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Motion prediction based on kinematic information such as body segment displacement and joint angle has been widely studied. Because motions originate from forces, it is beneficial to estimate dynamic information, such as the ground reaction force (GRF), in addition to kinematic information for advanced motion prediction. In this study, we proposed a method to estimate GRF and ground reaction moment (GRM) from electromyography (EMG) in combination with and without an inertial measurement unit (IMU) sensor using a machine learning technique. A long short-term memory network, which is suitable for processing long time-span data, was constructed with EMG and IMU as input data to estimate GRF during posture control and stepping motion. The results demonstrate that the proposed method can provide the GRF estimation with a root mean square error (RMSE) of 8.22 ± 0.97% (mean ± SE) for the posture control motion and 11.17 ± 2.16% (mean ± SE) for the stepping motion. We could confirm that EMG input is essential especially when we need to predict both GRF and GRM with limited numbers of sensors attached under knees. In addition, we developed a GRF visualization system integrated with ongoing motion in a Unity environment. This system enabled the visualization of the GRF vector in 3-dimensional space and provides predictive motion direction based on the estimated GRF, which can be useful for human motion prediction with portable sensors.

Multiple tasks are simultaneously performed during walking in our daily life. Distracted walk by smartphone usage is recently getting a social problem. The term dual-task gait refers to the secondary task added to the walking. Attention demanding tasks may influence how a person walks. Since in-lab measurement may not accurately reflect the daily living gait, wearable sensors approach have been proposed for gait analysis in an out-of-lab setting. This study addresses the potential of using only two inertial measurement units (IMUs) attached to the shoes for the assessment of cognitive dual-task gait and how it differs from single-task gait. We found that the proposed system is sensitive to recognizing a tiny change in gait features such as on the double support time and gait indices when subject performing dual-task gait compared to the single-task gait experiment.

In this study, we proposed a framework for extracting gait events and extensive temporal features, seamlessly, during walking and running on a treadmill by constructing a finite state machine (FSM) transition rules based on two IMU sensors attached to the back of the shoes. Detailed innerclass states were defined to recognize the double support phase on walking gait and the double flight phase on running gait. Further, an in-depth speed-based analysis of temporal gait features can be performed for each tested speed with an automatic speed change detection algorithm based on the moving average filter applied to motion intensity data. The results have demonstrated that the FSM can accurately distinguish walking gait and running gait while also extract a detailed gait phase, respectively. This finding may contribute to a more flexible gait analysis where a change in speed or transition from walk to run can be anticipated and recognized accordingly.

In this study, we investigated a control algorithm for a semi-active prosthetic knee based on reinforcement learning (RL). Model-free reinforcement Q-learning control with a reward shaping function was proposed as the voltage controller of a magnetorheological damper based on the prosthetic knee. The reward function was designed as a function of the performance index that accounts for the trajectory of the subject-specific knee angle. We compared our proposed reward function to a conventional single reward function under the same random initialization of a Q -matrix. We trained this control algorithm to adapt to several walking speed datasets under one control policy and subsequently compared its performance with that of other control algorithms. The results showed that our proposed reward function performed better than the conventional single reward function in terms of the normalized root mean squared error and also showed a faster convergence trend. Furthermore, our control strategy converged within our desired performance index and could adapt to several walking speeds. Our proposed control structure has also an overall better performance compared to user-adaptive control, while some of its walking speeds performed better than the neural network predictive control from existing studies., Competing Interests: 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., (Copyright © 2020 Hutabarat, Ekkachai, Hayashibe and Kongprawechnon.)

