Comparative Evaluation of Filtering Methods for Wearable sEMG Sensing in Sign Language Recognition: Classification Performance and Generalization Robustness

Wearable surface electromyography (sEMG) biosensors enable non-invasive monitoring of hand and forearm activity and are promising for sign language recognition (SLR) in digital health applications. However, signals acquired with wearable sEMG devices are affected by noise and motion artifacts, and the preprocessing filter strongly shapes the extracted features, yet filtering methods are rarely compared under identical conditions. Here, eight preprocessing methods (Bessel, Butterworth, Chebyshev Type I and II, Elliptic, Equiripple FIR, Discrete Wavelet Transform (DWT), and Modified Wiener) were compared under controlled laboratory conditions using four-channel sEMG recorded with a wireless wearable acquisition system from 20 participants performing 10 sign classes. All methods shared the same segmentation, feature extraction, and classification pipeline with KNN, LDA, LR, RF, and SVM classifiers, evaluated under random split, cross-session, and subject-independent protocols. Butterworth filtering with KNN achieved the highest random split performance (Accuracy 96.66%, F1-score 96.12%) and the highest subject-independent accuracy (88.81%). Filter effectiveness depended on the classifier and validation scenario, and a higher SNR did not guarantee higher accuracy. LR and LDA required approximately 0.1 ms per sample for prediction in the evaluated computational environment. However, these measurements do not establish real-time performance on wearable hardware. The findings provide comparative evidence for selecting sEMG preprocessing methods under controlled offline conditions, while their applicability to real-time wearable systems requires further validation.