An estimation model of human dynamic arm strength with joint rotation compensation. (July 2021) - Zhang, Tie; Sun, Hanlei; Zou, Yanbiao; Xiao, Meng
Highlights: Human arm strength estimation model based on EMG signal considering joint rotation. The influence of joint rotation is eliminated to improve the accuracy of the model. Forgetting parameter is introduced to improve the real-time performance of the model. The model can be used as a human-robot collaboration interface. Abstract: The measurement of human arm strength is of great significance for human-robot collaboration. As compared with the measurement of human arm strength with force sensors, the estimation of arm strength via electromyogram (EMG) signals is more flexible and convenient, but the corresponding application in industrial field is restricted due to joint rotation and time delay. In order to solve this problem, an estimation model of dynamic arm strength with joint rotation compensation (JRC) is proposed, in which a forgetting parameter is introduced to optimize the feature extraction method, and a deep learning model of joint rotation and EMG signal is constructed to eliminate the impact of joint rotation. Experimental results show that, as compared with the traditional parallel cascade identification (PCI) model, the proposed estimation model is of higher accuracy. Human-robot collaborative sawing experiment indicates that the proposed model can be used as a human-robot collaboration interface.