Simultaneous estimation of joint angle and interaction force towards sEMG-driven human-robot interaction during constrained tasks

Abstract Human has excellent motor capability and performance in completing various manipulation tasks. During some tasks such as tightening/loosening a screw with a screwdriver, the motion is accompanied by force exertion to the environment (that is, constrained motion). To obtain natural human-robot interaction (HRI) as human interacts/collaborates with the environment, interpreting the human’s intention in a way of motion and interaction force is meaningful for carrying out constrained tasks. This paper proposes a long-short term memory (LSTM) network-based decoding method for the simultaneous estimation of human motion and interactive force from surface electromyography (sEMG) signals. The surface EMG recorded from the muscles of forearm is used to decode human’s motion intention. In order to extract smooth features from non-stationary sEMG signals, Bayesian filter is applied instead of traditional time-domain feature extraction method. From the real-time experiments on eight subjects, the LSTM-based decoding method represents high accuracy of motion estimates (91.7%) and force estimates (96.1%) despite of the existence of muscle coupling and non-stationary mapping during such constrained tasks. It indicates that the estimated motion and interactive force can be further applied for HRI in accomplishing such constrained tasks.