A hierarchical classification of gestures under two force levels based on muscle synergy

Abstract

Multifunctional intelligent prosthetics (IPs) require precise control of the movement under users’ intentions where force interaction between the prosthetics and the environment must be considered. Surface electromyography (sEMG) is a promising input for IP control as it reflects users’ motor intention on both movement and force levels. However, simultaneously decoding the movements and their force levels based on sEMG has not yet been deeply studied. This paper proposed a sEMG-based hierarchical pattern recognition strategy that enables decoding of gesture types and force levels simultaneously. The hierarchical strategy consisted of two layers of classification (gesture and force level classification) based on two sEMG features: muscle synergy (MS) and root mean square (RMS). The strategy was tested on 13 healthy participants with 6 wireless sEMG electrodes. Resutls showed that the gesture classification with MS and RMS achieved similar accuracies of 98.78% and 98.12, respectively, while the force level classification obtained a significantly higher accuracy of 94.04% with MS compard to 78.94% with RMS. The result of correlation analysis was that the MS correlation coeficients within group were significantly larger than those between groups, which validated that muscle activities at different force levels varied with not only increasing or decreasing the activation intensity but also changing muscle synergy patterns. Finally, the hierarchical strategy achieved an accuracy of 93.4% with MS as the input of the two-layer classification. It was concluded that the hierarchical stragety was promising for gesture and force level recognition, especially with MS featues, which was sensitive to force level recognition.