Measuring Motor Unit Discharge, Myofiber Vibration, and Haemodynamics for Enhanced Myoelectric Gesture Recognition

It is of great significance to recognize hand gestures via measuring biological signals from forearm muscles for portable human–machine interaction (HMI). Decreasing the number of sensor nodes is imperative for practical HMI applications. However, it would be an enormous challenge to maintain gesture recognition performance for traditional myoelectric interface with sparse-site sensing. To overcome this drawback, we present a novel HMI predicting more than ten hand and wrist motions relying on only two hybrid mini-grid surface electromyography (sEMG), mechanomyography (MMG), and near-infrared spectroscopy (NIRS) sensor nodes. Beyond the time domain (TD) features of sEMG, additional information containing movement intention is measured from motor unit (MU) action potential trains (MUAPts) according to the decomposition of four-channel arrayed sEMG. Furthermore, low-frequency myofiber vibration and haemodynamics are extracted from MMG and NIRS, respectively. Experiments are performed on 13 healthy subjects to recognize 12 hand and wrist gestures. The results indicate that combining motor unit discharge feature yields consistently higher (<inline-formula> <tex-math notation="LaTeX">${p} &lt; 0.01$ </tex-math></inline-formula>) classification accuracy (CA) (91.6%) than traditional TD features of sEMG (87.8%) using linear discrimination analysis (LDA) classifier. Additionally, both MMG and NIRS features are demonstrated effective supplementary to distinguish muscular activation patterns, producing significantly enhanced (4.2%–11.2%, <inline-formula> <tex-math notation="LaTeX">${p} &lt; 0.05$ </tex-math></inline-formula>) recognition performance with the fused information. The outcomes of this study are promising for the HMI applications such as controlling prosthetic hand and wearable device.