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

Abstract

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.

Keywords: Adaptive learning; Classification; Electromyography; Incremental learning.

Graphical abstract

Fig. 1

Overview of the factors affecting the behavior of sEMG signals.

Fig. 2

Comparison of sEMG Signals During Hard Ball Grasping in a Pair of Alcoholic and Non-Alcoholic Participants.

Fig. 3

Comparison of sEMG Signals During Hard Ball Grasping in Another Pair of Alcoholic and Non-Alcoholic Participants.

Fig. 4

Comparison of sEMG Signals During Hard Ball Grasping in a Pair of Smoker and Non-Smoker Participants.

Fig. 5

Comparison of sEMG Signals During Hard Ball Grasping in Another Pair of Smoker and Non-Smoker Participants.

Fig. 6

Radar plot 37 features.

Fig. 7

Radar plot 8 features.

Fig. 8

Training, Validation, and Testing Average Accuracies by Dataset Group.

Fig. 9

Average Trainin and Testing Accuracies over Train and Testing Data Diversity.

Fig. 10

Enhancing sEMG-Based Learning Through Data-Centric and Model-Centric Approaches.

Fig. 11

Impact of Sliding Window Size on Learning Performance.

Fig. 12

Alcoholic category confusion matrix.

Fig. 13

Smoking category confusion matrix.

Fig. 14

Comparison of training time trade-off.

Fig. 15

Prototype of the Interactive Gesture Recognition Dashboard.