Reconocimiento automático de gestos de la mano a partir de señales de electromiografía de superficie multicanal mediante modelos de ML y D
DOI:
https://doi.org/10.83179/th8hpj19Palabras clave:
electromiografía de superficie, reconocimiento de gestos de la mano, machine learning, deep learningResumen
El reconocimiento de gestos de la mano (HGR, por su sigla en inglés) a partir de señales de electromiografía de superficie (sEMG) se ha consolidado como una línea de investigación de gran interés en los ámbitos biomédico y tecnológico, debido a su potencial aplicación en prótesis, rehabilitación y control de dispositivos inteligentes. El objetivo de esta revisión es analizar los avances recientes y los retos técnicos asociados al HGR, considerando el flujo típico de estos sistemas: adquisición de datos, preprocesamiento, extracción de características y clasificación. Se abordan técnicas tradicionales de machine learning (ML) como máquinas de soporte vectorial (SVM), K-vecinos cercanos (KNN) y random forest, así como enfoques de deep learning (DL), entre estos redes neuronales convolucionales (CNN) y modelos híbridos. Igualmente, se discuten los principales desafíos del área, entre los cuales se destacan la variabilidad intersujeto, la falta de bases de datos etiquetadas y la robustez de los modelos en entornos reales. Esta revisión contribuye a identificar vacíos de investigación y orientaciones futuras para el desarrollo de sistemas de HGR más robustos, accesibles y generalizables.
Descargas
Referencias
- Aarotale, P., y Rattani, A. (2025). Time frequency analysis of EMG signal for gesture recognition using fine-grained features. arXiv preprint. https://doi.org/10.48550/arXiv.2504.14708
- Arul, V. (2021). Deep learning methods for data classification. En D. Binu y B. Rajakumar (eds.), Artificial intelligence in data mining (pp. 87-108). Academic Press. https://doi.org/10.1016/B978-0-12-820601-0.00001-X
- Benalcázar, M. E., Motoche, C., Zea, J. A. et al. (2017). Real-time hand gesture recognition using the Myo armband and muscle activity detection. En 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE. https://doi.org/10.1109/ETCM.2017.8247458
- Burden, A. (2010). How should we normalize electromyograms obtained from healthy participants? What we have learned from over 25 years of research. Journal of Electromyography and Kinesiology, 20(6), 1023-1035. https://doi.org/10.1016/j.jelekin.2010.07.004
- Carver, J., Ganus, A., Ivey, J., Plummer, T., y Eubank, A. (2015). The impact of mobility assistive technology devices on participation for individuals with disabilities. Disability and Rehabilitation: Assistive Technology, 11(2). https://doi.org/10.3109/17483107.2015.1027295
- Challa, K., Alhmoud, I. W., Jaiswal, C., Turlapaty, A. C., y Gokaraju, B. (2025). EMG features dataset for arm activity recognition. Data in Brief, 60, 111519. https://doi.org/10.1016/j.dib.2025.111519
- Chen, X., Li, Y., Hu, R., Zhang, X., y Chen, X. (2021). Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method. IEEE Journal of Biomedical and Health Informatics, 25(4), 1292-1304. https://doi.org/10.1109/JBHI.2020.3009383
- Chowdhury, R. H., Reaz, M. B. I., Ali, M. A. B. M., Bakar, A. A. A., Chellappan, K., y Chang, T. G. (2013). Surface electromyography signal processing and classification techniques. Sensors, 13(9), 12431-12466. https://doi.org/10.3390/s130912431
- Departamento Administrativo Nacional de Estadística (2020, noviembre). Panorama general de la discapacidad en Colombia. https://www.dane.gov.co/files/investigaciones/discapacidad/Panorama-general-de-la-discapacidad-en-Colombia.pdf
- Degadwala, S., y Vyas, D. (2024). Systematic analysis of deep learning models vs. machine learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(4), 60-70. https://doi.org/10.32628/CSEIT24104108
- Gehlot, N., Jena, A., Kumar, R., y Bukya, M. (2024). MEET: Mixture of experts extra tree-based sEMG hand gesture identification. arXiv preprint. https://doi.org/10.48550/arXiv.2405.09562
