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25-08-2022

Remote Sports Injury Monitoring using Wireless Sensor Networks

Authors: Ying Song, Gautam Srivastava

Published in: Mobile Networks and Applications | Issue 6/2023

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Abstract

When remote sports injuries are traditionally monitored, the feedback is not timely, and there are some problems such as low running speed and poor accuracy of sports injury monitoring. Therefore, this paper designs a remote sports injury monitoring method based on a wireless sensor network. Firstly, the terminal node of the sports injury monitoring process is designed, and three-terminal devices are tied to the experimental object's body to collect motion information, to realize the collection of human motion information based on ZigBee wireless sensor network; Secondly, the USB module circuit interface is designed to realize the series connection of each line, and the local processing ability of network nodes is used to make a centralized decision. Then, the skeleton coordinate system is constructed, and the rotation of the human skeleton is measured by an inertial sensor. Through a variety of posture fitting, the error of remote sports injury monitoring is reduced from the two directions of joint error and muscle error. Finally, the training sample set is learned through the BP algorithm, the fitness function of the genetic algorithm is obtained, the external structural parameters of the adaptive neural network model are adjusted, the discrimination deviation and fitness function are calculated, the adaptive neural network model with the best generalization ability is output, and the local processing ability of remote sports injury monitoring method is improved combined with wireless sensor network technology, The design of remote sports injury monitoring method based on wireless sensor network is realized. The experimental results show that the accuracy of the method is 99%, the average time delay is 1 s, and the accuracy of the method is 92% even with noise. Therefore, the method can effectively improve the running speed of the remote sports injury monitoring method and improve the accuracy of sports injury monitoring.

