[논문]다채널 근전도 기반 딥러닝 동작 인식을 활용한 손 재활 훈련시스템 개발 및 사용성 평가

안성무; 이건희; 김세진; 배소정; 이현주; 오도창; 태기식

doi:10.9718/jber.2022.43.5.361

[국내논문] 다채널 근전도 기반 딥러닝 동작 인식을 활용한 손 재활 훈련시스템 개발 및 사용성 평가
Development and Usability Evaluation of Hand Rehabilitation Training System Using Multi-Channel EMG-Based Deep Learning Hand Posture Recognition 원문보기

Journal of biomedical engineering research : the official journal of the Korean Society of Medical & Biological Engineering, v.43 no.5, 2022년, pp.361 - 368

안성무 (건양대학교 의공학과) , 이건희 (건양대학교 의공학과) , 김세진 (건양대학교 의공학과) , 배소정 (건양대학교 물리치료학과) , 이현주 (건양대학교 물리치료학과) , 오도창 (건양대학교 의공학과) , 태기식 (건양대학교 의공학과)

Abstract ▼ AI-Helper

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), convenience of the device (4.9±0.30), and satisfaction after treatment (4.1±0.48). On the other hand, Conformity 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.

주제어

표/그림 (10)

그림 그림 1. 전체 시스템 블록도 Fig. 1. Block diagram of system
그림 그림 2. 데이터 수집을 위해 선정한 손 동작Fig 2. Selected hand gestures for data collection
표 표 1. 브룬스트롬 손 회복 단계 Table 1. Brunnstrom stages for hand recovery hand recovery stage
그림 그림 3. Myo armband 착용 위치 Fig. 3. Myo armband wearing position
그림 그림 4. 최종 데이터 형태 Fig. 4. Final data form
그림 그림 5. CNN 모델 구조 Fig. 5. CNN model structure
표 표 2. 모델 레이어 정의 및 구조 Table 2. Definition and structure of model layer
그림 그림 6. 재활 게임 구동(좌측 : 가위바위보 게임, 우측 : DDR 게임) Fig. 6. Driving a rehabilitation game(Left : rock-paper-scissors, right: DDR)
표 표 3. 5개 손동작 인식 정확도 Table 3. Prediction accuracy of 5 hand postures
표 표 4. 사용성 평가 결과 Table 4. The result of usability evaluation

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