Non-Invasive Human-Machine Interface (HMI) Systems With Hybrid On-Body Sensors for Controlling Upper-Limb Prosthesis: A Review

Abstract

In this work, we present a systematic review on non-invasive HMIs employing hybrid wearable sensor modalities for recognition of upper limb intentions. Different combinations of the sensors are investigated. As sEMG is dominant in the applications of externally powered prosthetic hands, it is involved in most hybrid sensor combinations. The combined use of sEMG and IMU is most studied in the literature as IMU is easy to be integrated. Though limited, the investigation on other hybrid modalities has been drawing more and more research attention and efforts, especially those with FMG and NIRS. For all the reported hybrid sensors, it is verified that this strategy can enrich the information of user intention and help the pattern recognition and/or intensity regulation of robotic hand/arm prosthesis. Though it is the trend, the development of these hybrid-sensor-based HMIs are still at the preliminary stage. More dedicated sensor fusion models and system architectures as well as new hybrid features and algorithms need to be developed to make the best use of each sensing modality’s strength to achieve robust and stable user intention recognition, which is essential for the progress and user acceptance of upper limb prosthesis.