Dynamic time warping for reducing the effect of force variation on myoelectric control of hand prostheses. (October 2019)

• Record Type: Journal Article
• Authors: Powar, Omkar S; Chemmangat, Krishnan
• Is Part Of: Journal of electromyography and kinesiology. Volume 48(2019)
• Page Start: 152
• Page End: 160
• Publication Date: 2019-10
• Subjects: Electromyography (EMG) -- Dynamic Time Warping (DTW) -- Pattern Recognition (PR) -- Force level variation -- Classification; Electromyography -- Periodicals; Kinesiology -- Periodicals; Electromyography -- Periodicals; Movement -- physiology -- Periodicals; Muscles -- physiology -- Periodicals; Électromyographie -- Périodiques; Cinésiologie -- Périodiques; Electromyography; Kinesiology; Electronic journals; Periodicals; 616.740757
• DOI: 10.1016/j.jelekin.2019.07.006
• Languages: English
• ISSNs: 1050-6411

Abstract

Abstract: Research in pattern recognition (PR) for myoelectric control of the upper limb prostheses has been extensive. However, there has been limited attention to the factors that influence the clinical translation of this technology. A relevant factor of influence in clinical performance of EMG PR-based control of prostheses is the variation in muscle activation level, which modifies the EMG patterns even when the amputee attempts the same movement. To decrease the effect of muscle activation level variations on EMG PR, this work proposes to use dynamic time warping (DTW) and is validated on two databases. The first database, which has data from ten intact-limbed subjects, was used to test the baseline performance of DTW, resulting in an average classification accuracy of more than 90%. The second database comprised data from nine upper limb amputees recorded at three levels of force for six hand grips. The results showed that DTW trained at a single force level achieved an average classification accuracy of 60 ± 9%, 70 ± 8%, and 60 ± 7% at the low, medium and high force levels respectively across all amputee subjects. The proposed scheme with DTW achieved a significant 10% improvement in classification accuracy when trained at a low force level when compared to the traditional time-dependent power spectrum descriptors (TD-PSD) method.