Concurrent surface electromyography and force myography classification during times of prosthetic socket shift and user fatigue. (July 2017)

• Record Type: Journal Article
• Authors: Sanford, Joe; Patterson, Rita; Popa, Dan O
• Is Part Of: Journal of rehabilitation and assistive technologies engineering. Volume 4(2017)
• Publication Date: 2017-07
• Subjects: Hand biomechanics -- human machine interface -- physical human–robot interaction -- pressure sensitive robot skin -- prosthetic device; Rehabilitation technology -- Periodicals; 617.03
• DOI: 10.1177/2055668317708731
• Languages: English
• ISSNs: 2055-6683

Abstract

Objective: Surface electromyography has been a long-standing source of signals for control of powered prosthetic devices. By contrast, force myography is a more recent alternative to surface electromyography that has the potential to enhance reliability and avoid operational challenges of surface electromyography during use. In this paper, we report on experiments conducted to assess improvements in classification of surface electromyography signals through the addition of collocated force myography consisting of piezo-resistive sensors. Methods: Force sensors detect intrasocket pressure changes upon muscle activation due to changes in muscle volume during activities of daily living. A heterogeneous sensor configuration with four surface electromyography–force myography pairs was investigated as a control input for a powered upper limb prosthetic. Training of two different multilevel neural perceptron networks was employed during classification and trained on data gathered during experiments simulating socket shift and muscle fatigue. Results: Results indicate that intrasocket pressure data used in conjunction with surface EMG data can improve classification of human intent and control of a powered prosthetic device compared to traditional, surface electromyography only systems. Significance: Additional sensors lead to significantly better signal classification during times of user fatigue, poor socket fit, as well as radial and ulnar wrist deviation. Results from experimentally obtained training data sets are presented.