also essential. In order to assign weights in the FRI system, numerous MCDA methods can be applied. For example, AHP  is a kinIn our application, we chose to use the FAHP method, which is the fuzzy extension of the AHP method and integrates the following advantages of the AHP: (1) the AHP is a widely used MCDA method in many applications and problems according to the survey results in ; (2) psyuildings ,40.After assigning the weights, the computation of scores in the assessment with FRI should be studied. Reference  introducApart from FRI, there are also attempts at risk assessments using other methods. For example,  applied Considering all the methods discussed above, FRI is still the most important and intuitive one for the fire risk assessment. It can not only predict the fire risk but also provide practical instructions for risk reduction, since the method is derived from practical fire risk factors. Therefore, our approach concentrates on improving the weight assignments and exploiting coupling relationships among the factors in the FRI method, thereby bettering the assessment scheme.In this section, we introduce our methodology in the following steps: (1) we first establish a fire-risk indexing system, based on which we can assess the management performance; (2) with the fire-risk indexing system designed, we provide a weight for each factor using the FAHP method; (3) according to the coupling relationships among the fire risk factors, we integrate those coupling relationships into the fire-risk indexing system and revise the score of each factor by utilizing the quantified coupling extent. The second and third steps are the major contents of the proposed fire risk assessment approach. The overall framework of our methodology for fire risk assessments is shown in Building legitimacy: The building should have legitimate records in the corresponding administrative office.Fire safety regulation: The safety chief of the building should construct complete regulations for fire safety management.Fire safety operation standard: The implementation and operation of all the regulations and facilities should be standardized.Fire prevention patrol: The property management office should arrange a team to exercise a daily fire prevention patrol and set up a patrol record.Publicity and education: There should be posters in conspicuous places emphasizing basic fire safety knowledge.Power supply and distribution: The power supply and distribution should work normally during emergencies.Automatic fire alarm system: The building should be equipped with automatic smoke detectors and fire alarm systems.Automatic water spraying system: The building should be equipped with automatic water spraying fire extinguishing systems.Smoke exhaust system: The building should have a suitable smoke exhaust design and facilities.Fire separation facility: The building should have fire separation facilities including fireproof rolling shutter doors and fire compartmentation areas.Fire elevator: The building should be equipped with enough fire elevators.Emergency lighting: Emergency fire lights should keep working during power failures.Emergency broadcast: The building should be equipped with emergency broadcast facilities.Water supply facility: Water supply is essential for fire extinguishing and should be checked regularly.Self-rectification: All the defects found during inspections should be rectified within a reasonable timeframe.Firefighting ability: Firefighting ability contains the following aspects: the building should have adequate fire distinguishing facilities; there should be several teams that know basic firefighting operations; and the evacuation drills should be organized regularly.Electricity and gas safety: The use of electricity and gas should be checked regularly to ensure safety.Evacuation facilities: The evacuation facilities should not be blocked.To conduct a fire risk assessment, we must have a fair and comprehensive indexing system. In this system, all factors are fully relative to the features of large-scale commercial and high-rise buildings based on the national standards on fire safety management, the maintenance reports from third parties and the annual fire management assessment reports. After investigating the above contents, we created a fire-risk indexing system with a tree structure, as shown in Those 18 factors can be summarized into 4 categories: fire control management, facility maintenance, firefighting and rescue, and potential risk inspection. The fire-risk indexing system in our method is listed in In this subsection, we introduce a proper method of weight distribution among the categories and factors via the FAHP. To that end, we first designed a questionnaire for the categories and factors in our fire-risk indexing system in order to collect experts’ knowledge on the importance of each category and factor. Then, based on the questionnaire results from multiple experts, we generated a tuple for each category/factor to represent the overall opinion of all the experts and obtain the fuzzy matrix. Finally, based on the fuzzy matrix, we created the weight distribution for our fire-risk indexing system. The corresponding details will be