Optimization of Electromyography (EMG) Signal Parameters for Assistive Device Control Using a Convolutional Neural Network (CNN)
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Keywords:
assistive device control, deep learning, facial EMG, neural networks, hyperparameters
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Abstract
Facial Electromyography (EMG) signals offer a promising modality for intuitive human-machine interfaces (HMIs). The development of robust control systems, however, remains challenging in view of the inherent complexity, noise susceptibility, and significant inter-subject variability of EMG signals in the facial region. This study addresses these technical challenges by developing and validating an optimized Deep Learning framework for facial gesture recognition. The primary objective of this study is to create a reliable classification model for five essential facial movements: 'Rest', 'Smile', 'Eyebrow Raise', 'Right Lip Movement', and 'Left Lip Movement'. The model will serve as precise control inputs for assistive devices. The proposed methodology employs a systematic workflow comprising signal preprocessing (filtering, normalization, and segmentation) followed by the automated hyperparameter optimization of a one-dimensional (1D) Convolutional Neural Network (CNN). The experimental results demonstrate that the optimized model achieved a classification accuracy of 90% on internal test data, with the learning rate identified as the most critical hyperparameter influencing performance. Furthermore, validation of the model on entirely new participants yielded an accuracy of 71%. While this result underscores the persistent challenge of generalizing across different users, it establishes a reliable baseline. Ultimately, this work provides a validated, optimization-based framework that utilizes low-cost instrumentation, thereby offering a substantial pathway towards more accessible and personalized hands-free assistive technologies to restore autonomy for individuals with severe motor impairments.
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Ramadhan, I. W., & Adinandra, S. (2025). Optimization of Electromyography (EMG) Signal Parameters for Assistive Device Control Using a Convolutional Neural Network (CNN). Communications in Science and Technology, 10(2), 361–368. https://doi.org/10.21924/cst.10.2.2025.1758
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References
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2. Moon, I., Lee, M., Ryu, J., Mun, M., Intelligent robotic wheelchair with EMG-, gesture-, and voice-based interfaces, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. 3 (2003) 3453–3458.
3. Mak, A.F.T., Zhang, M., Boone, D.A., State-of-the-art research in lower-limb prosthetic biomechanics-socket interface, J. Rehabil. Res. Dev. 38 (2001) 161–173.
4. Manik, N.I., Ivan, A., Sistem deteksi emosi menggunakan sinyal EEG ‘Emoclass’, J. Algoritma Logika Komputasi 3 (2020) 1–8.
5. Rosemari, P., Rini, D.P., Sari, W.K., Klasifikasi sinyal EEG untuk mengenali jenis emosi menggunakan Deep Learning, J. Sist. Komput. Inform. 5 (2023).
6. Utari, A., Rini, D.P., Sari, W.K., Saputra, T., Klasifikasi sinyal EEG untuk mengenali jenis emosi menggunakan Recurrent Neural Network, J. Sist. Komput. Inform. 5 (2023).
7. Kawamura, K., Iskarous, M., Trends in service robots for the disabled and the elderly, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. 3 (1994) 1647–1654.
8. Putra, A.R.S.H., Pemrosesan dan klasifikasi sinyal navigasi berbasis Electromyography (EMG) pada otot lengan bawah, Islamic University of Indonesia (2020).
9. Gohel, V., Mehendale, N., Review on electromyography signal acquisition and processing, Biophys. Rev. 12 (2020) 1361–1367.
10. Firoozabadi, S., Oskoei, M.A., Hu, H., A human – computer interface based on forehead multi-channel bio-signals to control a virtual wheelchair, Proc. IEEE Int. Conf. Mechatronics Mach. Vis. Pract. (2002).
11. Tamura, H., Manabe, T., Tanno, K., Fuse, Y., The electric wheelchair control system using surface-electromygram of facial muscles, World Autom. Congr. (2010).
12. Hamedi, M., Salleh, S.H., Swee, T.T., Kamarulafizam, Surface electromyography-based facial expression recognition in bi-polar configuration, J. Comput. Sci. 7 (2011) 1407–1415.
13. van den Broek, E., Lisy, V., Janssen, J.H., Westerink, J.H.D.M., Schut, M.H., Tuinenbreijer, K., Affective man-machine interface: Unveiling human emotions through biosignals, Biomed. Eng. Syst. Technol. (2010) 21–47.
14. Rezazadeh, I.M., Firoozabadi, S.M., Hu, H., Golpayegani, S.M.R.H., A novel human-machine interface based on recognition of multi-channel facial bioelectric signals, Australas. Phys. Eng. Sci. Med. 34 (2011) 497–513.
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16. Gibert, G., Pruzinec, M., Schultz, T., Stevens, C., Enhancement of human computer interaction with facial electromyographic sensors, Proc. 21st Annu. Conf. Aust. Comput.-Hum. Interact. Spec. Interest Gr. (2009) 421–424.
17. Tamura, H., Manabe, T., Tanno, K., Fuse, Y., The electric wheelchair control system using surface-electromygram of facial muscles, World Autom. Congr. (2010) 1–6.
18. Ang, L.B.P., et al., Facial expression recognition through pattern analysis of facial muscle movements utilizing electromyogram sensors, IEEE Reg. 10 Conf. TENCON 3 (2004) 600–603.
19. Hamedi, M., Rezazadeh, I.M., Firoozabadi, M., Facial gesture recognition using two-channel bio-sensors configuration and fuzzy classifier: A pilot study, Int. Conf. Electr. Control Comput. Eng. (2011) 338–343.
20. Hamedi, M., et al., Human facial neural activities and gesture recognition for machine-interfacing applications, Int. J. Nanomedicine 6 (2011) 3461–3472.
21. Welihinda, D.V.D.S., Gunarathne, L.K.P., Herath, H.M.K.K.M.B., Yasakethu, S.L.P., Madusanka, N., Lee, B.I., EEG and EMG-based human-machine interface for navigation of mobility-related assistive wheelchair (MRA-W), Heliyon 10 (2024) e27777.
22. Tao, W., Xu, J., Liu, T., Electric-powered wheelchair with stair-climbing ability, Int. J. Adv. Robot. Syst. 14 (2017).
23. Dhindsa, I.S., Agarwal, R., Ryait, H.S., Performance evaluation of various classifiers for predicting knee angle from electromyography signals, Expert Syst. 36 (2019) e12381.
24. Sarhan, S.M., Al-Faiz, M.Z., Takhakh, A.M., A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients, Heliyon 9 (2023) e18308.
25. Torres-Oviedo, G., Ting, L.H., Muscle synergies characterizing human postural responses, J. Neurophysiol. 98 (2007) 2144–2156.
26. Perry, J., Burnfield, J.M., Gait Analysis: Normal and Pathological Function, J. Sports Sci. Med. 9 (2010) 353.
27. Clarys, J.P., Cabri, J., Electromyography and the study of sports movements: a review, J. Sports Sci. 11 (1993) 379–448.
28. Hug, F., Dorel, S., Electromyographic analysis of pedaling: a review, J. Electromyogr. Kinesiol. 19 (2009) 182–198.
29. Prilutsky, B.L., Coordination of two- and one-joint muscles: Functional consequences and implications for motor control, Motor Control 4 (2000) 1–44.
30. Farina, D., Merletti, R., Enoka, R.M., The extraction of neural strategies from the surface EMG: an update, J. Appl. Physiol. 117 (2014) 1215–1230.
31. Erdemir, A., McLean, S., Herzog, W., Van Den Bogert, A.J., Model-based estimation of muscle forces exerted during movements, Clin. Biomech. 22 (2007) 131–154.
32. Abel, E.W., Zacharia, P.C., Forster, A., Farrow, T.L., Neural network analysis of the EMG interference pattern, Med. Eng. Phys. 18 (1996) 12–17.
33. Pattichis, C.S., Schizas, C.N., Middleton, L.T., Neural network models in EMG diagnosis, IEEE Trans. Biomed. Eng. 42 (1995) 486–496.
34. Rutkowska, D., Neuro-fuzzy Architectures and Hybrid Learning, Physica-Verlag (2002).
35. Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning representations by back-propagating errors, Nature 323 (1986) 533–536.
36. Kohonen, T., Adaptive, associative, and self-organizing functions in neural computing, Appl. Opt. 26 (1987) 4910–4918.
