Development of A Hybrid Mini-Grid sEMG, NIRS and MMG Sensor System for Human-Machine Interaction

Surface electromyography (sEMG) is now a widely used biosignal for the human-machine interface (HMI), especially for prosthetic manipulation. However, the existing sEMG acquisition system could barely be taken into practical applications. Firstly, the traditional multi-channel system usually fails to provide high spatial resolution signal. Moreover, a high-density sEMG system could hardly be implemented as a portable or wearable device because of the considerable analog front-end owing to its excessive number of sample channels. This paper presents a compact size sEMG array integrating with near-infrared spectroscopy (NIRS) and mechanomyography (MMG) to acquire high-throughput muscular information. The sample channels are grouped by four and arrayed on a single sensor to improve the spatial resolution of sEMG. It is convenient to record sEMG, NIRS and MMG signals synchronously from multiple muscles. Extensive experiments are carried out to test the signal quality. The experimental results indicate that the proposed sensor system can reliably obtain three kinds of muscle contraction information from the perspective of motor unit action potential trains (MUAPt), hemodynamic and low-frequency myofiber vibration. It is verified that the proposed system has equivalent signal quality compared to existing systems. The outcomes of this study have great potential to improve the performance of HMI.