Hand gesture recognition systems (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) have been studied for different applications in recent years. Most commonly, cutting-edge HGR methods are based on supervised machine learning methods. However, the potential benefits of reinforcement learning (RL) techniques have shown that these techniques could be a viable option for classifying EMGs. Methods based on RL have several advantages such as promising classification performance and online learning from experience. In this work, we developed an HGR system made up of the following stages: pre-processing, feature extraction, classification, and post-processing. For the classification stage, we built an RL-based agent capable of learning to classify and recognize eleven hand gestures—five static and six dynamic—using a deep Q-network (DQN) algorithm based on EMG and IMU information. The proposed system uses a feed-forward artificial neural network (ANN) for the representation of the agent policy. We carried out the same experiments with two different types of sensors to compare their performance, which are the Myo armband sensor and the G-force sensor. We performed experiments using training, validation, and test set distributions, and the results were evaluated for user-specific HGR models. The final accuracy results demonstrated that the best model was able to reach up to 97.50%±1.13% and 88.15%±2.84% for the classification and recognition, respectively, with regard to static gestures, and 98.95%±0.62% and 90.47%±4.57% for the classification and recognition, respectively, with regard to dynamic gestures with the Myo armband sensor. The results obtained in this work demonstrated that RL methods such as the DQN are capable of learning a policy from online experience to classify and recognize static and dynamic gestures using EMG and IMU signals.

https://www.mdpi.com/1424-8220/22/24/9613/pdf?version=1670486753

Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks

Understanding the lower-limb coordination of individuals with unilateral transfemoral amputation (uTFA) while walking is essential to understand their gait mechanisms. Continuous relative phase (CRP) analysis provides insights into gait coordination patterns of the neuromusculoskeletal system based on movement kinematics. Fourteen individuals with uTFA and their age-matched able-bodied individuals participated in this study. Kinematic data of the lower limbs of the participants were collected during walking. The joint angles, segment angles, and CRP values of the thigh-shank and shank-foot couplings were investigated. The curves among the lower limbs of the participants were compared using a statistical parametric mapping test. Compensatory strategies were found in the lower limbs from coordination patterns. In thigh-shank coupling, although distinct coordination traits in stance and swing phases among the lower limbs were found, the lower limbs in both groups were discovered to remain in a similar coordination pattern during gait. For individuals with uTFA, in shank-foot coupling, intact limbs demonstrated a short period of foot-leading pattern which was significantly different from that of the other limbs during mid-stance to compensate for the weaker force generation by prosthetic limbs. The findings offer normative coordination patterns on the walking of individuals with uTFA, which could benefit prosthetic gait rehabilitation and development.

Coordination of Lower Limb during Gait in Individuals with Unilateral Transfemoral Amputation.

The aim of this review article is to appraise the design and functionality of above-knee prosthetic legs. So far, various transfemoral prosthetic legs are found to offer a stable gait to amputees but are limited to laboratories. The commercially available prosthetic legs are not reliable and comfortable enough to satisfy amputees. There is a dire need for creating a powered prosthetic knee joint that could address amputees’ requirements. To pinpoint the gap in transfemoral prosthetic legs, prosthetic knee unit model designs, control frameworks, kinematics, and gait evaluations are concentrated. Ambulation exercises, ground-level walking, running, and slope walking are considered to help identify research gaps and areas where existing prostheses can be ameliorated. The results show that above-knee amputees can more effectively manage their issues with the aid of an active prosthesis, capable of reliable gait. To accomplish the necessary control, closed loop controllers and volitional control are integral parts. Future studies should consider designing a transfemoral electromechanical prosthesis based on electromyographic (EMG) signals to better predict the amputee’s intent and control in accordance with that intent.

