Abstract
INTRODUCTION: Abandonment rates for myoelectric upper limb prostheses can reach 44%, negatively affecting quality of life and increasing the risk of injury due to compensatory movements. Traditional myoelectric prostheses rely on conventional signal processing for the detection and classification of movement intentions, whereas machine learning offers more robust and complex control through pattern recognition. However, the non-stationary nature of surface electromyogram signals and their day-to-day variations significantly degrade the classification performance of machine learning algorithms. Although single-session classification accuracies exceeding 99% have been reported for 8-class datasets, multisession accuracies typically decrease by 23% between morning and afternoon sessions. Retraining or adaptation can mitigate this accuracy loss. METHODS: This study evaluates three paradigms for retraining a machine learning-based classifier: confidence scores, nearest neighbour window assessment, and a novel signal-to-noise ratio-based approach. RESULTS: The results show that all paradigms improve accuracy against no retraining, with the nearest neighbour and signal-to-noise ratio methods showing an average improvement 5% in accuracy over the confidence-based approach. DISCUSSION: The effectiveness of each paradigm is assessed based on intersession accuracy across 10 sessions recorded over 5 days using the NinaPro 6 dataset.