Overcoming behavioral variability in electromyography signals by an adaptive incremental classification approach.

Surface electromyography (sEMG)-based hand gesture recognition systems often experience significant performance degradation when deployed in heterogeneous populations, particularly among individuals with lifestyle-related physiological variations such as smoking or alcohol consumption. These variations induce distributional shifts that conventional models fail to address, reducing their practical applicability. To overcome this challenge, we introduce an adaptive incremental k-Nearest Neighbors (ADINC-kNN) algorithm designed to maintain robust classification performance without requiring full model retraining. The proposed method integrates a sliding-window buffer with distance-weighted voting to dynamically refine decision boundaries, enabling smooth adaptation from baseline populations (non-smokers/non-drinkers) to target groups (smokers/alcohol consumers). Extensive evaluation was conducted on 15 hand force exercises performed by 14 subjects using 5-fold cross-validation. ADINC-kNN consistently outperformed static kNN, achieving substantial improvements in accuracy, precision, recall, and F1-score across target populations, with classification performance exceeding 90 % in both smoking and alcohol groups. Although the prediction time per fold increased, the algorithm achieved a superior balance between computational efficiency and predictive accuracy compared with state-of-the-art approaches that typically rely on costly retraining. These results demonstrate the effectiveness of ADINC-kNN as a scalable and practical solution for robust sEMG-based gesture recognition. Its ability to adapt dynamically to population-specific physiological variations makes it particularly suitable for real-world applications in rehabilitation, assistive technology, and human-machine interaction, where user diversity and changing conditions are unavoidable.