Preliminary testing of a hand gesture recognition wristband based on EMG and inertial sensor fusion

Abstract

Electromyography (EMG) is well suited for capturing static hand features involving relatively long and stable muscle activations. At the same time, inertial sensing can inherently capture dynamic features related to hand rotation and translation. This paper introduces a hand gesture recognition wristband based on combined EMG and IMU signals. Preliminary testing was performed on four healthy subjects to evaluate a classification algorithm for identifying four surface pressing gestures at two force levels and eight air gestures. Average classification accuracy across all subjects was 88% for surface gestures and 96% for air gestures. Classification accuracy was significantly improved when both EMG and inertial sensing was used in combination as compared to results based on either single sensing modality.