Gait is one of the most extensively studied motor tasks using motion capture systems, the gold standard for instrumental gait analysis. Various sensor-based solutions have been recently proposed to evaluate gait parameters, typically providing lower accuracy but greater flexibility. Validation procedures are crucial to assess the measurement accuracy of these solutions since residual errors may arise from environmental, methodological, or processing factors. This study aims to enhance validation by employing machine learning techniques to investigate the impact of such errors on the overall assessment of gait profiles. Two datasets of gait trials, collected from healthy and post-stroke subjects using a motion capture system and a 3D camera-based system, were considered. The estimated gait profiles include spatiotemporal, asymmetry, and body center of mass parameters to capture various normal and pathologic gait peculiarities. Machine learning models show the equivalence and the high level of agreement and concordance between the measurement systems in assessing gait profiles (accuracy: 98.7%). In addition, they demonstrate data interchangeability and integrability despite residual errors identified by traditional statistical metrics. These findings suggest that validation procedures can extend beyond strict measurement differences to comprehensively assess gait performance. [ABSTRACT FROM AUTHOR]

The volitional control of powered assistive devices is commonly performed by mapping the electromyographic (EMG) activity of the lower limb to joints' angular kinematics, which are then used as the input for regulation. However, during walking, the ground reaction force (GRF) plays a central role in the modulation of the gait, providing dynamic stability and propulsion during the stance phase. Including this information within the control loop of prosthetic devices can improve the quality of the final output, providing more physiological walking dynamics that enhances the usability and patient comfort. In this work, we explored the feasibility of the estimation of the ground reaction force vertical component (VGRF) by using only the EMG activities of the thigh and shank muscles. We compared two deep learning models in three experiments that involved different muscular configurations. Overall, the outcomes show that the EMG signals could be leveraged to obtain a reliable estimation of the VGRF during walking, and the shank muscles alone represent a viable solution if a reduced recording setup is needed. On the other hand, the thigh muscles failed in providing performance enhancements, either when used alone or together with the shank muscles. The results outline the feasibility of including GRF information within an EMG-driven control scheme for prosthetic and assistive devices. [ABSTRACT FROM AUTHOR]

Background: Gait variables assessed by inertial measurement units (IMUs) show promise as screening tools for aging-related diseases like sarcopenia. The main aims of this systematic review were to analyze and synthesize the scientific evidence for screening sarcopenia based on gait variables assessed by IMUs, and also to review articles that investigated which gait variables assessed by IMUs were related to sarcopenia. Methods: Six electronic databases (PubMed, SportDiscus, Web of Science, Cochrane Library, Scopus and IEEE Xplore) were searched for journal articles related to gait, IMUs and sarcopenia. The search was conducted until December 5, 2023. Titles, abstracts and full-length texts for studies were screened to be included. Results: A total of seven articles were finally included in this review. Despite some methodological variability among the included studies, IMUs demonstrated potential as effective tools for detecting sarcopenia when coupled with artificial intelligence (AI) models, which outperformed traditional statistical methods in classification accuracy. The findings suggest that gait variables related to the stance phase such as stance duration, double support time, and variations between feet, are key indicators of sarcopenia. Conclusions: IMUs could be useful tools for sarcopenia screening based on gait analysis, specifically when artificial intelligence is used to process the recorded data. However, more development and research in this field is needed to provide an effective screening tool for doctors and health systems. [ABSTRACT FROM AUTHOR]

The joint moment is a key measurement in locomotion analysis. Transferable prediction across different subjects is advantageous for calibration-free, practical clinical applications. However, even for similar gait motions, intersubject variance presents a significant challenge in maintaining reliable prediction performance. The optimal deep learning models for ankle moment prediction during dynamic gait motions remain underexplored for both intrasubject and intersubject usage. This study evaluates the feasibility of different deep-learning models for estimating ankle moments using sEMG data to find an optimal intrasubject model against the inverse dynamic approach. We verified and compared the performance of 1302 intrasubject models per subject on 597 steps from seven subjects using various architectures and feature sets. The best-performing intrasubject models were recurrent convolutional neural networks trained using signal energy features. They were then transferred to realize intersubject ankle moment estimation. [ABSTRACT FROM AUTHOR]

Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnormal gait classification algorithm considering joint impairments using inertial measurement units (IMUs) and walkway systems. Ten healthy male participants simulated normal walking, walking with knee impairment, and walking with ankle impairment under three conditions: without joint braces, with a knee brace, and with an ankle brace. Based on these simulated gaits, we developed classification models: distinguishing abnormal gait due to joint impairments, identifying specific joint disorders, and a combined model for both tasks. Recursive Feature Elimination with Cross-Validation (RFECV) was used for feature extraction, and models were fine-tuned using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB). The IMU-based system achieved over 91% accuracy in classifying the three types of gait. In contrast, the walkway system achieved less than 77% accuracy in classifying the three types of gait, primarily due to high misclassification rates between knee and ankle joint impairments. The IMU-based system shows promise for accurate gait assessment in patients with joint impairments, suggesting future research for clinical application improvements in rehabilitation and patient management. [ABSTRACT FROM AUTHOR]

Ground reaction force (GRF) data is often collected for the biomechanical analysis of running, due to the performance and injury risk insights that GRF analysis can provide. Traditional methods typically limit GRF collection to controlled lab environments, recent studies have looked to combine the ease of use of wearable sensors with the statistical power of machine learning to estimate continuous GRF data outside of these restrictions. Before such systems can be deployed with confidence outside of the lab they must be shown to be a valid and accurate tool for a wide range of users. The aim of this study was to evaluate how accurately a consumer-priced sensor system could estimate GRFs whilst a heterogeneous group of runners completed a treadmill protocol with three different personalised running speeds and three gradients. Fifty runners (25 female, 25 male) wearing pressure insoles made up of 16 resistive sensors and an inertial measurement unit ran at various speeds and gradients on an instrumented treadmill. A long short term memory (LSTM) neural network was trained to estimate both vertical $(GRF_v)$ (G R F v) and anteroposterior $(GRF_{ap})$ (G R F a p) force traces using leave one subject out validation. The average relative root mean squared error (rRMSE) was 3.2% and 3.1%, respectively. The mean $(GRF_v)$ (G R F v) rRMSE across the evaluated participants ranged from 0.8% to 8.8% and from 1.3% to 17.3% in the $(GRF_{ap})$ (G R F a p) estimation. The findings from this study suggest that current consumer-priced sensors could be used to accurately estimate two-dimensional GRFs for a wide range of runners at a variety of running intensities. The estimated kinetics could be used to provide runners with individualised feedback as well as form the basis of data collection for running injury risk factor studies on a much larger scale than is currently possible with lab based methods. [ABSTRACT FROM AUTHOR]

