title: An upper-limb power-assist exoskeleton using proportional myoelectric control.
abstract: We developed an upper-limb power-assist exoskeleton actuated by pneumatic muscles. The exoskeleton included two metal links: a nylon joint, four size-adjustable carbon fiber bracers, a potentiometer and two pneumatic muscles. The proportional myoelectric control method was proposed to control the exoskeleton according to the user's motion intention in real time. With the feature extraction procedure and the classification (back-propagation neural network), an electromyogram (EMG)-angle model was constructed to be used for pattern recognition. Six healthy subjects performed elbow flexion-extension movements under four experimental conditions: (1) holding a 1-kg load, wearing the exoskeleton, but with no actuation and for different periods (2-s, 4-s and 8-s periods); (2) holding a 1-kg load, without wearing the exoskeleton, for a fixed period; (3) holding a 1-kg load, wearing the exoskeleton, but with no actuation, for a fixed period; (4) holding a 1-kg load, wearing the exoskeleton under proportional myoelectric control, for a fixed period. The EMG signals of the biceps brachii, the brachioradialis, the triceps brachii and the anconeus and the angle of the elbow were collected. The control scheme's reliability and power-assist effectiveness were evaluated in the experiments. The results indicated that the exoskeleton could be controlled by the user's motion intention in real time and that it was useful for augmenting arm performance with neurological signal control, which could be applied to assist in elbow rehabilitation after neurological injury.
authors.affiliation: College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China. ttzzcc@163.com.
authors.forename: Zhichuan
authors.initials: Z
authors.lastname: Tang
authors.affiliation: College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China. channy@zju.edu.cn.
authors.forename: Kejun
authors.initials: K
authors.lastname: Zhang
authors.affiliation: College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China. ssq@zju.edu.cn.
authors.forename: Shouqian
authors.initials: S
authors.lastname: Sun
authors.affiliation: College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China. gzg@zju.edu.cn.
authors.forename: Zenggui
authors.initials: Z
authors.lastname: Gao
authors.affiliation: College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China. zlkzhang@gmail.com.
authors.forename: Lekai
authors.initials: L
authors.lastname: Zhang
authors.affiliation: College of Mechanical Engineering, Donghua University, Shanghai 201620, China. yzl@dhu.edu.cn.
authors.forename: Zhongliang
authors.initials: Z
authors.lastname: Yang
mesh_terms: D000328:Adult; D004551:Elbow Joint / Q000502:physiology; D011211:Electric Power Supplies; D004576:Electromyography / Q000295:instrumentation*; D006801:Humans; D008297:Male; D009132:Muscles / Q000502:physiology; D016571:Neural Networks, Computer; D012044:Regression Analysis; D034941:Upper Extremity / Q000502:physiology*
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