Title :
Crosspoint switching of EMG signals to increase number of channels for pattern recognition myoelectric control
Author :
Gajendran, Rudhram ; Tkach, Dennis C. ; Hargrove, Levi J.
Author_Institution :
Univ. of Illinois at Chicago, Chicago, IL, USA
Abstract :
Myoelectric pattern recognition (PR) can provide a more intuitive control for upper limb amputees in using multifunction prosthesis than direct control. Accuracy of a pattern recognition system has been shown to improve with increasing number of EMG channels. However, increasing the number of channels comes with a drawback of increased weight, cost and complexity of the prosthesis. This paper presents the concept and design of a novel EMG acquisition system to acquire higher number of channels without increasing the number of electrodes placed or the complexity of the prosthetic device. A prototype of the device was developed and tested on able-bodied subjects to evaluate its performance in pattern recognition. Subjects were requested to perform 9 different hand movements while EMG data was collected into training and test groups. Test results indicate a 15% improvement in classification accuracy with the new system when compared to conventional systems. A system like this is valuable for patients with higher level amputations where placing higher number of electrodes is not feasible due to limited availability of appropriate muscle sites.
Keywords :
biomedical electrodes; electromyography; gait analysis; handicapped aids; medical signal processing; pattern recognition; prosthetics; signal classification; EMG acquisition system design; EMG channels; EMG data; EMG signals; PR; able-bodied subjects; appropriate muscle sites; classification accuracy; crosspoint switching; direct control; electrode number; hand movements; multifunction prosthesis; pattern recognition myoelectric control; pattern recognition system; prosthesis cost; prosthetic device complexity; upper limb amputees; Electrodes; Electromyography; Muscles; Pattern recognition; Prosthetics; Switches;
Conference_Titel :
Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on
Conference_Location :
San Diego, CA
DOI :
10.1109/NER.2013.6695921