Purpose: To build an Mult-Task Learning (MTL) based Artificial Intelligence(AI) model that can simultaneously predict clinical stage, histology, grade and LNM for cervical cancer before surgery. Methods: This retrospective and prospective cohort study was conducted from January 2001 to March 2014 for the training set and from January 2018 to November 2021 for the validation set at Beijing Chaoyang Hospital, Capital Medical University. Preoperative clinical information of cervical cancer patients was used. An Artificial Neural Network (ANN) algorithm was used to build the MTL-based AI model. Accuracy and weighted F1 scores were calculated as evaluation indicators. The performance of the MTL model was compared with Single-Task Learning (STL) models. Additionally, a Turing test was performed by 20 gynecologists and compared with this AI model. Results: A total of 223 cervical cancer cases were retrospectively enrolled into the training set, and 58 cases were prospectively collected as independent validation set. The accuracy of this cervical cancer AI model constructed with ANN algorithm in predicting stage, histology, grade and LNM were 75%, 95%, 86% and 76%, respectively. And the corresponding weighted F1 score were 70%, 94%, 86%, and 76%, respectively. The average time consumption of AI simultaneously predicting stage, histology, grade and LNM for cervical cancer was 0.01s (95%CI: 0.01–0.01) per 20 patients. The mean time consumption doctor and doctor with AI were 581.1s (95%CI: 300.0-900.0) per 20 patients and 534.8s (95%CI: 255.0-720.0) per 20 patients, respectively. Except for LNM, both the accuracy and F-score of the AI model were significantly better than STL AI, doctors and AI-assisted doctors in predicting stage, grade and histology. (P < 0.05) The time consumption of AI was significantly less than that of doctors' prediction and AI-assisted doctors' results. (P < 0.05 Conclusion: A multi-task learning AI model can simultaneously predict stage, histology, grade, and LNM for cervical cancer preoperatively with minimal time consumption. To improve the conditions and use of the beneficiaries, the model should be integrated into routine clinical workflows, offering a decision-support tool for gynecologists. Future studies should focus on refining the model for broader clinical applications, increasing the diversity of the training datasets, and enhancing its adaptability to various clinical settings. Additionally, continuous feedback from clinical practice should be incorporated to ensure the model's accuracy and reliability, ultimately improving personalized patient care and treatment outcomes. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) is a non-invasive bioelectrical signal used to capture changes in muscle activity during exercise. Because it is closely related to sports, it is widely used in the research and development process of intelligent assisted rehabilitation equipment to provide support and assistance for rehabilitation patients. Rehabilitation training involves complex three-dimensional motion, and sEMG-based joint continuous motion estimation is a method to estimate joint angle or moment by analyzing sEMG during exercise, which can effectively alleviate the problem of insufficient adaptability between rehabilitation machine and human body, providing safer assistance and significantly improving the rehabilitation effect. This paper firstly introduces the current status of joint continuous motion estimation, and then classifies the existing sEMG joint continuous motion estimation models into biomechanics-based musculoskeletal model and machine learning-based regression model according to different research methods, and summarizes and analyzes the relevant models respectively. In addition, the paper also analyzes the current challenges and looks forward to the future research trends. [ABSTRACT FROM AUTHOR]

Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson's disease. During a FOG episode, patients report that their feet are suddenly and inexplicably "glued" to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment. To address this problem, we organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams from 83 countries submitted 24,862 solutions. The winning solutions demonstrated high accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occurrences. This successful endeavor underscores the potential of machine learning contests to rapidly engage AI experts in addressing critical medical challenges and provides a promising means for objective FOG quantification. The absence of an objective way of assessing freezing of gait in Parkinson's Disease hinders research and care. A machine-learning contest using wearable sensor data delivered detection algorithms with high precision and identified time-of-day effects. [ABSTRACT FROM AUTHOR]

The biomechanical-model-based approach with a contact model offers advantages in estimating ground reaction forces (GRFs) and ground reaction moments (GRMs), as it does not rely on the need for training data and gait assumptions. However, this approach faces the challenge of long computational times due to the inclusion of optimization processes. To address this challenge, the present study developed a new optical motion capture (OMC)-based method to estimate GRFs, GRMs, and joint torques without prolonged computational times. The proposed approach performs the estimation process by distributing external forces, as determined by a multibody model, between the left and right feet based on foot deformations, thereby predicting the GRFs and GRMs without relying on optimization techniques. In this study, prediction accuracies during level walking were confirmed by comparing a general analysis using a force plate with the estimation results. The comparison revealed excellent or strong correlations between the prediction and the measurements for all GRFs, GRMs, and lower-limb-joint torques. The proposed method, which provides practical estimation with low computational cost, facilitates efficient biomechanical analysis and rapid feedback of analysis results, contributing to its increased applicability in clinical settings. [ABSTRACT FROM AUTHOR]

Magnetorheological fluids (MRF) are intelligent materials that can vary their yield stress in response to an applied magnetic field. This characteristic, combined with active and multifunctional control, allows the development of actuators with fast response time, low energy consumption, long service life, and reduced dimensions and weights. Various studies have been conducted to improve MR dampers in prosthetic applications, including knees, ankle-foot, hands, and sockets. Here, we present a critical review of the progress of MRFs in the prosthetic field. In addition, research in prostheses' design, optimization, and control of magnetorheological actuators is investigated, along with MRF modeling, mode of operation, type of MR actuator, classification, and working principle of MRF-based devices. Although MRFs are considered promising materials for designing novel prosthetic devices, this review shows that applications have been predominantly focused on lower limb prostheses. We conclude by discussing possible future applications and challenges that must be faced to enable and improve commercial applications based on MRF technology. [ABSTRACT FROM AUTHOR]

Gait quality is of significant interest in studies investigating interventions for individuals with gait pathology. A primary challenge in gait analysis is selecting an appropriate metric which adequately reflects aberrant deviations and provides an assessment of gait quality in individuals with gait pathology. Metrics vary in definition of gait quality, and there is lack of consensus on an objective outcome measure for assessing gait; while three-dimensional gait analysis is the gold standard, metrics operationalizable in the form of wearable sensors would provide important information to clinicians and researchers without requiring a laboratory. In this study, we investigated and compared the ability of four metrics to detect aberrant gait through systematically applied joint constraint: Prosthetic Observational Gait Score (POGS), Impulse Asymmetry, Lateral Sway, and Gait Deviation Index (GDI). We analyze these metrics to understand their sensitivity and ability to detect systematic perturbed gait with an eye toward future operationalization in the form of a wearable sensor suite. We systematically applied four unilateral lower limb joint constraint conditions to nine able-bodied participants walking at three speeds creating four distinct gait patterns with variations from the baseline. Notably, POGS and GDI together distinguished five of six joint constraint comparisons, with each metric able to distinguish four joint constraint comparisons. Lateral Sway distinguished three joint constraint conditions and two speed conditions, while Impulse Asymmetry distinguished three constraint conditions. No single metric distinguished every condition. A single metric may be adequate for assessing specific gait features; however, multiple metrics are recommended for comprehensive assessment of pathological gait. [ABSTRACT FROM AUTHOR]

In this paper, we present a method of gait analysis on a treadmill based on pressure distribution. We aimed to model the gait patterns of a subject walking at a constant speed on a treadmill based on differences in current consumption. The changes in current consumption were converted into pressure distribution curves, and then specific features were extracted. The extracted features were used to model the walking pattern on a treadmill. To verify the validity of our proposed feature-based gait pattern modeling, we conducted experiments by gender, age, BMI (body mass index), and step-to-step symmetry. The experimental results showed that the heavier the subject, the higher the value of each feature. In particular, our feature point-based gait modeling provides an index that can help determine whether a subject's gait is abnormal, depending on the difference between the features. [ABSTRACT FROM AUTHOR]

Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson's disease. We previously designed a rehabilitation assist device that can detect and classify a user's gait at only the swing phase of the gait cycle, for the ease of data processing. In this study, we analyzed the impact of various factors in a gait detection algorithm on the gait detection and classification rate (GDCR). We collected acceleration and angular velocity data from 25 participants (1 male and 24 females with an average age of 62 ± 6 years) using our device and analyzed the data using statistical methods. Based on these results, we developed an adaptive GDCR control algorithm using several equations and functions. We tested the algorithm under various virtual exercise scenarios using two control methods, based on acceleration and angular velocity, and found that the acceleration threshold was more effective in controlling the GDCR (average Spearman correlation −0.9996, p < 0.001) than the gyroscopic threshold. Our adaptive control algorithm was more effective in maintaining the target GDCR than the other algorithms (p < 0.001) with an average error of 0.10, while other tested methods showed average errors of 0.16 and 0.28. This algorithm has good scalability and can be adapted for future gait detection and classification applications. [ABSTRACT FROM AUTHOR]

