Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics

• Published in: Frontiers in neurorobotics Vol. 15; p. 659876
• Main Authors: Zhou, Hui, Zhang, Qianqian, Zhang, Mengjun, Shahnewaz, Sameer, Wei, Shaocong, Ruan, Jingzhi, Zhang, Xinyan, Zhang, Lingling
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 13.05.2021; Frontiers Media S.A
• Subjects: Accuracy; Activities of daily living; Classification; Electromyography; EMG; Experiments; hand pattern recognition; Kinematics; machine learning; Medical research; Motion capture; Neuroscience; Pattern recognition; Pattern recognition systems; Rehabilitation; Robotics; Robots; sensor fusion; Sensors; Stroke; United States > US; San Francisco California
• ISSN: 1662-5218; 1662-5218
• DOI: 10.3389/fnbot.2021.659876

Wearable hand robots are becoming an attractive means in the facilitating of assistance with daily living and hand rehabilitation exercises for patients after stroke. Pattern recognition is a crucial step toward the development of wearable hand robots. Electromyography (EMG) is a commonly used biological signal for hand pattern recognition. However, the EMG based pattern recognition performance in assistive and rehabilitation robotics post stroke remains unsatisfactory. Moreover, low cost kinematic sensors such as Leap Motion is recently used for pattern recognition in various applications. This study proposes feature fusion and decision fusion method that combines EMG features and kinematic features for hand pattern recognition toward application in upper limb assistive and rehabilitation robotics. Ten normal subjects and five post stroke patients participating in the experiments were tested with eight hand patterns of daily activities while EMG and kinematics were recorded simultaneously. Results showed that average hand pattern recognition accuracy for post stroke patients was 83% for EMG features only, 84.71% for kinematic features only, 96.43% for feature fusion of EMG and kinematics, 91.18% for decision fusion of EMG and kinematics. The feature fusion and decision fusion was robust as three different levels of noise was given to the classifiers resulting in small decrease of classification accuracy. Different channel combination comparisons showed the fusion classifiers would be robust despite failure of specific EMG channels which means that the system has promising potential in the field of assistive and rehabilitation robotics. Future work will be conducted with real-time pattern classification on stroke survivors.