- Gómez-Correa, M., Ballesteros, M., Salgado, I., y Cruz-Ortiz, D. (2023, mayo). Forearm sEMG data from young healthy humans during the execution of hand movements. Scientific Data, 10. https://doi.org/10.1038/s41597-023-02223-x
- Guo, L., Lu, Z., y Yao, L. (2021). Human-machine interaction sensing technology based on hand gesture recognition: a review. IEEE Transactions on Human-Machine Systems, 51(4), 300-309. https://doi.org/10.1109/THMS.2021.3086003
- Hu, H., y Zhou, Z. (2023). Evaluation and comparison of ten machine learning classification models based on the mobile users experience. En 2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS) (pp. 767-771). https://doi.org/10.1109/EIECS59936.2023.10435603
- Hu, Y., Wong, Y. W., Wei, W., Du, Y., Kankanhalli, M., y Geng, W. (2018). A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition. PLOS ONE, 13(10), e0206049. https://doi.org/10.1371/journal.pone.0206049
- Jaramilo-Yáñez, A., Benalcázar, M. E., y Mena-Maldonado, E. (2020). Real-time hand gesture recognition using surface electromyography and machine learning: a systematic literature review. Sensors, 20(9). https://doi.org/10.3390/s20092467
- Jiang, S., Kang, P., Song, X., Lo, B. P. y Shull, P. B. (2022). Emerging wearable interfaces and algorithms for hand gesture recognition: a survey. IEEE Reviews in Biomedical Engineering, 15, 85-102. https://doi.org/10.1109/RBME.2021.3078190
- Jochumsen, M., Waris, A., y Kamavuako, E. N. (2018). The effect of arm position on classification of hand gestures with intramuscular EMG. Biomedical Signal Processing and Control, 43, 1-8. https://doi.org/10.1016/j.bspc.2018.02.013
- Khushaba, R. (2020). A framework of temporal-spatial descriptors-based feature extraction for improved myoelectric pattern recognition. Journal of Neural Engineering, 17(4), 046023. https://doi.org/10.1088/1741-2552/ab9f1b
- Köpüklü, O., Gündüz, A., Köse, N., y Rigoll, G. (2019, febrero). Real-time hand gesture detection and classification using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/CVPRW.2019.00015
- Kumar, D., y Ganesh, A. (2022, agosto). A critical review on hand gesture recognition using sEMG: challenges, application, process and techniques. Journal of Physics: Conference Series, 2327, 012075. https://doi.org/10.1088/1742-6596/2327/1/012075
- Kuo, Y., y Zhang, Z. (2019, mayo). Real-time pattern recognition for hand gesture based on ANN and surface EMG. En 2019 IEEE 11th International Conference on Information Technology and Artificial Intelligence Conference (ITAIC) (pp. 799-802). IEEE. https://doi.org/10.1109/ITAIC.2019.8785894
- Lee, K. H., Min, J. Y., y Byun, S. (2022). Electromyogram-based classification of hand and finger gestures using artificial neural networks. Sensors, 22(1). https://doi.org/10.3390/s22010225
- Li, J., Nanchang, C., y Song, K. (2021). Research on image classification based on deep learning. En 2021 IEEE/ACIS 19th International Conference on Computer and Information Science (ICIS) (pp. 132-136). IEEE. https://doi.org/10.1109/ICIS51600.2021.9516872
- Li, W., Shi, P., y Yu, H. (2021). Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future. Frontiers in Neuroscience, 15. https://doi.org/10.3389/fnins.2021.646671
- Ma, Y., Chen, B., Ren, P., Zheng, N., Indiveri, G., y Donati, E. (2020). EMG-based gestures classification using a mixed-signal neuromorphic processing system. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 10(4), 578-587. https://doi.org/10.1109/JETCAS.2020.3037951
- Miah, A. S. M., Hassan, N., Maniruzzaman, M., Asai, N., y Shin, J. (2024). EMG-based hand gesture recognition through diverse domain feature enhancement and machine learning-based approach. arXiv preprint. https://doi.org/10.48550/arXiv.2408.13723
- Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., y Gao, J. (2021). Deep learning-based text classification: a comprehensive review. ACM Computing Surveys, 54(3). https://doi.org/10.1145/3439726
- Mishra, S., y Pradhan, R. K. (2023). Analyzing the impact of feature correlation on classification accuracy of machine learning model. En 2023 International Conference on Artificial Intelligence and Smart Communication (AISC) (pp. 949-953). IEEE. https://doi.org/10.1109/AISC56616.2023.10085293
