Processing and recognition of EMG signals through CNN networks for the control of electric vehicles-Reference-Cited by-同舟云学术

Processing and recognition of EMG signals through CNN networks for the control of electric vehicles

Published:2023-12-31 Issue: Volume: Page:15-23
ISSN:2524-2121
Container-title:ECORFAN Journal Taiwan
language:es
Short-container-title:EJT

Author:

López-Rodríguez Pedro¹^ORCID,Montiel-Rodríguez Martin¹^ORCID,Samano-Flores Yosafat Jetsemani¹^ORCID,Mandujano-Nava Arturo¹^ORCID

Affiliation:

1. Universidad Politécnica de Guanajuato

Abstract

The increase in autonomous driving technologies, as well as biometrics using biosignals from vehicle drivers, provide information that can be used for the development of personalized biosecurity and driving systems for each user. Currently, studies are being carried out on the extraction and classification of driver characteristics with great precision, to generate intelligent systems that are auxiliary and that help to safeguard the integrity of people while driving vehicles. This work presents the recognition of 5 hand gestures to control the driving actions of an electric vehicle using the EMG signals from the MYOTM bracelet, these signals have also been used to detect users and thus allow the use only of the people registered in the application. To perform gesture recognition, a convolutional neural network was trained and implemented for the classification of actions. Finally, a cross-validation was carried out to validate the reliability of the proposed system, obtaining 99.2% accuracy during the classification.

Publisher

ECORFAN

Reference35 articles.

1. [1] G. Choi, K. Lim and S. B. Pan, "Driver Identification System Using 2D ECG and EMG Based on Multistream CNN for Intelligent Vehicle," in IEEE Sensors Letters, vol. 6, no. 6, pp. 1-4, June 2022, Art no. 6001904, URL: https://ieeexplore.ieee.org/document/9782761, doi: 10.1109/LSENS.2022.3175787.

2. [2] Vargas-Lopez, O., Perez-Ramirez, C. A., Valtierra-Rodriguez, M., Yanez-Borjas, J. J., & Amezquita-Sanchez, J. P. (2021). An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals. Sensors, 21(9), 3155. URL: https://www.mdpi.com/1424-8220/21/9/3155, http://dx.doi.org/10.3390/s21093155.

3. [3] J. Ju, L. Bi and A. G. Feleke, "Detection of Emergency Braking Intention From Soft Braking and Normal Driving Intentions Using EMG Signals," in IEEE Access, vol. 9, pp. 131637-131647, 2021,URL: https://ieeexplore.ieee.org/document/9541374, doi: 10.1109/ACCESS.2021.3114341.

4. [4] S. Aso, A. Sasaki, H. Hashimoto and C. Ishii, "Driving Electric Car by Using EMG Interface," 2006 IEEE Conference on Cybernetics and Intelligent Systems, Bangkok, Thailand, 2006, pp. 1-5, URL: https://ieeexplore.ieee.org/document/4017819, doi: 10.1109/ICCIS.2006.252260.

5. [5] Vu, T.H., Misra, A., Roy, Q., Wei, K.C.T., Lee, Y.: Smartwatch-based Early Gesture Detection 8 Trajectory Tracking for Interactive Gesture-Driven Applications. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2(1), 1–27 (2018). URL: https://dl.acm.org/doi/10.1145/3191771, https://doi.org/10.1145/3191771.