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sEMG-based continuous hand gesture recognition using GMM-HMM and threshold model | IEEE Conference Publication | IEEE Xplore

sEMG-based continuous hand gesture recognition using GMM-HMM and threshold model


Abstract:

To detect the person's intention for the control of upper-limb exoskeleton robot, we propose a recognition frame of continuous hand gestures. This frame is mainly concent...Show More

Abstract:

To detect the person's intention for the control of upper-limb exoskeleton robot, we propose a recognition frame of continuous hand gestures. This frame is mainly concentrated on dynamic segmentation and real time gesture recognition based on sEMG. The hand gesture was modeled and decomposed by the use of Gaussian Mixture Model-Hidden Markov Models (GMM-HMM). GMMs are employed as a sub-states of HMMs to decode sEMG feature of gesture. The log-likelihood threshold and KL-divergence threshold are adopted to select target gesture model. In myoelectric control schemes, the sEMG data are collected by Myo armband 8-channels sEMG sensors. The proposed framework has ideal classification accuracy and its simpler acquisition armband make it attractive to a real-time myoelectric control system.
Date of Conference: 05-08 December 2017
Date Added to IEEE Xplore: 26 March 2018
ISBN Information:
Conference Location: Macau, Macao
Citations are not available for this document.

I. Introduction

The surface electromyography (sEMG) signal contains useful information about motion intension that is extensively used for control of the prosthetic hand. However, sEMG signal is a semi-stochastic signal whose dynamic changing process is influenced by anatomical and physiological properties of the contracting muscle [1]. Furthermore, there would be other factors that have impacts on the sEMG signals. Firstly, sEMG sensors with different placements and contact conditions might collect the distinct signals. Secondly, kinetic differences between different individuals when perform the same hand gesture might result in vague corresponding relationship between sEMG and hand gesture [2]. Thirdly, due to muscles collaboration or interaction together during dynamic contractions, coordinated activation of groups of muscles might be non-stationary [3].

Cites in Papers - |

Cites in Papers - IEEE (4)

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1.
Wenyao Zhu, Zhenbang Liu, Yizhi Chen, Dejiu Chen, Zhonghai Lu, "Amputee Gait Phase Recognition Using Multiple GMM-HMM", IEEE Access, vol.12, pp.193796-193806, 2024.
2.
Haomeng Qiu, Zhitao Chen, Yan Chen, Chaojie Yang, Sihan Wu, Fanglin Li, Longhan Xie, "A Real-Time Hand Gesture Recognition System for Low-Latency HMI via Transient HD-SEMG and In-Sensor Computing", IEEE Journal of Biomedical and Health Informatics, vol.28, no.9, pp.5156-5167, 2024.
3.
Adilbek Turgunov, Kudratjon Zohirov, Rashid Nasimov, Sanjar Mirzakhalilov, "Comparative Analysis of the Results of EMG Signal Classification Based on Machine Learning Algorithms", 2021 International Conference on Information Science and Communications Technologies (ICISCT), pp.1-4, 2021.
4.
Chuan-Feng Chiu, Timothy K. Shih, Chi-Yen Lin, Lin Hui, Fitri Utaminingrum, Tsai-Ni Yang, "Application of Hand Recognition System Based on Electromyography and Gyroscope Using Deep Learning", 2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media), pp.96-101, 2019.

Cites in Papers - Other Publishers (12)

1.
HanFan Liu, Pei Tian, HaiFang Yao, Cong Shen, FengYang Hu, "Research on fastDTW and isolation forest-based lightweight gesture authentication", International Journal of Computers and Applications, pp.1, 2024.
2.
Majed M. Alwateer, Mahmoud Elmezain, Mohammed Farsi, Elsayed Atlam, "Hidden Markov Models for Pattern Recognition", Markov Model [Working Title], 2023.
3.
Noor Fadel, Emad I. Abdul Kareem, "Computer Vision Techniques for Hand Gesture Recognition: Survey", New Trends in Information and Communications Technology Applications, vol.1764, pp.50, 2023.
4.
Alonso A. Cifuentes-Cuadros, Enzo Romero, Sebastian Caballa, Daniela Vega-Centeno, Dante A. Elias, "The LIBRA NeuroLimb: Hybrid Real-Time Control and Mechatronic Design for Affordable Prosthetics in Developing Regions", Sensors, vol.24, no.1, pp.70, 2023.
5.
Mirco Vangi, Chiara Brogi, Alberto Topini, Nicola Secciani, Alessandro Ridolfi, "Enhancing sEMG-Based Finger Motion Prediction with CNN-LSTM Regressors for Controlling a Hand Exoskeleton", Machines, vol.11, no.7, pp.747, 2023.
6.
Beyza Eraslan, Kutlucan Gorur, Feyzullah Temurtas, "Novel Biometric Approach Based on Diaphragmatic Respiratory Movements Using Single-Lead EMG Signals", IETE Journal of Research, pp.1, 2023.
7.
Kaikui Zheng, Shuai Liu, Jinxing Yang, Metwalli Al-Selwi, Jun Li, "sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning", Sensors, vol.22, no.24, pp.9949, 2022.
8.
Zengyu Qing, Zongxing Lu, Yingjie Cai, Jing Wang, "Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time", Sensors, vol.21, no.22, pp.7713, 2021.
9.
Cengiz Tepe, Mehmet Can Demir, "The effects of the number of channels and gyroscopic data on the classification performance in EMG data acquired by Myo armband", Journal of Computational Science, vol.51, pp.101348, 2021.
10.
Andres Jaramillo-Yanez, Marco E. Benalcazar, Elisa Mena-Maldonado, "Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review", Sensors, vol.20, no.9, pp.2467, 2020.
11.
Doreen Jirak, Stephan Tietz, Hassan Ali, Stefan Wermter, "Echo State Networks and Long Short-Term Memory for Continuous Gesture Recognition: a Comparative Study", Cognitive Computation, 2020.
12.
Cengiz TEPE, Mehmet Can DEMİR, "Detection and Classification of Muscle Activation in EMG Data Acquired by Myo Armband", European Journal of Science and Technology, pp.178, 2020.

References

References is not available for this document.