EMG Fusion Feature Extraction Method Based on Coefficient of Variation
Pages 508 - 513
Abstract
Feature extraction is the key to better application of muscle-machine interface. The selection rationality of a single index is poor, so it is usually necessary to adopt the method of multi-index integration. However, this method will not consider the physical significance or the weight imbalance when selecting indicators, resulting in inappropriate selection, which directly affects the signal processing performance. Based on the idea of multiple eigenvalues obtained by different simultaneous time and frequency feature extraction methods, a feature subset algorithm based on variation coefficient is proposed, and the fitting index system is established according to different time-frequency domain indexes. In the experiment of recognition of 6 kinds of gestures on 10 subjects, the algorithm can effectively reflect the difference between gestures, and the average success rate of recognition of gestures can reach 98.5%.
References
[1]
X. Chen, W. Guo, C. Lin, N. Jiang and J. Su, "Cross-Subject Lifelong Learning for Continuous Estimation From Surface Electromyographic Signal," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, vol. 32, pp. 1965-1973.
[2]
C. Losada, B. Pastrana and R. Rodriguez, "Machine Learning Based Feature Extraction and Classification of Surface EMG Signals in the Time Domain," 2022 V Congreso Internacional en Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), San Jose, Costa Rica, 2022, pp. 1-8.
[3]
J. Zhang, W. Li, S. Du, X. Zhao and H. Sui, "Experimental study of a human-computer evaluation model based on the performance of surface electromyographic massage pillows," 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Baishan, China, 2022, pp. 740-743.
[4]
J. P. G. Calderón, D. F. G. Ruiz and R. R. Serrezuela, "Deep learning of EMG signals in hand grip posture identification using time-frequency domain applying STFT.," 2023 VI Congreso Internacional en Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), Cali, Colombia, 2023, pp. 1-8.
[5]
A. Phinyomark, G. Chujit, P. Phukpattaranont, C. Limsakul and Huosheng Hu, "A preliminary study assessing time-domain EMG features of classifying exercises in preventing falls in the elderly," 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2012, pp. 1-4.
[6]
D. C. Toledo-Pérez, J. Rodríguez-Reséndiz and R. A. Gómez-Loenzo, "A Study of Computing Zero Crossing Methods and an Improved Proposal for EMG Signals," in IEEE Access, vol. 8, pp. 8783-8790, 2020.
[7]
H. Hayashi, A. Furui, Y. Kurita and T. Tsuji, "A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution," in IEEE Transactions on Biomedical Engineering, vol. 64, no. 11, pp. 2672-2681, Nov. 2017.
[8]
B. N. Cahyadi et al., "Analysis of EMG based Arm Movement Sequence using Mean and Median Frequency," 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2018, pp. 440-444.
[9]
M. Ayaz, M. W. Ayub and I. A. Qureshi, "Arduino Based Fatigue Level Measurement in Muscular Activity using RMS Technique," 2020 International Conference on e-Health and Bioengineering (EHB), 2020, pp. 1-4.
[10]
C. Dai, B. Bardizbanian and E. A. Clancy, "Comparison of Constant-Posture Force-Varying EMG-Force Dynamic Models About the Elbow," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 9, pp. 1529-1538, Sept. 2017.
[11]
Z. Arief, I. A. Sulistijono and R. A. Ardiansyah, "Comparison of five time series EMG features extractions using Myo Armband," 2015 International Electronics Symposium (IES), 2015, pp. 11-14.
[12]
M. Lyu, C. Xiong and Q. Zhang, "Electromyography (EMG)-based Chinese voice command recognition," 2014 IEEE International Conference on Information and Automation (ICIA), 2014, pp. 926-931.
[13]
T. Puttasakuf, M. Sangworasil and T. Matsuura, "Feature extraction of wavelet transform coefficients for EMG classification," The 7th 2014 Biomedical Engineering International Conference, 2014, pp. 1-4.
[14]
A. B. M. S. U. Doulah and S. A. Fattah, "Neuromuscular disease classification based on mel frequency cepstrum of motor unit action potential," 2014 International Conference on Electrical Engineering and Information & Communication Technology, 2014, pp. 1-4.
[15]
N. D. Linhares and A. O. Andrade, "Parametric EMG muscle activity detection based on MAV and sample entropy," 5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 2014, pp. 1-6.
[16]
F. Di Nardo, M. Morano and S. Fioretti, "Quantification of ankle muscle co-contraction during early stance by wavelet-based analysis of surface electromyographic signals," 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Messina, Italy, 2022, pp. 1-5.
[17]
M. Abdu Alomar, "Wavelet Transform and Cascaded Feed Forward Neural Network Analysis of Electromyographic Data For Involuntary Detection of Hand Gesticulation," 2023 1st International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP), BHOPAL, India, 2023, pp. 402-406.
[18]
Y. Zou, L. Cheng, L. Han, Z. Li and L. Song, "Decoding Electromyographic Signal With Multiple Labels for Hand Gesture Recognition," in IEEE Signal Processing Letters, 2023, vol. 30, pp. 483-487.
[19]
G. Shi, P. Hu, J. Chen, C. Li, H. Zhou and Y. Ma, "Wavelet De-noising Method Analysis of Pipeline Magnetic Flux Leakage In-line Inspection Based on Coefficient of Variation," 2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE), Wuhan, China, 2022, pp. 1-5.
[20]
Round W H, Sleigh J W. Studies of the coefficient of variation of the magnitude of EEG signals [A]. // 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society [C], Istanbul, Turkey: IEEE, 2001, 873-876.
[21]
S. G. K., A. P. Vinod and R. Subasree, "A Phase-based EEG Epoch Selection Method for Decoding Bi-directional Hand Movement Imagination in Stroke Patients," 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 2023, pp. 1-4.
Index Terms
- EMG Fusion Feature Extraction Method Based on Coefficient of Variation
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August 2024
967 pages
ISBN:9798400717826
DOI:10.1145/3706890
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Published: 13 January 2025
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ISAIMS 2024
ISAIMS 2024: 2024 5th International Symposium on Artificial Intelligence for Medicine Science
August 13 - 17, 2024
Amsterdam, Netherlands
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