Fatigue Diagnosis Utilizing the Support Vector Machine Classification of Wrist Electromyography Signals with Feature Selection

[1] S. Hussain, P.K. Jamwal, P. Van Vliet, M.H.J.I.T.o.H.-M.S. Ghayesh, State-of-the-art robotic devices for wrist rehabilitation: Design and control aspects, 50(5) (2020) 361-372.

[2] A. Abbasi Moshaii, M. Mohammadi Moghaddam, V.J.I.R.t.i.j.o.r.r. Dehghan Niestanak, Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger, Industrial Robot: the international journal of robotics research, 46(6) (2019) 839-850.

[3] B. Saeedi, M. Alizadeh, M.M. Moghaddam, M. Sadedel, Design of a Nonlinear Backstepping Versus Sliding Mode Controller for a Human Musculoskeletal arm Model in Sagittal plane, in:  2022 8th International Conference on Control, Instrumentation and Automation (ICCIA), 2022, pp. 1-6.

[4] A.J. Young, D.P.J.I.T.o.N.S. Ferris, R. Engineering, State of the art and future directions for lower limb robotic exoskeletons, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(2) (2016) 171-182.

[5] L. Marchal-Crespo, D.J.J.J.o.n. Reinkensmeyer, rehabilitation, Review of control strategies for robotic movement training after neurologic injury, Journal of neuroengineering and rehabilitation, 6(1) (2009) 20.

[6] S. Adineh, M. Sadedel, M.J.I.J.o.M.E.T.o.I. Moghaddam, Impedance Control of a 6 DoF Robot for Upper-Limb Rehabilitation of Disabled Children Aim to Facilitate Drawing Geometrical Shapes, Iranian Journal of Mechanical Engineering Transactions of ISME,  (2023).

[7] A. Ghasemi, M. Sadedel, M.M.J.P.o.t.I.o.M.E. Moghaddam, Part H: Journal of Engineering in Medicine, A wearable system to assist impaired-neck patients: Design and evaluation, 238(1) (2024) 63-77.

[8] L. Bi, C.J.B.S.P. Guan, Control, A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration, 51 (2019) 113-127.

[9] C. Fang, B. He, Y. Wang, J. Cao, S.J.B. Gao, EMG-centered multisensory-based technologies for pattern recognition in rehabilitation: state of the art and challenges, Biosensors, 10(8) (2020) 85.

[10] R. Clark, T. Dickinson, J. Loaiza, D.W. Geiger, S.K.J.J.o.B.E. Charles, Tracking joint angles during whole-arm movements using electromagnetic sensors, Journal of Biomechanical Engineering, 142(7) (2020) 074502.

[11] N. Nazmi, M.A.A. Rahman, S.-I. Yamamoto, S.A.J.B.S.P. Ahmad, Control, Walking gait event detection based on electromyography signals using artificial neural network, 47 (2019) 334-343.

[12] M. Arozi, W. Caesarendra, M. Ariyanto, M. Munadi, J.D. Setiawan, A.J.S. Glowacz, Pattern recognition of single-channel sEMG signal using PCA and ANN method to classify nine hand movements, 12(4) (2020) 541.

[13] E. Scheme, K.J.J.o.R.R. Englehart, Development, Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use, Journal of Rehabilitation Research & Development 48(6) (2011).

[14] F. Sadikoglu, C. Kavalcioglu, B.J.P.c.s. Dagman, Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease, Procedia computer science, 120 (2017) 422-429.

[15] S. Tahmid, J.M. Font-Llagunes, J.J.J.o.B.E. Yang, Upper Extremity Muscle Activation Pattern Prediction Through Synergy Extrapolation and Electromyography-Driven Modeling, Journal of Biomechanical Engineering, 146(1) (2024).

[16] Y. Zhao, Z. Li, Z. Zhang, K. Qian, S.J.B.S.P. Xie, Control, An EMG-driven musculoskeletal model for estimation of wrist kinematics using mirrored bilateral movement, Biomedical Signal Processing and Control, 81 (2023) 104480.

