Human Movement Modeling to Detect Biosignal Sensor Failures for Myoelectric Assistive Robot Control

In this study, we propose a human movement model both for myoelectric assistive robot control and biosignal-sensor-failure detection. We particularly consider an application to upper extremity exoskeleton robot control. When using electromyography (EMG)-based assistive robot control, EMG electrodes can be easily disconnected or detached from skin surfaces because the human body is always in contact with the robot. If multiple electrodes are used to estimate multiple joint movements, the probability of sensor electrode misplacement increases due to human error. To cope with the aforementioned issues, we propose a novel human movement estimation model that takes anomalies into account as uncertain observations. We estimated human joint torques by automatically modulating the contribution of each sensor channel for the movement estimation based on anomaly scores that were computed according to synergistic muscular coordination. We compared our proposed method with conventional approaches during drinking-movement estimation with five healthy subjects in the three aforementioned anomaly situations and showed the effectiveness of our proposed method. We applied it to a four-DOF upper limb assistive exoskeleton robot and showed proper control in sensor failure situations.