Variable impedance interaction and demonstration interface design based on measurement of arm muscle co-activation for demonstration learning

Real-time modeling and feature extraction method of surface electromyography signal for hand movement classification based on oscillatory theory

OBJECTIVE

Research of surface electromyography (sEMG) signal modeling and feature extraction is important in human motion intention recognition, prosthesis and exoskeleton robot. However, the existing methods mostly use the signal segmentation processing way rather than point-to-point signal processing way and lack physiological mechanism support.

APPROACH

In this study, a real-time sEMG signal modeling and separation method is developed based on oscillatory theory. On this basis, a sEMG signal feature extraction method is constructed and ensemble learning method is combined to achieve the real-time human hand motion intention recognition.

MAIN RESULTS

The experimental results show that the average root mean square difference (RMSD) value of sEMG signal modeling is 0.3838±0.0591, and the average accuracy of human hand motion intention recognition is 96.03±1.74%. On a computer with an Intel (R) Core (TM) i5-8250U CPU running Matlab 2016Rb, the execution time for the sEMG signal with an actual duration of 2 second is 0.66 second.

SIGNIFICANCE

Compared with several existing methods, the proposed method has better modeling accuracy, motion intention recognition accuracy and real-time performance. The method developed in this study may provide a new perspective of sEMG modeling and feature extraction for hand movement classification.

Variable Impedance Control and Learning-A Review

Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and proficient manner. Impedance control is considered as a prominent approach in robotics to avoid large impact forces while operating in unstructured environments. In such environments, the conditions under which the interaction occurs may significantly vary during the task execution. This demands robots to be endowed with online adaptation capabilities to cope with sudden and unexpected changes in the environment. In this context, variable impedance control arises as a powerful tool to modulate the robot's behavior in response to variations in its surroundings. In this survey, we present the state-of-the-art of approaches devoted to variable impedance control from control and learning perspectives (separately and jointly). Moreover, we propose a new taxonomy for mechanical impedance based on variability, learning, and control. The objective of this survey is to put together the concepts and efforts that have been done so far in this field, and to describe advantages and disadvantages of each approach. The survey concludes with open issues in the field and an envisioned framework that may potentially solve them.