The effect of arm position on classification of hand gestures with intramuscular EMG. (May 2018)

• Record Type: Journal Article
• Authors: Jochumsen, Mads; Waris, Asim; Kamavuako, Ernest Nlandu
• Is Part Of: Biomedical signal processing and control. Volume 43(2018)
• Page Start: 1
• Page End: 8
• Publication Date: 2018-05
• Subjects: Surface EMG -- Intramuscular EMG -- Prosthetics -- Arm position -- Upper extremity; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.02.013
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Discrimination of upper limb movement using intramuscular EMG is affected by position. Combining surface and intramuscular EMG improves discrimination of upper limb movement. The effect of arm position can be reduced by including more training positions in the calibration. Abstract: The arm position affects discrimination between upper limb motion classes when using surface EMG (sEMG). In this study, the effect of arm position on motion class discrimination was investigated using intramuscular EMG (iEMG). Eight able-bodied subjects performed five motion classes (hand grasp, hand open, rest, wrist extension, wrist flexion) in four different arm positions (0, 45, 90, 135°). Three classification scenarios were evaluated using Hudgins' time domain features and a Bayes classifier; within position classification (WPC), across position classification (APC), and between position classification (BPC). The same analysis was performed using sEMG and with combined surface and iEMG. For WPC, similar classification accuracies were obtained using the different types of EMG (93–98%). The mean absolute value and waveform length were associated with the highest classification accuracies compared to zero crossing and slope sign changes for WPC. For APC, classification accuracies dropped to 85–95%, and for BPC, classification accuracies dropped to 69–83% with hand opening being the least discriminable motion class. The degree of decreased performance was computed as: 1) APC/WPC: 0.94 ± 0.03 (sEMG) and 0.92 ± 0.05 (iEMG), and 2) BPC/WPC: 0.81 ± 0.06 (sEMG) and 0.78 ± 0.12 (iEMG), indicating that arm position affects iEMG in a similar degree as sEMG, which is a practicality issue for the clinical application of pattern recognition based control schemes.