Abstract
A number of musculoskeletal disorders occur worldwide in occupations that perform physically demanding tasks. In order to treat musculoskeletal disorders, rehabilitation must be performed, but it is not easy to correctly perform rehabilitation exercise by the patient at home where they spend a long time. Therefore, for effective rehabilitation exercise, a rehabilitation exercise posture determination system using body information of patients is required. In this paper, we implemented rehabilitation exercise posture determination system based on CNN using EMG and acceleration sensors. The implemented system measures data during rehabilitation exercise, performs pre-processing, and inputs it into the trained CNN model to determine the exercise posture. In order to evaluate performance of the implemented system, actual measurement data was input CNN model 50 times each to confirm the accuracy of posture determination. As a result of the experiment, the accuracy of 98.6% was confirmed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Carrillo-Castrillo, J.A., Perez-Mira, V., Pardo-Ferreira, M.D.C., Rubio-Romero, J.C.: Analysis of required investigations of work-related musculoskeletal disorders in Spain. Int. J. Environ. Res. Public Health 16(10), 1682 (2019)
Kodom-Wiredu, J.K.: The relationship between firefighters’ work demand and work-related musculoskeletal disorders: the moderating role of task characteristics. Saf. Health Work 10(1), 61–66 (2019)
Hutting, N., Johnston, V., Staal, J.B., Heerkens, Y.F.: Promoting the use of self-management strategies for people with persistent musculoskeletal disorders: the role of physical therapists. J. Orthop. Sports Phys. Ther. 49(4), 212–215 (2019)
Zhu, Z.A., Lu, Y.C., You, C.H., Chiang, C.K.: Deep learning for sensor-based rehabilitation exercise recognition and evaluation. Sensors 19(4), 887 (2019)
Abo-Tabik, M., Costen, N., Darby, J., Benn, Y.: Towards a smart smoking cessation app: a 1D-CNN model predicting smoking events. Sensors 20(4), 1099 (2020)
Jo, Y.H., Ha, J.Y., Seo, J.Y., Jeong, D.Y.: Implementation of customized rehabilitation monitoring system based on EMG and acceleration. In: Conference of Korea Institute of Convergence Signal Processing (2020)
Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2018R1D1A1B07045337) and MSIT (Ministry of Science, ICT & Future Planning), Korean, under the National Program for Excellence in SW (2019–0-01817) supervised by the IITP (Institute of Information & communications Technology Planning & Evaluation).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Lee, JS., Seo, JY., Jung, SJ., Noh, YH., Jeong, DU. (2021). Implementation of Rehabilitation Exercise Posture Determination System Based on CNN Using EMG and Acceleration Sensors. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12616. Springer, Cham. https://doi.org/10.1007/978-3-030-68452-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-68452-5_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68451-8
Online ISBN: 978-3-030-68452-5
eBook Packages: Computer ScienceComputer Science (R0)