Dual Window Pattern Recognition Classifier for Improved Partial-Hand Prosthesis Control.

ABSTRACT: Although partial-hand amputees largely retain the ability to use their wrist, it is difficult to preserve wrist motion while using a myoelectric partial-hand prosthesis without severely impacting control performance. Electromyogram (EMG) pattern recognition is a well-studied control method; however, EMG from wrist motion can obscure myoelectric finger control signals. Thus, to accommodate wrist motion and to provide high classification accuracy and minimize system latency, we developed a training protocol and a classifier that switches between long and short EMG analysis window lengths. Seventeen non-amputee and two partial-hand amputee subjects participated in a study to determine the effects of including EMG from different arm and hand locations during static and/or dynamic wrist motion

SUBMITTER: Earley EJ 

PROVIDER: S-EPMC4763088 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature