Yanjuan Geng

TL;DR: The performance of EMG pattern-recognition based method in classifying movements strongly depends on arm positions, which suggests that the investigations associated with practical use of a myoelectric prosthesis should use the limb amputees as subjects instead of using able-body subjects.

TL;DR: In this article, the authors explored the principles and dynamics of the existing intelligently driven EMG-PR-based prostheses control scheme and investigated on core issues including variation in muscle contraction force, electrode shift, and subject mobility.

TL;DR: The results suggest that the combination of sampling rate of 500 Hz and high-pass cut-off frequency of 60 Hz should be an optimal selection in EMG recordings for recognition of different arm movements without sacrificing too much of classification accuracy.

TL;DR: The proposed multi-class common spatial pattern (MCCSP) method would be a practicable means in channel selection and would facilitate the design of practical myoelectric control systems in the active rehabilitation of mildly-impaired TBI patients and in other rehabilitation applications such as the multifunctional myoelectedric prostheses for limb amputees.

TL;DR: The effect of mobility on the performance of EMG-PR motion classifier was investigated based on myoelectric and accelerometer signals acquired from six upper-limb amputees across four scenarios and three methods were proposed to mitigate such effect.