Deep learning approach to improve the recognition of hand gesture with multi force variation using electromyography signal from amputees.

Variations in muscular contraction are known to significantly impact the quality of the generated EMG signal and the output decision of a proposed classifier. This is an issue when the classifier is further implemented in prosthetic hand design. Therefore, this study aims to develop a deep learning classifier to improve the classification of hand motion gestures and investigate the effect of force variations on their accuracy on amputees. The contribution of this study showed that the resulting deep learning architecture based on DNN (deep neural network) could recognize the six gestures and robust against different force levels (18 combinations). Additionally, this study recommended several channels that most contribute to the classifier's accuracy. Also, the selected time domain features were used for a classifier to recognize 18 combinations of EMG signal patterns (6 gestures and three forces). The average accuracy of the proposed method (DNN) was also observed at 92.0 ± 6.1 %. Moreover, several other classifiers were used as comparisons, such as support vector machine (SVM), decision tree (DT), K-nearest neighbors, and Linear Discriminant Analysis (LDA). The increase in the mean accuracy of the proposed method compared to other conventional classifiers (SVM, DT, KNN, and LDA), was 17.86 %. Also, the study's implication stated that the proposed method should be applied to developing prosthetic hands for amputees that recognize multi-force gestures.

Correlation of fatigue on walking ability in myasthenia gravis patients: a cross-sectional study.

Introduction: Myasthenia gravis (MG) is a neuromuscular junction autoimmune disease characterised of intermittent muscle weakness that increases with activity and recovers with rest. Objective: Analysing the correlation of fatigue on walking ability in MG patients. Methods: This study used a cross-sectional design with consecutive sampling. Participants MG patients took in this trial. Data collection encompasses fatigue and walking ability, with fatigue being assessed using the fatigue severity scale (FSS) and walking ability being assessed using the 10-metre walking test. The 10-metre walking test assessment contains three components: comfortable walking speed (CWS), maximum walking speed (MWS), and natural cadence. The statistical analysis used in this study includes the Pearson correlation and Spearman rank tests with P<0.05. Results: The number of participants was 23 MG patients, and most of the participant was female (69.6%). The participant's fatigue value was 5.46±1.13, including MGFA 1=5.32±1.15, MGFA 2A=5.5±1.11, and MGFA 2B=5.61±1.30. Meanwhile, the participant's walking abilities included CWS of 1.10±0.11 m/s, MWS of 1.31±0.15 m/s, and natural cadence of 110.91±7.74 steps/min. No significant correlation of fatigue on walking ability including FSS vs. CWS (r=-0.141; P=0.520), FSS vs MWS (r=-0.169; P=0.442), and FSS vs. natural cadence (r=-0.050; P=0.822). Conclusion: There was no significant correlation between fatigue and walking ability in MG patients who had MGFA 1, MGFA 2A, and MGFA 2B.

An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter.

High accuracy in pattern recognition based on electromyography(EMG) contributes to the effectiveness of prosthetics hand development. This study aimed to improve performance and simplify the deep learning pre-processing based on the convolution neural network (CNN) algorithm for classifying ten hand motion from two raw EMG signals. The main contribution of this study is the simplicity of pre-processing stage in classifier machine. For instance, the feature extraction process is not required. Furthermore, the performance of the classifier was improved by evaluating the best hyperparameter in deep learning architecture. To validate the performance of deep learning, the public dataset from ten subjects was evaluated. The performance of the proposed method was compared to other conventional machine learning, specifically LDA, SVM, and KNN. The CNN can discriminate the ten hand-motion based on raw EMG signal without handcrafts feature extraction. The results of the evaluation showed that CNN outperformed other classifiers. The average accuracy for all motion ranges between 0.77 and 0.93. The statistical t-test between using two-channel(CH1 and CH2) and single-channel(CH2) shows that there is no significant difference in accuracy with p-value >0.05. The proposed method was useful in the study of prosthetic hand, which required the simple architecture of machine learning and high performance in the classification.