Electromyography-based assistive and rehabilitation devices have shown potential for restoring mobility, especially for post-stroke patients. However, the variability of biological signals and the processing delays caused by signal acquisition and feature extraction influence myoelectric control systems’ real-time functionality and robustness. This study evaluates the classification performance of electromyographic (EMG) signals for six distinct hand gestures in healthy individuals and post-stroke patients. Different feature extraction methods and machine learning algorithms are employed to analyze the impact of acquisition time (0.5–4 s) and the number of channels (1–4) on model accuracy, robustness, and generalization. The best results are obtained using power spectral density and dimensionality reduction, reaching a classification accuracy of 94.79% with a 2 s signal and 95.31% for 4 s. Acquisition time has a greater effect on accuracy than the number of channels used with accuracy stabilizing at 2 s. We test for generalization using post-stroke patient data, evaluating two scenarios: intra-patient validation with 90% accuracy and cross-patient validation with 35–40% accuracy. This study contributes to developing effective real-time myoelectric control systems for neurorehabilitation.

This research investigates the influence of thickness on residual stress profiles in aluminum cold spray coatings using finite element analysis (FEA). Residual stress is a critical factor that impacts coating adhesion, fatigue life, and susceptibility to delamination in thermal spray processes. Despite its acknowledged importance, predictive analysis of these stresses on a layer-by-layer basis remains relatively unexplored. This study introduces an innovative numerical methodology to analyze the progression of residual stresses across various deposition efficiencies (10%, 40%, 60%, and 100%) and layer thicknesses, thereby enhancing predictive accuracy for cold spray coatings. The findings demonstrate that the number of deposited layers significantly affects residual stress profiles in both coatings and the substrate, with compressive residual stress predominating in the coatings and deeper tensile stress predominating in the substrate. Residual stress behavior near the last deposited layer aligns with the expected peening effect. Discrepancies in substrate stress distributions may arise from variations in deposition parameters and unconsidered temperature effects. While the model generally aligns with theoretical and some empirical data, observed discrepancies underscore the need for further validation. This study lays the groundwork for informed decision-making for cold spray processes by providing insights into stress management, thereby contributing to enhancing coating integrity and performance.

Electromyography-based assistive and rehabilitation devices have shown potential for restoring mobility, especially for post-stroke patients. However, the variability of biological signals and the processing delays caused by signal acquisition and feature extraction influence myoelectric control systems’ real-time functionality and robustness. This study evaluates the classification performance of electromyographic (EMG) signals for six distinct hand gestures in healthy individuals and post-stroke patients. Different feature extraction methods and machine learning algorithms are employed to analyze the impact of acquisition time (0.5–4 s) and the number of channels (1–4) on model accuracy, robustness, and generalization. The best results are obtained using power spectral density and dimensionality reduction, reaching a classification accuracy of 94.79% with a 2 s signal and 95.31% for 4 s. Acquisition time has a greater effect on accuracy than the number of channels used, with accuracy stabilizing at 2 s. We test for generalization using post-stroke patient data, revealing a substantial accuracy drop from 90% (intra-patient validation) to 35–40% (cross-patient validation). This study paves the way for developing effective real-time myoelectric control systems for neurorehabilitation.