Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pre-collision safety strategies for human-robot interaction
Autonomous Robots
Technology and Health Care - Synergy of Informatics and Biology - Grand Challenge of Bio-nantechnology Based Future Biomedical Engineering
A Human--Exoskeleton Interface Utilizing Electromyography
IEEE Transactions on Robotics
A myosignal-based powered exoskeleton system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.