In a society centered on hyper-connectivity, information sharing is crucial, but it must be ensured that each piece of information is viewed only by legitimate users; for this purpose, the medium that connects information and users must be able to identify illegal users. In this paper, we propose a smartphone authentication system based on human gait, breaking away from the traditional authentication method of using the smartphone as the medium. After learning human gait features with a convolutional neural network deep learning model, it is mounted on a smartphone to determine whether the user is a legitimate user by walking for 1.8 s while carrying the smartphone. The accuracy, precision, recall, and F1-score were measured as evaluation indicators of the proposed model. These measures all achieved an average of at least 90%. The analysis results show that the proposed system has high reliability. Therefore, this study demonstrates the possibility of using human gait as a new user authentication method. In addition, compared to our previous studies, the gait data collection time for user authentication of the proposed model was reduced from 7 to 1.8 s. This reduction signifies an approximately four-fold performance enhancement through the implementation of filtering techniques and confirms that gait data collected over a short period of time can be used for user authentication. [ABSTRACT FROM AUTHOR]

Background: Public Gas Station (SPBU) is a public infrastructure to provide the fuel needs of the wider community. The high demand for fuel in Indonesia leads to demanding activities and potentially causes work fatigue to the workforce, especially operators. The study aims to analyze the correlation between working period, noise, workload, and work fatigue among the operators of the Public Gas Station in Kupang, East Nusa Tenggara. Subjects and Method: A cross-sectional study was conducted in Kupang, East Nusa Tenggara from October-November 2022. A total of 68 subjects consisted of 30 operators of 16 hours Public Gas Station; 38 operators of 24 hours Public Gas Station. The dependent variable was work fatigue. The independent variables were working period, noise, and workload. The data were collected using a questionnaire and analyzed using Spearman Correlation (rho). Results: Working period indicated a strong negative correlation with work fatigue, and was statistically significant (r= -0.74; p< 0.001). Noise revealed a weak negative correlation with work fatigue at gas station operators (r= -0.25; p= 0.837), while workloads showed a strong positive correlation with work fatigue at gas station operators, and the result was statistically significant (r= 0.850; p< 0.001). Conclusion: Working period, noise, and workload have a correlation with work fatigue among public gas station operators. [ABSTRACT FROM AUTHOR]

Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate ( rRMSE 0 : 6.3%) and predict 200-ms-ahead ( rRMSE 200 ms : 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices. [ABSTRACT FROM AUTHOR]

Basic human ambulation relies on a bipedal gait, which has been reported to be directly related to quality of life. However, injuries to the lower limb can cause an inability to walk and require non-weightbearing periods to heal. Among the many ambulatory aids, standard axillary crutches are prescribed. However, due to the disadvantages of having to use both hands, a slow gait, pain, nerve damage, and gait patterns that differ from that of healthy subjects, currently, a new generation of ambulatory aids has emerged. Among such aids, hands-free crutches (HFCs) are of particular interest due to their form factor, which does not require the use of the hands and facilitates a bipedal gait. In this study, we present an assessment of whether any different gait patterns, compared to overground gait, appeared on the unaffected limb during walking with an HFC. The spatiotemporal parameters, plantar force, lower-limb joint angles, and EMG patterns were evaluated. In conclusion, the results from 10 healthy subjects suggest that wearing an HFC causes only slight changes in the biomechanical gait patterns examined in the unaffected limb compared with overground walking without an HFC. [ABSTRACT FROM AUTHOR]

One of the fundamental limitations in human biomechanics is that we cannot directly obtain joint moments during natural movements without affecting the motion. However, estimating these values is feasible with inverse dynamics computation by employing external force plates, which can cover only a small area of the plate. This work investigated the Long Short-Term Memory (LSTM) network for the kinetics and kinematics prediction of human lower limbs when performing different activities without using force plates after the learning. We measured surface electromyography (sEMG) signals from 14 lower extremities muscles to generate a 112-dimensional input vector from three sets of features: root mean square, mean absolute value, and sixth-order autoregressive model coefficient parameters for each muscle in the LSTM network. With the recorded experimental data from the motion capture system and the force plates, human motions were reconstructed in a biomechanical simulation created using OpenSim v4.1, from which the joint kinematics and kinetics from left and right knees and ankles were retrieved to serve as output for training the LSTM. The estimation results using the LSTM model deviated from labels with average R2 scores (knee angle: 97.25%, knee moment: 94.9%, ankle angle: 91.44%, and ankle moment: 85.44%). These results demonstrate the feasibility of the joint angle and moment estimation based solely on sEMG signals for multiple daily activities without requiring force plates and a motion capture system once the LSTM model is trained. [ABSTRACT FROM AUTHOR]

