Machine Learning Based Hand Gesture Recognition via EMG Data
Resumen Los datos de la electromiografía (EMG) brindan información sobre la actividad eléctrica relacionada con los músculos. Los datos de EMG obtenidos del brazo a través de sensores ayudan a comprender los gestos de las manos. Para este trabajo, los datos de los gestos de la mano se tomaron del conjunto de datos EMG UCI2019 obtenido del brazalete tálmico MYO y se clasificaron con seis algoritmos de aprendizaje automático diferentes. Para la comparación se prefirieron los métodos de red neuronal artificial (ANN), máquina de vectores de soporte (SVM), k-vecino más cercano (k-NN), Naive Bayes (NB), árbol de decisión (DT) y bosque aleatorio (RF). basado en varias métricas de desempeño que son exactitud, precisión, sensibilidad, especificidad, error de clasificación, kappa, error cuadrático medio (RMSE) y correlación. Los datos pertenecen a siete gestos con las manos. Se utilizaron 700 muestras de 7 clases (100 muestras por grupo) en los experimentos. La relación de división en la clasificación fue 0,8-0,2, es decir, el 80% de las muestras se utilizaron en el entrenamiento y el 20% de los datos se utilizaron en la fase de prueba del clasificador. Se encontró que NB era el mejor entre otros métodos debido a su alta precisión (96,43%) y sensibilidad (96,43%) y el RMSE más bajo (0,189). Considerando los resultados de los parámetros de desempeño, se puede decir que este estudio reconoce y clasifica siete gestos con las manos con éxito en comparación con la literatura.
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Englehart, K., Hudgins, B., and Parker, P. A., (2001). A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 48(3):302–311. ISSN 00189294. doi:10.1109/10.914793.
Eshitha, K. V. and Jose, S., (2018). Hand Gesture Recognition Using Artificial Neural Network. In 2018 International Conference on Circuits and Systems in Digital Enterprise Technology, ICCSDET 2018. Institute of Electrical and Electronics Engineers Inc. ISBN 9781538605769. doi:10.1109/ICCSDET.2018.8821076.
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Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., and Acharya, U. R., (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161:1–13. ISSN 18727565. doi:10.1016/j.cmpb.2018.04.005.
Guo, W., Sheng, X., Liu, H., and Zhu, X., (2017). Toward an Enhanced Human-Machine Interface for Upper-Limb Prosthesis Control with Combined EMG and NIRS Signals. IEEE Transactions on Human-Machine Systems, 47(4):564–575. ISSN 21682291. doi:10.1109/THMS.2016.2641389.
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Singh, D. and Singh, B., (2019). Investigating the impact of data normalization on classification performance. Applied Soft Computing Journal. ISSN 15684946. doi:10.1016/j.asoc.2019.105524.
Vaiman, M., (2007). Standardization of surface electromyography utilized to evaluate patients with dysphagia. Head and Face Medicine, 3(1). ISSN 1746160X. doi:10.1186/1746-160X-3-26.
Van Drongelen, W., (2018). Signal Processing for Neuroscientists. Academic Press, 2 edition. ISBN 9780128104835.
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Xie, B., Li, B., and Harland, A., (2018). Movement and Gesture Recognition Using Deep Learning and Wearable-sensor Technology. In ACM International Conference Proceeding Series, pages 26–31. Association for Computing Machinery. ISBN 9781450365246. doi:10.1145/3268866.3268890.
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Karapinar Senturk, Z., & Bakay, M. S. (2021). Machine Learning Based Hand Gesture Recognition via EMG Data. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(2). https://doi.org/10.14201/ADCAIJ2021102123136
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