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KMID : 1137820220430050361
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의공학회지
2022 Volume.43 No. 5 p.361 ~ p.368
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Development and Usability Evaluation of Hand Rehabilitation Training System Using Multi-Channel EMG-Based Deep Learning Hand Posture Recognition
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Ahn Sung-Moo
Lee Gun-Hee
Kim Se-Jin
Bae So-Jeong
Lee Hyun-Ju
Oh Do-Chang
Tae Ki-Sik
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Abstract
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The purpose of this study was to develop a hand rehabilitation training system for hemiplegic patients. We also tried to find out five hand postures (WF: Wrist Flexion, WE: Wrist Extension, BG: Ball Grip, HG: Hook Grip, RE: Rest) in real-time using multi-channel EMG-based deep learning. We performed a pre-processing method that converts to Spider Chart image data for the classification of hand movement from five test subjects (total 1,500 data sets) using Convolution Neural Networks (CNN) deep learning with an 8-channel armband. As a result of this study, the recognition accuracy was 92% for WF, 94% for WE, 76% for BG, 82% for HG, and 88% for RE. Also, ten physical therapists participated for the usability evaluation. The questionnaire consisted of 7 items of acceptance, interest, and satisfaction, and the mean and standard deviation were calculated by dividing each into a 5-point scale. As a result, high scores were obtained in immersion and interest in game (4.6±0.43), con- venience of the device (4.9±0.30), and satisfaction after treatment (4.1±0.48). On the other hand, Con- formity of intention for treatment (3.90±0.49) was relatively low. This is thought to be because the game play may be difficult depending on the degree of spasticity of the hemiplegic patient, and compensation may occur in patient with weakened target muscles. Therefore, it is necessary to develop a rehabilitation program suitable for the degree of disability of the patient.
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KEYWORD
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Convolutional Neural Network (CNN), EMG, Hand rehabilitation, Hand posture recognition, Usability evaluation
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