37. Christodoulou, C., Pattichis, C., Combining neural classifiers in EMG diagnosis, IEEE Int. Conf. Electron. Circuits Syst. (1998).
38. Katsis, C.D., et al., A two-stage method for MUAP classification based on EMG decomposition, Comput. Biol. Med. 37 (2007) 1232–1240.
39. Subasi, A., Yilmaz, M., Ozcalik, H.R., Classification of EMG signals using wavelet neural network, J. Neurosci. Methods 156 (2006) 360–367.
40. Chauvet, E., Fokapu, O., Hogrel, J.Y., Gamet, D., Duchene, J., A method of EMG decomposition based on fuzzy logic, Proc. 23rd Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2 (2001) 1948–1950.
41. Rasheed, S., Stashuk, D., Kamel, M.S., Adaptive fuzzy k-NN classifier for EMG signal decomposition, Med. Eng. Phys. 28 (2006) 694–709.
42. Disselhorst-Klug, C., Non-invasive diagnosis of neuromuscular disorders by high-spatial-resolution-EMG, IntechOpen (2012).
43. Kaur, G., Arora, A., Jain, V.K., Multi-class support vector machine classifier in EMG diagnosis, WSEAS Trans. Signal Process. (2009).
44. Kaur, G., Gurmanik, Arora, A., Jain, V., EMG diagnosis via AR modeling and binary support vector machine classification, Int. J. Eng. Sci. Technol. 2 (2010).
45. Istenic, R., Kaplanis, P.A., Pattichis, C.S., Zazula, D., Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders, Med. Biol. Eng. Comput. 48 (2010) 773–781.
46. Dobrowolski, A.P., Wierzbowski, M., Tomczykiewicz, K., Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders, Comput. Methods Programs Biomed. 107 (2012) 393–403.
47. Subasi, A., Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines, Comput. Biol. Med. 42 (2012) 806–815.
48. Subasi, A., Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders, Comput. Biol. Med. 43 (2013) 576–586.
49. Burges, C.J.C., A tutorial on support vector machines for pattern recognition, Data Min. Knowl. Discov. 2 (1998) 121–167.
50. Suykens, J., Advances in learning theory: methods, models and applications, NATO Science Series (2003).
51. Lin, C.F., Wang, S.D., Fuzzy support vector machines with automatic membership setting, Stud. Fuzziness Soft Comput. 177 (2005) 233–254.
52. Kamali, T., Boostani, R., Parsaei, H., A multi-classifier approach to MUAP classification for diagnosis of neuromuscular disorders, IEEE Trans. Neural Syst. Rehabil. Eng. 22 (2014) 191–200.
53. Koçer, S., Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases, J. Med. Syst. 34 (2010) 321–329.
54. Xie, H., Huang, H., Wang, Z., A hybrid neuro-fuzzy system for neuromuscular disorders diagnosis, Proc. IEEE Int. Workshop Biomed. Circuits Syst. (2004) 265–268.
55. Keleş, S., Subasi, A., Classification of EMG signals using decision tree methods, IBU Repository (2024).
56. Jia, R., Yang, L., Li, Y., Xin, Z., Gestures recognition of sEMG signal based on random forest, IEEE 16th Conf. Ind. Electron. Appl. (2021) 1673–1678.
57. Navneet, K., Panag, T., Statistical technique for classification of MUAPs for neuromuscular disease, Int. J. Comput. Appl. 1 (2010).
58. Pino, L.J., Stashuk, D.W., Using motor unit potential characterizations to estimate neuromuscular disorder level of involvement, Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (2008) 4138–4141.
59. Kołodziej, M., Majkowski, A., Jurczak, M., Acquisition and analysis of facial electromyographic signals for emotion recognition, Sensors 24 (2024).
60. Hamedi, M., Salleh, S.H., Astaraki, M., Noor, A.M., EMG-based facial gesture recognition through versatile elliptic basis function neural network, BioMed. Eng. OnLine 12 (2013) 73.
61. Firdousi, F., Ahmad, A., Concise convolutional neural network model for fault detection, Commun. Sci. Technol. 7 (2022) 1–8.