[17] I. Kang, P. Kunapuli, H. Hsu, A.J. Young, Electromyography (EMG) signal contributions in speed and slope estimation using robotic exoskeletons, in:  2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), IEEE, 2019, pp. 548-553.

[18] O.W. Samuel, M.G. Asogbon, Y. Geng, A.H. Al-Timemy, S. Pirbhulal, N. Ji, S. Chen, P. Fang, G.J.I.A. Li, Intelligent EMG pattern recognition control method for upper-limb multifunctional prostheses: advances, current challenges, and future prospects, Ieee Access, 7 (2019) 10150-10165.

[19] F. Pérez-Reynoso, N. Farrera-Vazquez, C. Capetillo, N. Méndez-Lozano, C. González-Gutiérrez, E.J.S. López-Neri, Pattern recognition of EMG signals by machine learning for the control of a manipulator robot, Sensors 22(9) (2022) 3424.

[20] D. Farina, I. Vujaklija, R. Brånemark, A.M. Bull, H. Dietl, B. Graimann, L.J. Hargrove, K.-P. Hoffmann, H. Huang, T.J.N.b.e., Ingvarsson, Toward higher-performance bionic limbs for wider clinical use, Nature Biomedical Engineering, 7(4) (2023) 473-485.

[21] X. Li, J. Liu, Y. Huang, D. Wang, Y.J.M. Miao, Human motion pattern recognition and feature extraction: An approach using multi-information fusion, Micromachines 13(8) (2022) 1205.

[22] S.T. Leitkam, T.J.J.o.B.E. Reid Bush, Comparison between healthy and reduced hand function using ranges of motion and a weighted fingertip space model, Journal of Biomechanical Engineering, 137(4) (2015) 041003.

[23] I. Bisio, A. Delfino, F. Lavagetto, A.J.I.I.o.T.J. Sciarrone, Enabling IoT for in-home rehabilitation: Accelerometer signals classification methods for activity and movement recognition, IEEE Internet of Things Journal, 4(1) (2016) 135-146.

[24] Y. Bouteraa, I.B. Abdallah, K.J.B. Boukthir, A new wrist–forearm rehabilitation protocol integrating human biomechanics and SVM-based machine learning for muscle fatigue estimation, Bioengineering 10(2) (2023) 219.

[25] Q. Liu, Y. Liu, C. Zhang, Z. Ruan, W. Meng, Y. Cai, Q.J.I.I.o.T.J. Ai, sEMG-based dynamic muscle fatigue classification using SVM with improved whale optimization algorithm, IEEE Internet of Things Journal, 8(23) (2021) 16835-16844.

[26] A. Zeiaee, R. Soltani-Zarrin, R. Langari, R.J.R. Tafreshi, Kinematic design optimization of an eight degree-of-freedom upper-limb exoskeleton, Robotica, 37(12) (2019) 2073-2086.

[27] K.E. Kaplan, K.A. Nichols, A.M. Okamura, Toward human-robot collaboration in surgery: performance assessment of human and robotic agents in an inclusion segmentation task, in:  2016 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2016, pp. 723-729.

[28] M.J. Solomito, Lagrangian Approach to Modeling the Biodynamics of the Upper Extremity: Applications to Collegiate Baseball Pitching,  (2015).

[29] H. Ghapanchizadeh, S.A. Ahmad, A.J. Ishak, Recommended surface EMG electrode position for wrist extension and flexion, in:  2015 IEEE Student Symposium in Biomedical Engineering & Sciences (ISSBES), IEEE, 2015, pp. 108-112.

[30] M.Z.J.C.I.i.E.A.-A.P.o.C.A. Jamal, F. Challenges, Signal acquisition using surface EMG and circuit design considerations for robotic prosthesis, Perspective on Current Applications and Future Challenges, 18 (2012) 427-448.

