2023/09/30 by Seitaro Yoneda, Yoneda, Seitaro, Akira Furui +1 · 2 citations
Engineering · Medicine · Neuroscience · #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Electrical engineering #Muscle activation and electromyography studies #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2310.00252
openalex publication_date 2023/09/30 · openalex created_date 2023/10/04 · openalex updated_date 2026/07/28
Intuitive human-machine interfaces may be developed using pattern classification to estimate executed human motions from electromyogram (EMG) signals generated during muscle contraction. The continual use of EMG-based interfaces gradually alters signal characteristics owing to electrode shift and muscle fatigue, leading to a gradual decline in classification accuracy. This paper proposes a Bayesian approach for adaptive EMG pattern classification using semi-supervised sequential learning. The proposed method uses a Bayesian classification model based on Gaussian distributions to predict the motion class and estimate its confidence. Pseudo-labels are subsequently assigned to data with high-prediction confidence, and the posterior distributions of the model are sequentially updated within the framework of Bayesian updating, thereby achieving adaptive motion recognition to alterations in signal characteristics over time. Experimental results on six healthy adults demonstrated that the proposed method can suppress the degradation of classification accuracy over time and outperforms conventional methods. These findings demonstrate the validity of the proposed approach and its applicability to practical EMG-based control systems.