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
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초록
In this paper, we propose a pre-processing method that converts to Spider Chart image data for classi- fication 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 train- ing, 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.
Abstract
In this paper, we propose a pre-processing method that converts to Spider Chart image data for classi- fication 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 train- ing, 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.
참고문헌 (20)
- Guo W, "Toward an Enhanced Human–Machine Interface for Upper-Limb Prosthesis Con-trol With Combined EMG and NIRS Signals", IEEE Transac-tions on Human-Machine Systems, 47(4), pp.564~575 (2017)
- Rautaray SS, "Vision based hand gesture recognition for human computer interaction : a survey", Artificial intelli-gence review, 43(1), pp.1~54 (2015)
- Sagayam KM, "Hand posture and gesture rec-ognition techniques for virtual reality applications : a survey", Virtual Reality, 21(2), pp.91~107 (2017)
- Liang H, "Ar in hand:Egocentric palm pose tracking and gesture recognition for augmented reality applications", Proceedings of the 23rd ACM international conference on Multimedia, pp.743~744 (2015)
- Andrianesis K, "Design of an anthropomorphic pros-thetic hand driven by shape memory alloy actuators", IEEE RAS & EMBS International Conference on Bio-medical Robotics and Biomechatronics, pp.517~ (2008)
- Lucas MF, "Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization", Biomedical Signal Processing and Control, 3(2), pp.169~174 (2008)
- Masum H, "Spider Chart based Pictographic Image Comparison in Walking Speed Estimation", 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), pp.1~4 (2019)
- Dalmazzo D, "Bowing Gestures Classification in Violin Performance: A Machine Learning Approach", Fron-tiers in Psychology, 10 (2019)
- Kopuklu O, "Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition", Pro-ceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp.2103~2111 (2018)
- Chen Z, "Dynamic Hand Ges-ture Classification Based on Multistatic Radar Micro-Dop-pler Signatures Using Convolutional Neural Network", 2019IEEE Radar Conference (RadarConf), pp.1~5 (2019)
- Jarque-Bou NJ, "Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset", Journal of NeuroEngineering and Rehabilitation, 63(19) (2019)
- Bakircioğlu K, "Classification of EMG signals using convolution neural network", International Journal of Applied Mathematics Electronics and Computers, 8(4), pp.115~119 (2020)
- Zhu H, "IMU Motion Capture Method with Adaptive Tremor Attenuation in Teleoperation Robot System", Sensors, 22(9), pp.3353~ (2022)
- Pisharady PK, "Recent methods and databases invision-based hand gesture recognition : A review", Computer Vision and Image Understanding, 141, pp.152~165 (2015)
- Shen S, "Movements Classification of Multi-Channel sEMG Based on CNN and Stacking Ensemble Learning", IEEE Access, 7, pp.137489~137500 (2019)
- Yang C, "Teleoperated robot writing using EMG signals", 2015 IEEE International Confer-ence on Information and Automation, pp.2264~2269 (2015)
- Farina D, "Assessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions", IEEE Trans-actions on Biomedical Engineering, 51(8), pp.1383~1393 (2004)
- Mizuno H, "Forearm motion discrimina-tion technique using real-time EMG signals", 2011 Annual International Conference of the IEEE Engineering in Medi-cine and Biology Society, pp.4435~4438 (2011)
- 조용운, "EMG 데이터 취득 구간별 딥러닝을 이용한 실시간 손동작 예측", 제어.로봇.시스템학회 논문지, 27(5), pp.349~355 (2021), 제어·로봇·시스템학회[DOI]
- Wong WK, "Multi-Features Capaci-tive Hand Gesture Recognition Sensor : A Machine Learning Approach", IEEE Sensors Journal, 21(6), pp.8441~8450 (2021)