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Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics by Hui Zhou; Qianqian Zhang; Mengjun Zhang; Sameer Shahnewaz; Shaocong Wei; Jingzhi Ruan; Xinyan Zhang; Lingling Zhang is a Engineering article available to read on EtoBox.

What is Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics about?

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 kin

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Author

Hui Zhou; Qianqian Zhang; Mengjun Zhang; Sameer Shahnewaz; Shaocong Wei; Jingzhi Ruan; Xinyan Zhang; Lingling Zhang

Publisher

Frontiers Media SA

Published

2021

Language

EN

Field

Engineering (Physical Sciences)