- Mohamed, N., Mustafa, M. B., y Jomhari, N. (2021). A review of the hand gesture recognition system: current progress and future directions. IEEE Access, 9, 157422-157436. https://doi.org/10.1109/ACCESS.2021.3129650
- Mouniadi, A., Srinivasan, A., y Ajiunan, S. (2020, agosto). A real-time capable linear time classifier scheme for anticipated hand movements recognition from amputee subjects using surface EMG signals. IRBM, 42. https://doi.org/10.1016/j.irbm.2020.08.003
- Ni, S., Al-Qaness, M. A., Hawbani, A., Al-Alimi, D., Abd Elaziz, M., y Ewees, A. A. (2024). A survey on hand gesture recognition based on surface electromyography: fundamentals, methods, applications, challenges and future trends. Applied Soft Computing, 166, 112235. https://doi.org/10.1016/j.asoc.2024.112235
- Nielsen, J. L., Holmgard, S., Jiang, N., Englehart, K. B., Farina, D., y Parker, P. A. (2011). Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training. IEEE Transactions on Biomedical Engineering, 58(3), 681-688. https://doi.org/10.1109/TBME.2010.2068298
- Pizzolato, S., Tagliapietra, L., Cognolato, M., Reggiani, M., Müller, H., y Atzori, M. (2017). Comparison of six electromyography acquisition setups on hand movement classification tasks. PLOS ONE, 12(10), e0186132. https://doi.org/10.1371/journal.pone.0186132
- Prakash, A., Kumari, B., y Sharma, S. (2019, agosto). A low-cost, wearable sEMG sensor for upper limb prosthetic application. Journal of Medical Engineering & Technology, 43, 1-13. https://doi.org/10.1080/03091902.2019.1653391
- Rani, G. J., Hashmi, M. F., y Gupta, A. (2023). Surface electromyography and artificial intelligence for human activity recognition: a systematic review on methods, emerging trends, applications, challenges, and future implementation. IEEE Access, 11, 105140-105169. https://doi.org/10.1109/ACCESS.2023.3316509
- Rumman, U., Ferdousi, A., Hossain, M. et al. (2024). FORS-EMG: a novel sEMG dataset for hand gesture recognition across multiple forearm orientations. arXiv preprint. https://doi.org/10.48550/arXiv.2409.07484
- Samadani, A.-A., y Kulic, D. (2014). Hand gesture recognition based on surface electromyography. En 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4196-4199). IEEE. https://doi.org/10.1109/EMBC.2014.6944549
- Sinderby, C., Lindström, L., y Grassino, A. E. (1995). Automatic assessment of electromyogram quality. Journal of Applied Physiology, 79(5), 1803-1815. https://doi.org/10.1152/jappl.1995.79.5.1803
- Stegeman, D., y Hermens, H. (2007, enero). Standards for surface electromyography: the European Project Surface EMG for Non-Invasive Assessment of Muscles (Seniam) (vol. 1).
- Sultana, A., Ahmed, F., y Alam, M. S. (2023). A systematic review on surface electromyography-based classification system for identifying hand and finger movements. Healthcare Analytics, 3, 100126. https://doi.org/10.1016/j.health.2022.100126
- Weiss, L. D., Weiss, J. M., y Silver, J. K. (2015). Easy EMG: a guide to performing nerve conduction studies and electromyography. Elsevier Health Sciences.
- Wu, Z., Zhang, J., y Hu, S. (2020). Review on classification algorithm and evaluation system of machine learning. En 2020 13th International Conference on Intelligent Computation Technology and Automation (ICICTA) (pp. 214-218). IEEE. https://doi.org/10.1109/ICICTA51737.2020.00052
- Zaheer, F., Roy, S., y De Luca, C. (2012). Preferred sensor sites for surface EMG signal decomposition. Physiological Measurement, 33(2), 195-206. https://doi.org/10.1088/0967-3334/33/2/195
- Zhang, Y., Yang, F., Fan, Q., Yang, A., y Li, X. (2022). Research on sEMG-based gesture recognition by dual-view deep learning. IEEE Access, 10, 32928-32937. https://doi.org/10.1109/ACCESS.2022.3158667
- Zheng, Z., Wu, Z., Zhao, R., Ni, Y., Jing, X., y Gao, S. (2022). A review of EMG-, FMG-, and EIT-based biosensors and relevant human-machine interactivities and biomedical applications. Biosensors, 12(7). https://doi.org/10.3390/bios12070516
Descargas
Publicado
Número
Sección
Categorías
