부산대학교 도서관

Objective: Electric hand prostheses are typically controlled using electromyographic (EMG) signals recorded from the residual muscles. However, non-stationarities that are characteristic for EMG interfaces impair the reliability of machine-learning-based approaches during daily life activities-based approaches (e.g., the limb position effect). Including additional EMG-independent information in the classification algorithm may mitigate this problem. Methods: In this study, we systematically investigated an electrical impedance myography (EIM) interface for its possible utility as an additional source of information to EMG. To this goal, six different hand-wrist motions in three arm positions were recorded from ten able-bodied volunteers and three prosthetic hand users. EIM and EMG data were evaluated in terms of information content and classified using linear discriminant analysis (LDA). Results: EIM contained less information and was more strongly influenced by changing limb positions than EMG, but a combination of EIM and EMG outperformed EMG alone. Training with pooled data from multiple arm positions was necessary to mitigate the limb position effect. Conclusion: EIM can be valuable for myoelectric control as it contains complementary information to EMG, but it is also strongly influenced by changing arm positions. Significance: This paper provides fundamental insights required for advancing the application of EIM in the context of modern prosthesis control.