Design of remote injury diagnosis system for Wushu competition based on wireless sensor network

Chunguang Pan

doi:10.4108/eetpht.v8i3.686

Authors

Chunguang Pan College of Physical Education, Wuchang Institute of Technology, Wuhan 430065, China

DOI:

https://doi.org/10.4108/eetpht.v8i3.686

Keywords:

Wireless sensor network, Remote Wushu competition, Injury diagnosis system, Support vector machine, ECG sensor, Temperature sensor

Abstract

INTRODUCTION: In this paper, a remote injury diagnosis system for Wushu competition based on wireless sensor network is designed to improve the safety of athletes in the process of Wushu competition.

OBJECTIVES: Improve the safety of athletes during martial arts competitions.

METHODS: The pulse sensor, temperature sensor and ECG sensor are set as the terminal nodes of the remote injury diagnosis system for Wushu competition. The physiological parameters of athletes in Wushu competition are collected by the sensor, and the collected parameters are transmitted to the wireless RF module. The wireless RF module uses the wireless sensor network to realize the wireless communication of various parameter data through the routing node and terminal node, and transmits the data to the remote diagnosis module. The remote diagnosis module uses the collected physiological parameters of athletes to realize the remote diagnosis of injury in Wushu competition through particle swarm optimization-support vector machine diagnosis model.

RESULTS: The experimental results show that the designed system can remotely collect the physiological parameters of athletes in Wushu competition, and remotely diagnose the injury of Wushu competition according to the collected data, and the diagnosis accuracy is as high as 99%.

CONCLUSION: It has good safety performance and is of practical significance.

Downloads

Download data is not yet available.

References

Yabe, Y. , Hagiwara, Y. , Sekiguchi, T. , Momma, H. & Nagatomi, R. (2020). Low back pain in school-aged martial arts athletes in japan: a comparison among judo, kendo, and karate. The Tohoku Journal of Experimental Medicine, 251(4), 295-301. DOI: https://doi.org/10.1620/tjem.251.295

Zito, P. M. , Rubenstein, R. M. & Glick, B. P. (2020). 18553 dermatologic foes faced by mixed martial arts fighters. Journal of the American Academy of Dermatology, 83(6), AB215. DOI: https://doi.org/10.1016/j.jaad.2020.06.948

Chakraborty, S. , Goyal, N. K. , Mahapatra, S. & Soh, S. (2020). A monte-carlo markov chain approach for coverage-area reliability of mobile wireless sensor networks with multistate nodes. Reliability Engineering & System Safety, 193(Jan.), 106662.1-106662.14. DOI: https://doi.org/10.1016/j.ress.2019.106662

Kanwar, V. & Kumar, A. (2021). Dv-hop localization methods for displaced sensor nodes in wireless sensor network using pso. Wireless Networks, 27(1), 91-102. DOI: https://doi.org/10.1007/s11276-020-02446-5

Eledlebi, K. , Ruta, D. , Hildmann, H. , Saffre, F. & Isakovic, A. F. (2020). Coverage and energy analysis of mobile sensor nodes in obstructed noisy indoor environment: a voronoi-approach. IEEE Transactions on Mobile Computing, PP(99), 1-11. DOI: https://doi.org/10.1109/TMC.2020.3046184

Singh, P. & Mittal, N. (2021). An efficient localization approach to locate sensor nodes in 3d wireless sensor networks using adaptive flower pollination algorithm. Wireless Networks, 27(3), 1999-2014. DOI: https://doi.org/10.1007/s11276-021-02557-7

Chen, B. , Hu, Y. , Li, J. , Yu, B. & Fu, P. (2020). Research on quench detection method using radio frequency wave technology. IEEE Transactions on Applied Superconductivity, 30(2), 1-5. DOI: https://doi.org/10.1109/TASC.2019.2957195

Gruska, M. , Aigner, G. , Altenber, R. J. , Burkart-Küttner, D. & Teubl, A. (2020). Recommendations on the utilization of telemedicine in cardiology. Wiener klinische Wochenschrift, 132(23-24), 782-800. DOI: https://doi.org/10.1007/s00508-020-01762-2

Vijayalakshmi, P. , Selvi, K. , Gowsic, K. & Muthumanickam, K. (2021). A misdirected route avoidance using random waypoint mobility model in wireless sensor network. Wireless Networks, 27(6), 3845-3856. DOI: https://doi.org/10.1007/s11276-021-02703-1

Shi, T. , Li, J. , Gao, H. & Cai, Z. (2020). A novel framework for the coverage problem in battery-free wireless sensor networks. IEEE Transactions on Mobile Computing, 21(3), 783-793. DOI: https://doi.org/10.1109/TMC.2020.3019470

Xu, Y. F. , Duan L. Z. (2021). Node Redeployment of Wireless Sensor Network Based on Leapfrog Algorithm. Computer Simulation, 38(10), 328-332.

Liu S, Wang S, Liu X, et al (2022). Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring, . IEEE Internet of Things Journal, online first, 10.1109/JIOT.2022.3142115 DOI: https://doi.org/10.1109/JIOT.2022.3142115

Karimi-Bidhendi, S. , Guo, J. & Jafarkhani, H. (2020). Energy-efficient node deployment in heterogeneous two-tier wireless sensor networks with limited communication range. IEEE Transactions on Wireless Communications, 20(1), 40-55. DOI: https://doi.org/10.1109/TWC.2020.3023065

Lyu, W. , Xu, W. , Yang, F. , Chen, S. & Yu, C. (2021). Non-invasive measurement for cardiac variations using a fiber optic sensor1. IEEE Photonics Technology Letters, 33(18), 990-993. DOI: https://doi.org/10.1109/LPT.2021.3078757

Liu S, Xu X, Zhang Y, et al (2022). A Reliable Sample Selection Strategy for Weakly-supervised Visual Tracking, IEEE Transactions on Reliability, online first, 10.1109/TR.2022.3162346.

Nancy, P. , Muthurajkumar, S. , Ganapathy, S. , Kumar, S. , Selvi, M. & Arputharaj, K. . (2020). Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks. IET Communications, 14(5), 888-895. DOI: https://doi.org/10.1049/iet-com.2019.0172

Qiu, S. , Zhu, Y. H. , Tian, X. & Chi, K. (2020). Goodput-maximised data delivery scheme for battery-free wireless sensor network. IET Communications, 14(4), 665-673. DOI: https://doi.org/10.1049/iet-com.2018.6215

Liu S, Guo C, Fadi A, et al (2020). Reliability of Response Region: A Novel Mechanism in Visual Tracking by Edge Computing for IIoT Environments, Mechanical Systems and Signal Processing, 138, 106537

Motin, M. A. , Karmakar, C. , Palaniswami, M. & Penzel, T. (2020). Ppg based automated sleep-wake classification using support vector machine. Physiological Measurement, 41(7), 075013 (13pp).

Hu, J. H. (2021). A particle swarm optimization algorithm with distributed adaptively weighted delays. Advances in Applied Mathematics, 10(3), 753-762. DOI: https://doi.org/10.12677/AAM.2021.103083