Gait analysis refers to the systematic study of human locomotion and finds numerous applications in the fields of clinical monitoring, rehabilitation, sports science and robotics. Wearable sensors for real-time gait monitoring have emerged as an attractive alternative to the traditional clinical-based techniques, owing to their low cost and portability. In addition, 3D printing technology has recently drawn increased interest for the manufacturing of sensors, considering the advantages of diminished fabrication cost and time. In this study, we report the development of a 3D-printed capacitive smart insole for the measurement of plantar pressure. Initially, a novel 3D-printed capacitive pressure sensor was fabricated and its sensing performance was evaluated. The sensor exhibited a sensitivity of 1.19   MP a − 1 , a wide working pressure range (< 872.4   kPa) , excellent stability and durability (at least 2.280 cycles), great linearity ( R 2 = 0.993 ), fast response/recovery time (142 – 160   ms) , low hysteresis (D H < 10 %) and the ability to support a broad spectrum of gait speeds (30 – 70 steps/min). Subsequently, 16 pressure sensors were integrated into a 3D-printed smart insole that was successfully applied for dynamic plantar pressure mapping and proven able to distinguish the various gait phases. We consider that the smart insole presented here is a simple, easy to manufacture and cost-effective solution with the potential for real-world applications. [ABSTRACT FROM AUTHOR]

Stroke is the second leading cause of death and one of the leading causes of disability in the world. According to the World Health Organization, 11 million people suffer a stroke yearly. The cost of the disease is exorbitant, and the most widely used treatment is conventional physiotherapy. Therefore, assistive technology emerges to optimize rehabilitation and functional capabilities, but cost, robustness, usability, and long-term results still restrict the technology selection. This work aimed to develop a low-cost ankle orthosis, the G-Exos, a wearable exoskeleton to increase motor capability by assisting dorsiflexion, plantarflexion, and ankle stability. A hybrid system provided near-natural gait movements using active, motor, and passive assistance, elastic band. The system was validated with 10 volunteers with foot drop: seven with stroke, two with incomplete spinal cord injury (SCI), and one with acute inflammatory transverse myelitis (ATM). The G-Exos showed assistive functionality for gait movement. A Friedman test showed a significant difference in dorsiflexion amplitude with the use of the G-Exos compared to gait without the use of the G-Exos [x²(3) = 98.56, p < 0.001]. In addition, there was also a significant difference in ankle eversion and inversion comparing walking with and without the G-Exos [x²(3) = 36.12, p < 0.001]. The G-Exos is a robust, lightweight, and flexible assistive technology device to detect the gait phase accurately and provide better human-machine interaction. G-Exos training improved capability to deal with gait disorders, usability, and motor and functional recovery. Wearable assistive technologies lead to a better quality of life and contribute using in activities of daily living. [ABSTRACT FROM AUTHOR]

Background: Use of a wearable gait analysis system (WGAS) is becoming common when conducting gait analysis studies due to its versatility. At the same time, its versatility raises a concern about its accuracy, because its calculations rely on assumptions embedded in its algorithms. The purpose of the present study was to validate twenty spatiotemporal gait parameters calculated by the WGAS by comparison with simultaneous measurements taken with an optical motion capture system (OMCS). Methods: Ten young healthy volunteers wore two inertial sensors of the commercially available WGAS, Physilog®, on their feet and 23 markers for the OMCS on the lower part of the body. The participants performed at least three sets of 10-m walk tests at their self-paced speed in the laboratory equipped with 12 high-speed digital cameras with embedded force plates. To measure repeatability, all participants returned for a second day of testing within two weeks. Results: Twenty gait parameters calculated by the WGAS had a significant correlation with the ones determined by the OMCS. Bland and Altman analysis showed that the between-device agreement for twenty gait parameters was within clinically acceptable limits. The validity of the gait parameters generated by the WGAS was found to be excellent except for two parameters, swing width and maximal heel clearance. The repeatability of the WGAS was excellent when measured between sessions. Conclusion: The present study showed that spatiotemporal gait parameters estimated by the WGAS were reasonably accurate and repeatable in healthy young adults, providing a scientific basis for applying this system to clinical studies. [ABSTRACT FROM AUTHOR]

