Article

Upper-limb amputation can cause a great deal of functional impairment for
patients, particularly for those with amputation at or above the elbow. Our
long-term objective is to improve functional outcomes for patients with
amputation by integrating a fully implanted electromyographic (EMG) recording
system with a wireless telemetry system that communicates with the patient's
prosthesis. We believe that this should generate a scheme that will allow
patients to robustly control multiple degrees of freedom simultaneously. The goal
of this study is to evaluate the feasibility of predicting dynamic arm movements
(both flexion/extension and pronation/supination) based on EMG signals from a set
of muscles that would likely be intact in patients with transhumeral amputation.
We recorded movement kinematics and EMG signals from seven muscles during a
variety of movements with different complexities. Time-delayed artificial neural
networks were then trained offline to predict the measured arm trajectories based
on features extracted from the measured EMG signals. We evaluated the relative
effectiveness of various muscle subsets. Predicted movement trajectories had
average root-mean-square errors of approximately 15.7 degrees and 24.9 degrees
and average R(2) values of approximately 0.81 and 0.46 for elbow
flexion/extension and forearm pronation/supination, respectively.

Langue : ANGLAIS