Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation.

ABSTRACT: High-density electromyography (HD-EMG) can provide a natural interface to enhance human-computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3±0.3% accuracy calculated on a

SUBMITTER: Tacca N 

PROVIDER: S-EPMC11316006 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature