Abstract
BackgroundMost prosthetic myoelectric control studies have concentrated on low density (less than 16 electrodes, LD) electromyography (EMG) signals, due to its better clinical applicability and low computation complexity compared with high density (more than 16 electrodes, HD) EMG signals. Since HD EMG electrodes have been developed more conveniently to wear with respect to the previous versions recently, HD EMG signals become an alternative for myoelectric prostheses. The electrode shift, which may occur during repositioning or donning/doffing of the prosthetic socket, is one of the main reasons for degradation in classification accuracy (CA).MethodsHD EMG signals acquired from the forearm of the subjects were used for pattern recognition-based myoelectric control in this study. Multiclass common spatial patterns (CSP) with two types of schemes, namely one versus one (CSP-OvO) and one versus rest (CSP-OvR), were used for feature extraction to improve the robustness against electrode shift for myoelectric control. Shift transversal (ST1 and ST2) and longitudinal (SL1 and SL2) to the direction of the muscle fibers were taken into consideration. We tested nine intact-limb subjects for eleven hand and wrist motions. The CSP features (CSP-OvO and CSP-OvR) were compared with three commonly used features, namely time-domain (TD) features, time-domain autoregressive (TDAR) features and variogram (Variog) features.ResultsCompared with the TD features, the CSP features significantly improved the CA over 10 % in all shift configurations (ST1, ST2, SL1 and SL2). Compared with the TDAR features, a. the CSP-OvO feature significantly improved the average CA over 5 % in all shift configurations; b. the CSP-OvR feature significantly improved the average CA in shift configurations ST1, SL1 and SL2. Compared with the Variog features, the CSP features significantly improved the average CA in longitudinal shift configurations (SL1 and SL2).ConclusionThe results demonstrated that the CSP features significantly improved the robustness against electrode shift for myoelectric control with respect to the commonly used features.
Highlights
Surface electromyography (EMG) signals, which contain neural information [1], have long been used as control inputs of myoelectric prostheses [2,3,4]
We investigate whether the common spatial patterns (CSP) of HD EMG signals can improve the myoelectric control performance under electrode shift for eleven classes of hand and wrist motions
Since the average classification accuracy (CA) of all features without electrode shift was over 90 %, it demonstrated that the half grid configuration without electrode shift was sufficient to provide good myoelectric control performance for all features (CSP-OvO, CSP-OvR, TD, time-domain autoregressive (TDAR) and Variog)
Summary
Surface electromyography (EMG) signals, which contain neural information [1], have long been used as control inputs of myoelectric prostheses [2,3,4]. Electrode shift is an identified problem existing in both LD and HD EMG applications It may occur during repositioning or donning/doffing of the prosthetic socket. Young et al demonstrated that electrode with larger size reduced the sensitivity of shift while performing worse than electrodes with smaller size without shift [11] They suggested that electrodes oriented in longitudinal direction with the muscle fibers performed better than that oriented in transversal direction. They showed that time-domain autoregressive (TDAR) features achieved the best classification performance and was least affected by electrode displacements. The electrode shift, which may occur during repositioning or donning/doffing of the prosthetic socket, is one of the main reasons for degradation in classification accuracy (CA)
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