A novel unsupervised dynamic feature domain adaptation strategy for cross-individual myoelectric gesture recognition.

Surface electromyography pattern recognition (sEMG-PR) is considered as a promising control method for human-machine interaction systems. However, the performance of a trained classifier would greatly degrade for novel users since sEMG signals are user-dependent and largely affected by a number of individual factors such as the quantity of subcutaneous fat and the skin impedance.To solve this issue, we proposed a novel unsupervised cross-individual motion recognition method that aligned sEMG features from different individuals by self-adaptive dimensional dynamic distribution adaptation (SD-DDA) in this study.

Toward improving control performance of myoelectric arm prosthesis by adding wrist position feedback.

Wearing a myoelectric prosthesis is a basic way for limb amputees to restore their lost limb functions in the activities of daily living (ADLs). However, it is estimated that around 40% of amputees refuse the prosthesis. One of the primary reasons would be that the current prostheses lack appropriate sensory feedback.