Real-time upper limb motion prediction from noninvasive biosignals for physical human-machine interactions

Abstract

Human motion and its intention sensing from noninvasive biosignals is one of the significant issues in the field of physical human-machine interactions (pHMI). This paper presents a real-time upper limb motion prediction method using surface electromyography (sEMG) signals for pHMI. The sEMG signals from 5 channels were collected and used to predict the motion by an artificial neural network (ANN) algorithm. We designed a human-machine interaction system to verify the proposed method. Interaction experiments were performed with or without physical contact, and the effects of instances of contact were investigated. The experimental results were compared with controlled experiments using a customized goniometer, which is able to measure upper limb flexion-extension. The results showed that the proposed method was not superior to the use of direct angle measurements; however, it provides sufficient accuracy and a fast response speed for interactions. SEMG-based interactions will become more natural with further studies of human-machine combination models.

Implementation of Human-Machine Synchronization Control for Active Rehabilitation Using an Inertia Sensor

“…Last, it is a precondition to perform active training that the device be backdrivable. The current method to improve the backdrivability of a device is the use of admittance control, which can drive the device according to the contact force between the human and the device [18,19]. However, it is also very difficult to obtain the accurate contact force for our device, because the contact condition between the ULERD and human limbs is very complicated.…”