Robust and reliable hand gesture recognition for myoelectric control

Surface Electromyography (sEMG) is a physiological signal to record the electrical activity of muscles by electrodes applied to the skin. In the context of Muscle Computer Interaction (MCI), systems are controlled by transforming myoelectric signals into interaction commands that convey the intent of user movement, mostly for rehabilitation purposes. Taking the myoeletric hand prosthetic control as an example, using sEMG recorded from the remaining muscles of the stump can be considered as the most natural way for amputees who lose their limbs to perform activities of daily living with the aid of prostheses. Although the earliest myoelectric control research can date back to the 1950s, there still exist considerable challenges to address the significant gap between academic research and industrial applications. Most recently, pattern recognition-based control is being developed rapidly to improve the dexterity of myoelectric prosthetic devices due to the recent development of machine learning and deep learning techniques. It is clear that the performance of Hand Gesture Recognition (HGR) plays an essential role in pattern recognition-based control systems. However, in reality, the tremendous success in achieving very high sEMG-based HGR accuracy (≥ 90%) reported in scientific articles produced only limited clinical or commercial impact. As many have reported, its real-time performance tends to degrade significantly as a result of many confounding factors, such as electrode shift, sweating, fatigue, and day-to-day variation. The main interest of the present thesis is, therefore, to improve the robustness of sEMG-based HGR by taking advantage of the most recent advanced deep learning techniques to address several practical concerns. Furthermore, the challenge of this research problem has been reinforced by only considering using raw sparse multichannel sEMG signals as input. Firstly, a framework for designing an uncertainty-aware sEMG-based hand gesture classifier is proposed. Applying it allows us to quickly build a model with the ability to make its inference along with explainable quantified multidimensional uncertainties. This addresses the black-box concern of the HGR process directly. Secondly, to fill the gap of lacking consensus on the definition of model reliability in this field, a proper definition of model reliability is proposed. Based on it, reliability analysis can be performed as a new dimension of evaluation to help select the best model without relying only on classification accuracy. Our extensive experimental results have shown the efficiency of the proposed reliability analysis, which encourages researchers to use it as a supplementary tool for model evaluation. Next, an uncertainty-aware model is designed based on the proposed framework to address the low robustness of hand grasp recognition. This offers an opportunity to investigate whether reliable models can achieve robust performance. The results show that the proposed model can improve the long-term robustness of hand grasp recognition by rejecting highly uncertain predictions. Finally, a simple but effective normalisation approach is proposed to improve the robustness of inter-subject HGR, thus addressing the clinical challenge of having only a limited amount of data from any individual. The comparison results show that better performance can be obtained by it compared to a state-of-the-art (SoA) transfer learning method when only one training cycle is available. In summary, this study presents promising methods to pursue an accurate, robust, and reliable classifier, which is the overarching goal for sEMG-based HGR. The direction for future work would be the inclusion of these in real-time myoelectric control applications

A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet

Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gestures many times in real life. This problem can be alleviated to a certain extent if sEMG from many other subjects could be used to train the classifier. In this paper, we propose a normalisation approach that allows implementing real-time subject-independent sEMG based hand gesture classification without training the deep learning algorithm subject specifically. We hypothesed that the amplitude ranges of sEMG across channels between forearm muscle contractions for a hand gesture recorded in the same condition do not vary significantly within each individual. Therefore, the min-max normalisation is applied to source domain data but the new maximum and minimum values of each channel used to restrict the amplitude range are calculated from a trial cycle of a new user (target domain) and assigned by the class label. A convolutional neural network (ConvNet) trained with the normalised data achieved an average 87.03 accuracy on our G. dataset (12 gestures) and 94.53 on M. dataset (7 gestures) by using the leave-one-subject-out cross-validation

Solid Oxide Fuel Cells with both High Voltage and Power Output by Utilizing Beneficial Interfacial Reaction