[31] A. Phinyomark, S. Thongpanja, H. Hu, P. Phukpattaranont, C.J.C.i.i.e.a.a.p.o.c.a. Limsakul, f. challenges, Frequencies in Electromyography Analysis, Computational intelligence in electromyography analysis: a perspective on current applications and future challenges,  (2012) 195.

[32] E.A.J.I.T.o.B.E. Clancy, Electromyogram amplitude estimation with adaptive smoothing window length, IEEE Transactions on Biomedical Engineering, 46(6) (2002) 717-729.

[33] A. Phinyomark, P. Phukpattaranont, C.J.E.s.w.a. Limsakul, Feature reduction and selection for EMG signal classification, Expert Systems with Applications, 39(8) (2012) 7420-7431.

[34] D.C. Toledo-Pérez, J. Rodríguez-Reséndiz, R.A. Gómez-Loenzo, J.J.A.S. Jauregui-Correa, Support vector machine-based EMG signal classification techniques: A review, Applied Sciences, 9(20) (2019) 4402.

[35] J. Too, A.R. Abdullah, N.M.J.I.J.o.A.C.S. Saad, Applications, Classification of hand movements based on discrete wavelet transform and enhanced feature extraction, International Journal of Advanced Computer Science and Applications, 10(6) (2019).

[36] R.H. Chowdhury, M.B. Reaz, M.A.B.M. Ali, A.A. Bakar, K. Chellappan, T.G.J.S. Chang, Surface electromyography signal processing and classification techniques, Sensors, 13(9) (2013) 12431-12466.

[37] A. Phinyomark, R. N. Khushaba, E.J.S. Scheme, Feature extraction and selection for myoelectric control based on wearable EMG sensors, Sensors, 18(5) (2018) 1615.

[38] D.C. Toledo-Pérez, J. Rodríguez-Reséndiz, R.A. Gómez-Loenzo, J.J.A.S. Jauregui-Correa, Support vector machine-based EMG signal classification techniques: A review, Applied Sciences, 9(20) (2019) 4402.

[39] B.F. Manly, Randomization, bootstrap, and Monte Carlo methods in biology, Chapman and hall/CRC, 2018.

[40] B.E. Arney, R. Glover, A. Fusco, C. Cortis, J.J. de Koning, T. van Erp, S. Jaime, R.P. Mikat, J.P. Porcari, C.J.I.j.o.s.p. Foster, Performance, Comparison of RPE (rating of perceived exertion) scales for session RPE, International Journal of Sports Physiology and Performance, 14(7) (2019) 994-996.

[41] D. Farina, M. Gazzoni, R.J.J.o.E. Merletti, Kinesiology, Assessment of low back muscle fatigue by surface EMG signal analysis: methodological aspects, Journal of Electromyography and Kinesiology, 13(4) (2003) 319-332.

[42] J. Sun, G. Liu, Y. Sun, K. Lin, Z. Zhou, J.J.F.i.S.N. Cai, Application of surface electromyography in exercise fatigue: a review, Frontiers in Systems Neuroscience, 16 (2022) 893275.

[43] B.F. Manly, Randomization, Bootstrap and Monte Carlo Methods in Biology: Texts in Statistical Science, chapman and hall/CRC, 2018.

[44] A.T. Poyil, V. Steuber, F.J.J.o.R. Amirabdollahian, A.T. Engineering, Influence of muscle fatigue on electromyogram–kinematic correlation during robot-assisted upper limb training, Journal of Rehabilitation and Assistive Technologies Engineering, 7 (2020) 2055668320903014.

[45] R. Wang, D.H. Fukuda, J.R. Stout, E.H. Robinson, A.A. Miramonti, M.S. Fragala, J.R.J.J.o.s.s. Hoffman, Medicine, Evaluation of electromyographic frequency domain changes during a three-minute maximal effort cycling test, Journal of sports science & medicine, 14(2) (2015) 452.