/pdf/design-kinematics-and-gait-analysis-of-prosthetic-knee-ip9vkzfe.pdf

Design, Kinematics and Gait Analysis, of Prosthetic Knee Joints: A Systematic Review

Gesture recognition is a foundational task in human-machine interaction (HMI). While there has been significant progress in gesture recognition based on surface electromyography (sEMG), accurate recognition of predefined gestures only within a closed set is still inadequate in practice. It is essential to effectively discern and reject unknown gestures of disinterest in a robust system. Numerous methods based on prototype learning (PL) have been proposed to tackle this open set recognition (OSR) problem. However, they do not fully explore the inherent distinctions between known and unknown classes. In this paper, we propose a more effective PL method leveraging two novel and inherent distinctions, feature activation level and projection inconsistency. Specifically, the Feature Activation Enhancement Mechanism (FAEM) widens the gap in feature activation values between known and unknown classes. Furthermore, we introduce Orthogonal Prototype Learning (OPL) to construct multiple perspectives. OPL acts to project a sample from orthogonal directions to maximize the distinction between its two projections, where unknown samples will be projected near the clusters of different known classes while known samples still maintain intra-class similarity. Our proposed method simultaneously achieves accurate closed-set classification for predefined gestures and effective rejection for unknown gestures. Extensive experiments demonstrate its efficacy and superiority in open-set gesture recognition based on sEMG.

Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning

Significant advances have been made in prostheses with the aim of enhancing the quality of life for transfemoral amputees. While commercially available transfemoral prostheses mainly focus on the developing passive prostheses that act only as dampers, academic research is centered round powered prostheses that can provide net-positive knee torque. Although recent active-powered prostheses have made excellent progress in terms of weight and battery life, it remains unclear if these prostheses can be effectively used in everyday life. This study presents a rotary hybrid prosthesis based on the combination of a multifunctional rotary hydraulic cylinder that is designed to operate as a brake, clutch, and damper with a 100 W active motor system. This prosthesis enables long-term level ground walking while supplying active power as needed. The passive and active components of this hybrid prosthesis are designed such that they can be decoupled when operated independently, allowing for quick switching between passive and active modes in 50–100 ms. The study outlines the aims and procedures for the design of rotary hybrid prostheses, as well as the feasibility tests for each module and the amputee’s clinical test on the developed knee prosthesis.

https://www.mdpi.com/2076-0825/12/3/118/pdf?version=1678357618

Selective Passive/Active Switchable Knee Prosthesis Based on Multifunctional Rotary Hydraulic Cylinder for Transfemoral Amputees

Changes in Spatiotemporal Parameters and Lower Limb Coordination During Prosthetic Gait Training in Unilateral Transfemoral Amputees

sEMG-based gesture recognition is useful for human–computer interactions, especially for technology supporting rehabilitation training and the control of electric prostheses. However, high variability in the sEMG signals of untrained users degrades the performance of gesture recognition algorithms. In this study, the hand posture recognition algorithm and radar plot-based visual feedback training were developed using multichannel sEMG sensors. Ten healthy adults and one bilateral forearm amputee participated by repeating twelve hand postures ten times. The visual feedback training was performed for two days and five days in healthy adults and a forearm amputee, respectively. Artificial neural network classifiers were trained with two types of feature vectors: a single feature vector and a combination of feature vectors. The classification accuracy of the forearm amputee increased significantly after three days of hand posture training. These results indicate that the visual feedback training efficiently improved the performance of sEMG-based hand posture recognition by reducing variability in the sEMG signal. Furthermore, a bilateral forearm amputee was able to participate in the rehabilitation training by using a radar plot, and the radar plot-based visual feedback training would help the amputees to control various electric prostheses.

https://www.mdpi.com/1424-8220/22/20/7984/pdf?version=1666694438

sEMG-Based Hand Posture Recognition and Visual Feedback Training for the Forearm Amputee

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Papers

Changes in Spatiotemporal Parameters and Lower Limb Coordination During Prosthetic Gait Training in Unilateral Transfemoral Amputees

sEMG-Based Hand Posture Recognition and Visual Feedback Training for the Forearm Amputee

Selective Passive/Active Switchable Knee Prosthesis Based on Multifunctional Rotary Hydraulic Cylinder for Transfemoral Amputees

Changes in Spatiotemporal Parameters and Lower Limb Coordination During Prosthetic Gait Training in Unilateral Transfemoral Amputees