[논문]근전도 기반의 Spider Chart와 딥러닝을 활용한 일상생활 잡기 손동작 분류

이성문; 피승훈; 한승호; 조용운; 오도창

doi:10.9718/jber.2022.43.5.299

근전도 기반의 Spider Chart와 딥러닝을 활용한 일상생활 잡기 손동작 분류
Classification of Gripping Movement in Daily Life Using EMG-based Spider Chart and Deep Learning 원문보기

Journal of biomedical engineering research : the official journal of the Korean Society of Medical & Biological Engineering, v.43 no.5, 2022년, pp.299 - 307

이성문 (건양대학교 의공학부) , 피승훈 (건양대학교 의공학부) , 한승호 (건양대학교 의공학부) , 조용운 (건양대학교 의공학부) , 오도창 (건양대학교 의공학부)

Abstract ▼ AI-Helper

In this paper, we propose a pre-processing method that converts to Spider Chart image data for classification of gripping movement using EMG (electromyography) sensors and Convolution Neural Networks (CNN) deep learning. First, raw data for six hand gestures are extracted from five test subjects using an 8-channel armband and converted into Spider Chart data of octagonal shapes, which are divided into several sliding windows and are learned. In classifying six hand gestures, the classification performance is compared with the proposed pre-processing method and the existing methods. Deep learning was performed on the dataset by dividing 70% of the total into training, 15% as testing, and 15% as validation. For system performance evaluation, five cross-validations were applied by dividing 80% of the entire dataset by training and 20% by testing. The proposed method generates 97% and 94.54% in cross-validation and general tests, respectively, using the Spider Chart preprocessing, which was better results than the conventional methods.

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표/그림 (12)

그림 그림 1. 데이터 수집을 위해 선정한 손동작 Fig. 1. Selected hand gestures for data collection ((a) Cylindrical: Carrying object that are clindrical, (b) Tip: Holding tiny object, (c) Hook: Holding a heavy item, (d) Palmar: Gripping object with palm facing it, (e) Spherical: Carrying spherical objects, (f) Lateral : Carrying flat objects)
그림 그림 2. Sliding window의 크기 Fig. 2. Size of sliding window
그림 그림 3. Hook 1회 손동작 시 (a) 원시 신호 데이터와 (b) 1개 윈도우 데이터 Fig. 3. (a) Raw signal data and (b) 1 window data when hook 1 hand gesture
그림 그림 4. 6개 손동작별 Spider Chart 모형 Fig. 4. Spider Chart shapes by 6 gestures
그림 그림 5. 손동작 분류를 위한 CNN의 구조도 Fig. 5. Structural diagram of CNN for hand gesture classification
그림 그림 6. Confusion matrix 결과 Fig. 6. Confusion matrix result
표 표 1. Confusion matrix 결과 Table 1. Confusion matrix results
표 표 2. 전처리 과정에 따른 Confusion matrix 결과Table 2. Results of confusion matrix for pre-processing
그림 그림 7. K-Fold Cross Validation Fig. 7. K-Fold Cross Validation
표 표 3. CNN 결과 Table 3. Results of CNN
표 표 4. Cross-Validation 결과 Table 4. Results of Cross-Validation
표 표 5. 선행 연구와의 비교 Table 5. Comparison with previous studies

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