An intriguing cell concept by applying proton-conducting oxide as the ionic conducting phase in the anode and taking advantage of beneficial interfacial reaction between anode and electrolyte is proposed to successfully achieve both high open circuit voltage (OCV) and power output for SOFCs with thin-film samarium doped ceria (SDC) electrolyte at temperatures higher than 600 °C. The fuel cells were fabricated by conventional route without introducing an additional processing step. A very thin and dense interfacial layer (2–3 μm) with compositional gradient was created by in situ reaction between anode and electrolyte although the anode substrate had high surface roughness (\u3e5 μm), which is, however, beneficial for increasing triple phase boundaries where electrode reactions happen. A fuel cell with Ni–BaZr0.4Ce0.4Y0.2O3 anode, thin-film SDC electrolyte and Ba0.5Sr0.5Co0.8Fe0.2O3–δ (BSCF) cathode has an OCV as high as 1.022 V and delivered a power density of 462 mW cm−2 at 0.7 V at 600 °C. It greatly promises an intriguing fuel cell concept for efficient power generation

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.Comment: 10 pages,7 figure

Reliability Analysis For Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks

Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge. This may be due to the lack of consensus on the definition of model reliability in this field. An uncertainty-aware model has the potential to self-evaluate the quality of its inference, thereby making it more reliable. Moreover, uncertainty-based rejection has been shown to improve the performance of sEMG-based hand gesture recognition. Therefore, we first define model reliability here as the quality of its uncertainty estimation and propose an offline framework to quantify it. To promote reliability analysis, we propose a novel end-to-end uncertainty-aware finger movement classifier, i.e., evidential convolutional neural network (ECNN), and illustrate the advantages of its multidimensional uncertainties such as vacuity and dissonance. Extensive comparisons of accuracy and reliability are conducted on NinaPro Database 5, exercise A, across CNN and three variants of ECNN based on different training strategies. The results of classifying 12 finger movements over 10 subjects show that the best mean accuracy achieved by ECNN is 76.34%, which is slightly higher than the state-of-the-art performance. Furthermore, ECNN variants are more reliable than CNN in general, where the highest improvement of reliability of 19.33% is observed. This work demonstrates the potential of ECNN and recommends using the proposed reliability analysis as a supplementary measure for studying sEMG-based hand gesture recognition

Protocol for an observational study on the effects of adolescent sports participation on health in early adulthood

We will study the impact of adolescent sports participation on early-adulthood health using longitudinal data from the National Survey of Youth and Religion. We focus on two primary outcomes measured at ages 23--28 -- self-rated health and total score on the PHQ9 Patient Depression Questionnaire -- and control for several potential confounders related to demographics and family socioeconomic status. Comparing outcomes between sports participants and matched non-sports participants with similar confounders is straightforward. Unfortunately, an analysis based on such a broad exposure cannot probe the possibility that participation in certain types of sports (e.g. collision sports like football or soccer) may have larger effects on health than others. In this study, we introduce a hierarchy of exposure definitions, ranging from broad (participation in any after-school organized activity) to narrow (e.g. participation in limited-contact sports). We will perform separate matched observational studies, one for each definition, to estimate the health effects of several levels of sports participation. In order to conduct these studies while maintaining a fixed family-wise error rate, we developed an ordered testing approach that exploits the logical relationships between exposure definitions. Our study will also consider several secondary outcomes including body mass index, life satisfaction, and problematic drinking behavior

Anti-PD-1 antibodies, a novel treatment option for advanced chemoresistant pulmonary lymphoepithelioma carcinoma

BackgroundPulmonary lymphoepithelioma-like carcinoma (LELC) exhibits a unique immune microenvironment, including high PD-L1 expression and abundant infiltrating-immune cells. However, the availability of PD-1/PD-L1 inhibitors in patients with LELC is still not determined.MethodsA total of 36 cases of pulmonary LELC treated with PD-1/PD-L1 inhibitors were reviewed, including 10 cases from our institute and 26 cases included from the literature. The Kaplan-Meier method and log-rank test were utilized to analyze the survival outcomes of LELC patients receiving immunotherapy, and the factors related to immunotherapy response were further examined.ResultsOf the 10 patients from our institute, the median age was 53.5 years, adrenal glands and distant lymph nodes were the most common metastatic sites, and 4 of 8 (50%) patients had a PD-L1 TPS ≥50%. The median progression-free survival and overall survival in patients from our institute and from the literature were 11.6 and 27.3 months, 17.2 months and not reached, respectively. In all 36 patients, the objective response rate was as high as 57.6%. Patients with higher PD-L1 expression were more likely to have a tumor response, but the association of PD-L1 expression with survival time remains to be determined.ConclusionsPD-1/PD-L1 inhibitors in patients with pulmonary LELC demonstrated a promising efficacy in retrospective cohorts, and deserve further validation in prospective studies administrating in front-line setting