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Literature
1.
go back to reference Ascioglu G, Senol Y (2020) Design of a wearable wireless multi-sensor monitoring system and application for activity recognition using deep learning[J]. IEEE Access 8(12):169183–169195CrossRef Ascioglu G, Senol Y (2020) Design of a wearable wireless multi-sensor monitoring system and application for activity recognition using deep learning[J]. IEEE Access 8(12):169183–169195CrossRef
2.
go back to reference Chen F (2021) Athlete muscle measurement and exercise data monitoring based on embedded system and wearable devices[J]. Microprocess Microsyst 82(5):103901–103912CrossRef Chen F (2021) Athlete muscle measurement and exercise data monitoring based on embedded system and wearable devices[J]. Microprocess Microsyst 82(5):103901–103912CrossRef
3.
go back to reference Zhao HW (2021) Target extraction in moving foreground region based on double three frame difference[J]. Comput Simul 38(07):433-436+441 Zhao HW (2021) Target extraction in moving foreground region based on double three frame difference[J]. Comput Simul 38(07):433-436+441
4.
go back to reference Wang SH, Zhou QH, Miguel MG et al (2021) Advances in data preprocessing for biomedical data fusion: an overview of the methods, challenges, and prospects[J]. Information Fusion 76:376–421CrossRef Wang SH, Zhou QH, Miguel MG et al (2021) Advances in data preprocessing for biomedical data fusion: an overview of the methods, challenges, and prospects[J]. Information Fusion 76:376–421CrossRef
5.
go back to reference Pan W, Xia W, Jiang FS et al (2020) Stretchable strain sensor for human motion monitoring based on an intertwined-coil configuration[J]. Nanomaterials 10(10):168–187CrossRef Pan W, Xia W, Jiang FS et al (2020) Stretchable strain sensor for human motion monitoring based on an intertwined-coil configuration[J]. Nanomaterials 10(10):168–187CrossRef
6.
go back to reference Zhou Y, Wu X, Li X (2021) Prediction model of sports injury based on dynamic sampling and transfer learning[J]. Microprocess Microsyst 80(02):103583–103591CrossRef Zhou Y, Wu X, Li X (2021) Prediction model of sports injury based on dynamic sampling and transfer learning[J]. Microprocess Microsyst 80(02):103583–103591CrossRef
7.
go back to reference He K (2021) Prediction model of juvenile football players’ sports injury based on text classification technology of machine learning[J]. Mob Inf Syst 20(2):1–10 He K (2021) Prediction model of juvenile football players’ sports injury based on text classification technology of machine learning[J]. Mob Inf Syst 20(2):1–10
8.
go back to reference Zhao J, Zhang J (2021) Motion vision system of sports injury prediction based on adaptive monte carlo algorithm and real-time positioning - sciencedirect[J]. Microprocess Microsyst 13(02):104083–104089CrossRef Zhao J, Zhang J (2021) Motion vision system of sports injury prediction based on adaptive monte carlo algorithm and real-time positioning - sciencedirect[J]. Microprocess Microsyst 13(02):104083–104089CrossRef
9.
go back to reference Saputra A, Pramono H, Rumini R (2020) Effectiveness of KONI tuban monitoring system application in sports week preparation in East Java Province[J]. J Phys Educ Sports 18(46):187–195 Saputra A, Pramono H, Rumini R (2020) Effectiveness of KONI tuban monitoring system application in sports week preparation in East Java Province[J]. J Phys Educ Sports 18(46):187–195
10.
go back to reference Farley JB, Barrett LM, Keogh J et al (2020) The relationship between physical fitness attributes and sports injury in female, team ball sport players: a systematic review[J]. Sports Med - Open 6(1):45–58CrossRef Farley JB, Barrett LM, Keogh J et al (2020) The relationship between physical fitness attributes and sports injury in female, team ball sport players: a systematic review[J]. Sports Med - Open 6(1):45–58CrossRef
11.
go back to reference Chen MY, Kuo YL, Chou CY (2020) Lateral abdominal muscle symmetry and risk of sports injury in baseball players: 351 board #167 May 27 10:30 AM - 12:00 PM[J]. Med Sci Sports Exerc 52(7):83–83CrossRef Chen MY, Kuo YL, Chou CY (2020) Lateral abdominal muscle symmetry and risk of sports injury in baseball players: 351 board #167 May 27 10:30 AM - 12:00 PM[J]. Med Sci Sports Exerc 52(7):83–83CrossRef
12.
go back to reference Shuai L, Xinyu L, Shuai W et al (2021) Fuzzy-aided solution for out-of-view challenge in visual tracking under IoT assisted complex environment. Neural Comput Appl 33(4):1055–1065CrossRef Shuai L, Xinyu L, Shuai W et al (2021) Fuzzy-aided solution for out-of-view challenge in visual tracking under IoT assisted complex environment. Neural Comput Appl 33(4):1055–1065CrossRef
13.
go back to reference Nakano N, Sakura T, Ueda K et al (2020) Evaluation of 3D markerless motion capture accuracy using openpose with multiple video cameras[J]. Front Sports Active Living 2(42):56–68 Nakano N, Sakura T, Ueda K et al (2020) Evaluation of 3D markerless motion capture accuracy using openpose with multiple video cameras[J]. Front Sports Active Living 2(42):56–68
14.
go back to reference Jia X, Wang T, Liu J et al (2020) Gait recognition and intention perception method based on manikin mapping [J]. J Instrum 41(12):236–244 Jia X, Wang T, Liu J et al (2020) Gait recognition and intention perception method based on manikin mapping [J]. J Instrum 41(12):236–244
15.
go back to reference Liu S, Wang S, Liu X et al (2021) Fuzzy detection aided real-time and robust visual tracking under complex environments[J]. IEEE Trans Fuzzy Syst 29(1):90–102CrossRef Liu S, Wang S, Liu X et al (2021) Fuzzy detection aided real-time and robust visual tracking under complex environments[J]. IEEE Trans Fuzzy Syst 29(1):90–102CrossRef
16.
go back to reference Shui HW, Deepak RN, David SG et al (2021) COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis[J]. Information Fusion 68:131–148CrossRef Shui HW, Deepak RN, David SG et al (2021) COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis[J]. Information Fusion 68:131–148CrossRef
17.
go back to reference Zhang N, Wang Zi, Wang DL (2021) Rigid body motion attitude algorithm based on motion differential equation and acceleration [J]. Mech Des Manuf 368(10):215-219 + 224 Zhang N, Wang Zi, Wang DL (2021) Rigid body motion attitude algorithm based on motion differential equation and acceleration [J]. Mech Des Manuf 368(10):215-219 + 224
18.
go back to reference Gogoi H, Rajpoot Y, Sajwan A (2020) Sports specific injury pattern of sportspersons[J]. Int J Hum Mov Sports Sci 8(32):199–210 Gogoi H, Rajpoot Y, Sajwan A (2020) Sports specific injury pattern of sportspersons[J]. Int J Hum Mov Sports Sci 8(32):199–210
19.
go back to reference Liu S, Wang S, Liu X et al (2021) Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring. IEEE Trans Multimedia 23:2188–2198CrossRef Liu S, Wang S, Liu X et al (2021) Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring. IEEE Trans Multimedia 23:2188–2198CrossRef
20.
go back to reference Shui HW, Vishnu VG, Juan MG et al (2021) Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network[J]. Information Fusion 67(6):208–229 Shui HW, Vishnu VG, Juan MG et al (2021) Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network[J]. Information Fusion 67(6):208–229
Metadata
Title
Remote Sports Injury Monitoring using Wireless Sensor Networks
Authors
Ying Song
Gautam Srivastava
Publication date
25-08-2022
Publisher
Springer US
Published in
Mobile Networks and Applications / Issue 6/2023
Print ISSN: 1383-469X
Electronic ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-022-02028-z