Onset-Triggered Vote-Locking (OTVL) for Stable Myoelectric Control of Multi-DoF Prosthetic Hands

Abstract

This study presents a novel semi-locked myoelectric pattern recognition algorithm (Onset-Triggered Vote-Locking or OTVL) to reduce misclassifications that typically occur during transition phases of muscular contractions for prosthetic hand control. It segments the electromyographic signals in real-time by adaptively defining enabling and disabling conditions at the onset of a muscular contraction in order to lock classifier predictions only when they are considered stable. The segmentation is based on signal standard deviation to improve robustness and reduce the need for rest transitions between movements. The proposed algorithm was implemented with a standard Linear Discriminant Analysis (LDA). It was functionally assessed by simulating activities of daily living and compared against a continuous LDA classifier with two conventional post-processing algorithms: Majority Vote (MV) and Confidence Rejection (CR). Eleven able-bodied participants executed ten repetitions of three Southampton Hand Assessment Procedure (SHAP) tasks using a bypass socket equipped with an active wrist and a multi-articulated hand. The OTVL algorithm significantly outperformed CR in terms of success rate (median:IQR; OTVL: 100.0%:0%; CR: 10.0%:60.0%; p < 0.001). Compared to MV, OTVL significantly reduced total completion time (OTVL: 56.4s:20.6s; MV: 62.2s:7.7s; p < 0.05) and the number of incorrect preshapes (OTVL: 0.6:0.4; MV: 1.5:1.3; p < 0.01) that occurred between tasks. The perceived workload, as assessed by the NASA Task Load Index, was also significantly reduced compared to MV and CR (OTVL: 52:33.5; MV: 67:21.5; CR: 95:36.3; p < 0.001). Therefore, the OTVL algorithm represents a viable alternative to traditional continuous classifiers, improving control stability and task execution.