We reviewed the current methodology and practices of the DNA metabarcoding approach using a universal PCR primer pair MiFish, which co-amplifies a short fragment of fish DNA (approx. 170 bp from the mitochondrial 12S rRNA gene) across a wide variety of taxa. This method has mostly been applied to biodiversity monitoring using environmental DNA (eDNA) shed from fish and, coupled with next-generation sequencing technologies, has enabled massively parallel sequencing of several hundred eDNA samples simultaneously. Since the publication of its technical outline in 2015, this method has been widely used in various aquatic environments in and around the six continents, and MiFish primers have demonstrably outperformed other competing primers. Here, we outline the technical progress in this method over the last 5 years and highlight some case studies on marine, freshwater, and estuarine fish communities. Additionally, we discuss various applications of MiFish metabarcoding to non-fish organisms, single-species detection systems, quantitative biodiversity monitoring, and bulk DNA samples other than eDNA. By recognizing the MiFish eDNA metabarcoding strengths and limitations, we argue that this method is useful for ecosystem conservation strategies and the sustainable use of fishery resources in "ecosystem-based fishery management" through continuous biodiversity monitoring at multiple sites. [ABSTRACT FROM AUTHOR]

Blueberries are rich in bioactive anthocyanins, which are associated with health benefits contributing to reducing the risk of diabetes and cardiovascular diseases. The objective of this study was to improve the yield of anthocyanins extracted from rabbiteye blueberry (Vaccinium ashei) fruits cultivated in Nanjing using ultrasound-assisted extraction and to identify the individual anthocyanins present in the extract. The extraction conditions of blueberry anthocyanins were optimized using response surface methodology. The Box–Behnken test was designed to investigate the effect of extraction using different ethanol concentrations, extraction time, and liquid-to-solid ratios. The optimum conditions of the extraction derived from the model were as follows: extraction time 24 h at 30°C using 72.50% ethanol which contains 0.02% v/v hydrochloric acid as a solvent and liquid-to-solid ratio 20 : 1 v/w. The extraction yield was 16.21 ± 0.44 mg/g under these optimum conditions. The 13 peaks of the anthocyanin extract from rabbiteye blueberry fruits in Nanjing were tentatively identified using high-performance liquid chromatography (HPLC) and high-performance liquid chromatography-electrospray ionization interface-mass spectrometer (HPLC-ESI-MS), which are the derivatives of delphinidin, cyanidin, petunidin, peonidin, and malvidin that were glycosylated by glucose, galactose, or arabinose. This research provides a reliable scientific basis for efficacious extraction and identification of anthocyanins from blueberry fruits, which would be helpful for further investigation of the function and application of blueberry anthocyanins extract to human health. [ABSTRACT FROM AUTHOR]

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The Indonesian dairy sector remains highly regulated, in spite of trade and investment policy reforms elsewhere in the economy. The regulations, aimed mainly at fostering development of local industry, include an import ratio requirement (BUSEP), import tariffs; an import licensing scheme, and restrictions on investment in milk processing The paper shows that the current policy mix has resulted in a significant increase in numbers of dairy cattle and dairy farmers, and in fresh milk and end-product output. However, this policy mix imposes costs. The analysis indicates that the economy, and in particular domestic consumers, would gain if dairy trade and investment restrictions were lessened. The gradual movement towards a more deregulated dairy sector is a necessary condition for improved efficiency and a strengthening of Indonesia's competitive position in the world dairy market. [ABSTRACT FROM PUBLISHER]