Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography Applications Using Sliding-Window Normalization

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 13; p. 4119
• Main Authors: Tanaka, Taichi, Nambu, Isao, Wada, Yasuhiro
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2025; MDPI
• Subjects: Accuracy; Adult; Algorithms; Calibration; Classification; DNN classification; Elbow; electrode shift; Electrodes; Electromyography; Electromyography - methods; EMG; Experiments; Fourier transforms; Humans; Machine learning; Male; Muscle fatigue; signal normalization; Signal Processing, Computer-Assisted; z-score; United States > US; DNN classification; z-score; electrode shift; electromyography; EMG; signal normalization
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25134119

Electromyography (EMG) signals have diverse applications, ranging from prosthetic hands and assistive suits to rehabilitation devices. Nonetheless, their performance suffers from cross-subject generalization issues, electrode shifts, and daily variability. In a previous study, while transfer learning narrowed the classification performance gap to −1% in an eight-class scenario under electrode shift, they imposed the burden of additional data collection and re-training. To address this issue in real-time prediction, we investigated a sliding-window normalization (SWN) technique that merges z-score normalization with sliding-window processing to align the EMG amplitude across channels and mitigate the performance degradation caused by electrode displacement. We validated SWN using experimental data from a right-arm trajectory-tracking task involving three motion classes (rest, flexion, and extension of the elbow). Offline analysis revealed that SWN mitigated accuracy degradation to −1.0% without additional data for re-training or multi-condition training, a 6.6% improvement compared with the −7.6% baseline without normalization. The advantage of SWN is that it operates with data from a single electrode position for training, which eliminates both the collection of multi-position training data and the calibration of deep learning models before practical use in EMG applications. Moreover, combining SWN with multi-position training exceeded the classification accuracy of the no-shift condition by 2.4%.