introduced as follows.In this part, according to , we showM categories  and can be denoted as Here, the function M by the membership function x in X that belongs to the fuzzy set M. The membership function In fuzzy set theory , such TF follows ,40,46.,,6), the ith category  data as the inputs to our evaluation system. The reasons for combining IoT data are that: On the one hand, the inputs to the current evaluation system are the monthly maintenance reports and the annual fire management reports from third parties; thus the assessment frequency is rather low. Moreover, the textual documents may have erroneous and even fake data, which might cause trouble during the data processing. On the other hand, firefighting nowadays relies more than ever on the stable and timely data, making manual data collection impossible. The firefighting IoT technology ,50 can oIn this article, a novel fire risk assessment approach was proposed for large-scale commercial and high-rise buildings. We first designed a fire-risk indexing system based on the Chinese national documents about fire control and management. Then based on this system, we used the FAHP method to integrate all the experts’ knowledge on the importance of each category and factor and distributed weights for the fire risk assessment. In addition, our approach analyzes the coupling relationships among factors in the system and makes necessary revisions for scores of the factors, if necessary. Finally, to validate our assessment approach, we chose 11 large-scale commercial and high-rise buildings in Weifang, Shandong Province, for a case study. The results show that the proposed approach can fairly conduct fire risk assessments. We also interviewed those 11 buildings’ chiefs in regard to the corresponding fire risk assessment results to verify the assessment quality, and we discussed the possibility of generalizing the proposed approach for other kinds of buildings. More importantly, the proposed approach provides both safety chiefs and inspectors with an easy and meaningful way to improve fire risk management. In the future, we will devote our efforts to further integrating the data from IoT systems into our approach to improve fire risk management.


The existing dogma that the former term ovarian "germinal epithelium" resulted from a mistaken belief that it could give rise to new germ cells is now strongly challenged.Two years ago, a research group of the University of Tennessee led by Antonin Bukovsky successfully demonstrated the oogenic process from the human ovarian covering epithelium now commonly called the ovarian surface epithelium. They showed the new oocyte with zona pellucida and granulosa cells, both originated from the surface epithelium arising from mesenchymal cells in the tunica albuginea, and stressed that the human ovary could form primary follicles throughout the reproductive period. This gives a big impact not only to the field of reproductive medicine, but also to the oncologic area. The surface epithelium is regarded as the major source of ovarian cancers, and most of the neoplasms exhibit the histology resembling müllerian epithelia. Since the differentiating capability of the surface epithelium has now expanded, the histologic range of the neoplasms in this category may extend to include both germ cell tumors and sex cord-stromal cell tumors.Since the oogenic capability of ovarian surface cells has been proven, it is now believed that the oocytes can originate from them. The term "germinal epithelium", hence, might reasonably be reinstated. Unlike the avian ovary, where the follicles containing a large yolky egg look like a cluster of yellowish grapes, the human ovary is solid in structure, amygdaloid in shape, grayish white in color, and the follicles are usually invisible through the surface. Since the intraovarian follicle was firstly identified by Reinier De Graaf in 1672 , or (2) A study of the histogenesis of ovarian components is essential to understand the oncogenesis of ovarian neoplasms. Since the ovarian covering tissue has now been revealed to have oogenic capability, the surface epithelium might be accountable as a source of germ cell tumors and sex cord-stromal cell tumors, as well as neoplasms exhibiting the müllerian histology.The nature of covering epithelium of the ovary is intriguing. Although only a part of peritoneal mesothelium, it proliferates to repair the minor trauma due to the ovulation, and thus has occasionally tumorigenic potential. The concept that the epithelium simulates the müllerian form in tumor formation has developed by degrees and now becomes a firm policy in the classification of ovarian cancer . AlthougIn our previous experimental study, in which a chemical carcinogen, 7,12-dimethylbenz [a]anthracene (DMBA), was directly applied to the rat ovarian surface, an ovarian cancer was observed in about half of the DMBA-treated rats. The histology of the induced tumors also simulated the epithelia of rat genital tracts. In the experiment, however, an unexplainable cancer composed of heterologous osteoid tissue was observed . At thatFrom the capability of ovarian covering tissue to produce oocytes, the term "germinal epithelium" might be reinstated.