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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

An expert system for detection of key pinch and tripod hand grip based on sEMG signals

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pp. 991–1003Vol. 45Issue 4May 2024DOI: 10.47974/JIOS-1622XML
Published Online:
08 Jun 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1622
Pages:
991–1003

Abstract

This work presents a system to recognize key pinch and tripod hand grips using surface electromyography. This system made use of sEMG signals collected from 8 subjects for training and testing, making use of a 4-channel system designed to recognize Key Pinch and Tripod Pinch hand grips. Signals were pre-processed using band pass filter and notch filter, and a time domain feature extraction was performed from these signals, namely Mean Absolute Value, Standard Deviation and Wilson Amplitude. This work implements and compares 5 different classifications techniques namely Random Forest, Decision Tree, Logistic Regression, KNN and Adaboost. Random Forest provided the highest testing accuracy of gesture recognition at 88.68% and a precision of 88.88%. The system correctly recognizes key pinch and tripod hand gestures.

Keywords

Subject Classifications

Primary 94A12Secondary 62H15

References

[1] Li, Wei, Ping Shi, and Hongliu Yu. “Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future.” Frontiers in neuroscience 15 : 621885 (2021). 
[2] Manimegalai, E. H. “Hand gesture recognition based on EMG signals using ANN.” Int J Comput Appl 3.2 : 31-9 (2013).
[3] Park, Ki-Hee, and Seong-Whan Lee. “Movement intention decoding based on deep learning for multiuser myoelectric interfaces.” 2016 4th international winter conference on brain-computer Interface (BCI). IEEE (2016).
[4] Tsagkas, N., et al. ‘On the Use of Deeper CNNs in Hand Grip Recognition Based on sEMG Signals’. 10th International Conference on Information, Intelligence, Systems and Applications, pp. 1–4 (2019).
[5] Yang, K., and Z. Zhang. ‘Real-Time Pattern Recognition for Hand Grip Based on ANN and Surface EMG’. IEEE 8th Joint International Information Technology and Artificial Intelligence Conference, pp. 799–802 (2019).
[6] Castro, Maria, et al. ‘Selection of Suitable Hand Grips for Reliable Myoelectric Human Computer Interface’. Biomedical Engineering Conference, pp. 1–11 (2015).
[7] Ceolini, Enea, et al. “Hand-gesture recognition based on EMG and event-based camera sensor fusion: A benchmark in neuromorphic computing.” Frontiers in neuroscience 14 : 637 (2020).
[8] Chen, X., X. Zhang, et al. ‘Multiple Hand Grip Recognition Based on Surface EMG Signal’. 1st International Conference on Bioinformatics and Biomedical Engineering, pp. 506–509 (2007).
[9] Chen, X., Y. Li, et al. ‘Hand Grip Recognition Based on Surface Electromyography Using Convolutional Neural Network with Transfer Learning Method’. IEEE Journal of Biomedical and Health Informatics,  pp. 1292–1304 (2021).
[10] Shi, Wan-Ting, et al. “A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study.” Biocybernetics and Biomedical Engineering 38.1 : 126-135 (2018).
[11] Samadani, A. ‘Gated Recurrent Neural Networks for EMG-Based Hand Grip Classification. A Comparative Study’. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1–4 (2018).
[12] Wang, Q., and X. Wang. ‘EMG-Based Hand Grip Recognition by Deep Time-Frequency Learning for Assisted Living & Rehabilitation’. 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, pp. 558–561 (2020).
[13] Barioul, R., et al. ‘Evaluation of EMG Signal Time Domain Features for Hand Grip Distinction’. 16th International Multi-Conference on Systems, Signals & Devices, pp. 489–493 (2019).
[14] Rahimian, E., et al. ‘Hybrid Deep Neural Networks for Sparse Surface EMG-Based Hand Grip Recognition’. 54th Asilomar Conference on Signals, Systems, and Computers, pp. 371–374 (2020).
[15] Raurale, Sumit A., et al. ‘EMG Biometric Systems Based on Different Wrist-Hand Movements’. IEEE Access: Practical Innovations, Open Solutions, vol. 9, Institute of Electrical and Electronics Engineers (IEEE), pp. 12256–12266 (2021).
[16] Jia, G., et al. ‘Classification of Electromyographic Hand Grip Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach’. IEEE Transactions on Neural Systems and Rehabilitation Engineering, pp. 1428–1435 (2020).
[17] Maragliulo, S., et al. ‘Foot Grip Recognition Through Dual Channel Wearable EMG System’. IEEE Sensors Journal, pp. 10187–10197 (2019).
[18] Bairagi, Vinayak K., and Varsha K. Harpale. ‘Improved Epileptic Seizure Detection Using Singular Spectrum Empirical Mode Decomposition and Machine Learning Approach’. Journal of Statistics and Management Systems, vol. 25, no. 1, Informa UK Limited, pp. 103–123 Jan. (2022).
[19] Khandelwal, Jyoti, and Vijay Kumar Sharma. ‘Extensive Dual Tree Complex Wavelet Transform-Based Image Steganography Using SVD and CNN Subspace’. Journal of Discrete Mathematical Sciences & Cryptography, vol. 26, no. 3, Taru Publications, pp. 617–627 (2023).
[20] Jain, S., Kumar, S., Sharma, V. K., & Poonia, R. C. Peregrine preying pattern based differential evolution for robot path planning. Journal of Interdisciplinary Mathematics, 23(2), 555–562 (2020). 
[21] Saini, Dilip Kumar Jang Bahadur, et al. ‘Improve QoS for Multi-Body Sensor Analytics in Smart Healthcare System Using Machine Learning Algorithm’. Journal of Interdisciplinary Mathematics, vol. 26, no. 3, Taru Publications, pp. 393–405 (2023).

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