Analyzing biomechanical force characteristics in sports performance monitoring using biochemical sensors and internet of things devices
Abstract
This study explores the application of Internet of Things (IoT) devices and biochemical sensors in sports performance monitoring, focusing on the biomechanical force characteristics of athletes to address limitations in traditional methods, such as limited data types, poor real-time accuracy, and insufficient visualization. Emphasizing mechanobiological principles, the analysis targets key force-producing regions of the body—such as the feet, legs, and torso—to optimize energy efficiency, motion precision, and overall athletic performance. Biochemical sensors were employed to monitor real-time biomechanical and physiological data, while IoT devices ensured accurate data transmission, visualization, and feedback. Data accuracy was enhanced through methods such as zero correction, timestamp synchronization, and Kalman filtering, while data transmission efficiency was optimized using a lossless compression algorithm, hierarchical structuring, the MQTT protocol, and encryption via the AES algorithm. Data organization utilized a star-structured MySQL database with composite indexing for swift access. Analytical tools such as the Apriori algorithm for data correlation, linear discriminant analysis for feature extraction, and multi-source data fusion enabled detailed visualization of performance metrics. Experimental applications in football and sprinting demonstrated the effectiveness of IoT-based monitoring. Football experiments captured multi-dimensional data on technical characteristics, while sprint tests recorded precise performance metrics, including real-time speed profiling and timing accuracy. For instance, in a 100-meter sprint test, an IoT system measured an athlete's performance at 12.54 seconds with 100% accuracy, surpassing manual timing methods. These findings highlight the transformative potential of IoT devices and biochemical sensors in sports analytics, offering enhanced accuracy, real-time tracking, and actionable insights to refine athletic performance and decision-making.
References
1. McGuigan H E, Hassmen P, Rosic N, Stevens C J. Monitoring of training in high-performance athletes: what do practitioners do[J]. J Sport Exerc Sci, 2021, 5(2): 121-129.
2. Zheng K. Research on the Physiological Monitoring and Evaluation of Pre-Competition Altitude Training for Zhejiang Elite Swimmers[J]. Physical Activity & Health, 2021, 5(1):64-70.
3. Talsnes R K, van den Tillaar R, Sandbakk O. Effects of increased load of low-versus high-intensity endurance training on performance and physiological adaptations in endurance athletes[J]. International Journal of Sports Physiology and Performance, 2021, 17(2): 216-225.
4. Kocakulak N A, Saygin A S. The Importance of Nano Biosensors and Ethical Elements in Sports Performance Analysis[J]. Natural and Applied Sciences Journal, 2020, 3(1): 17-27.
5. Rana M, Mittal V. Wearable sensors for real-time kinematics analysis in sports: A review[J]. IEEE Sensors Journal, 2020, 21(2): 1187-1207.
6. Plesa J, Kozinc Z, Sarabon N. A brief review of selected biomechanical variables for sport performance monitoring and training optimization[J]. Applied Mechanics, 2022, 3(1): 144-159.
7. Bian S, Ye S, Yang S. Application prospects for wearable body surface microfluidic system in sports[J]. Wearable Technology, 2024, 3(1): 88-100.
8. Raza T, Tufail M K, Ali A, Boakye A, Qi X, Qi Y, et al. Wearable and flexible multifunctional sensor based on laser-induced graphene for the sports monitoring system[J]. ACS Applied Materials & Interfaces, 2022, 14(48): 54170-54181.
9. Barbosa T M, Barbosa A C, Simbana Escobar D, Mullen G J, Cossor J M, Hodierne R, et al. The role of the biomechanics analyst in swimming training and competition analysis[J]. Sports Biomechanics, 2023, 22(12): 1734-1751.
10. Clemente F M, Silva A F, Clark C C T, Conte D, Ribeiro J, Mendes B, et al. Analyzing the seasonal changes and relationships in training load and wellness in elite volleyball players[J]. International Journal of Sports Physiology and Performance, 2020, 15(5): 731-740.
11. Wu Jiyi, Li Wenjuan, Cao Jian, Qian Shiyou, Zhang Qifei. Review of AIoT research on Intelligent Internet of Things [J]. Telecommunications science, 2021, 37(8): 1-17.
12. He Fenglu, Chen Jiaqi, Li Qinhao, Yi Yingqi, Zhang Yongjun. Application and development of Internet of Things technology in smart grid [J]. Power system protection and control, 2020, 48(3): 58-69.
13. Mouha R A. Internet of things (IoT)[J]. Journal of Data Analysis and Information Processing, 2021, 9(2): 77-101.
14. Baucas M J, Spachos P, Gregori S. Internet-of-Things devices and assistive technologies for health care: Applications, challenges, and opportunities[J]. IEEE Signal Processing Magazine, 2021, 38(4): 65-77.
15. Hadidi R, Cao J, Ryoo M S, Kim H. Toward collaborative inferencing of deep neural networks on Internet-of-Things devices[J]. IEEE Internet of Things Journal, 2020, 7(6): 4950-4960.
16. Gungor O, Rosing T S, Aksanli B. Dowell: diversity-induced optimally weighted ensemble learner for predictive maintenance of industrial internet of things devices[J]. IEEE Internet of Things Journal, 2021, 9(4): 3125-3134.
17. Cao J, Zhu T, Ma R, Guo, Z, Zhang Y. A Software-Based Remote Attestation Scheme for Internet of Things Devices[J]. IEEE Transactions on Dependable and Secure Computing, 2022, 20(2): 1422-1434.
18. Hubrechsen A, Remley K A, Catteau S. Reverberation chamber metrology for wireless internet of things devices: Flexibility in form factor, rigor in test[J]. IEEE Microwave Magazine, 2022, 23(2): 75-85.
19. Jusak J, Puspasari I, Kusumawati W I. A semi-automatic heart sounds identification model and its implementation in Internet of Things devices[J]. Advances In Electrical and Computer Engineering (AECE), 2021, 21(1): 45-56.
20. Nie Pengcheng, Zhang Hui, Geng Hongliang, Wang Zheng, He Yong. The current situation and development trend of Agricultural Internet of Things technology [J]. Journal of Zhejiang University (Agriculture and Life Sciences Edition)), 2021, 47(2): 135-146.
21. Lei Ronghua, Chen Li. Design of fault-tolerant algorithm of self-correcting feedback neural network for space robot based on scale factor recognition [J]. Control and decision-making, 2020, 35(8): 1833-1840.
22. Li X, Kennel R. General formulation of Kalman-filter-based online parameter identification methods for VSI-fed PMSM[J]. IEEE Transactions on Industrial Electronics, 2020, 68(4): 2856-2864.
23. Singh A K. Major development under Gaussian filtering since unscented Kalman filter[J]. IEEE/CAA Journal of Automatica Sinica, 2020, 7(5): 1308-1325.
24. Baidoo P K. Comparative Analysis of the Compression of Text Data Using Huffman, Arithmetic, Run-Length, and Lempel Ziv Welch Coding Algorithms[J]. Journal of Advances in Mathematics and Computer Science, 2023, 38(9): 144-156.
25. Wang G, Peng H, Tang Y, Jin Y. Error repair technology of lempel-ziv-welch (LZW) compression data[J]. Transactions of Beijing institute of Technology, 2020, 40(5): 562-569.
26. Gao X, Liu R, Kaushik A. Hierarchical multi-agent optimization for resource allocation in cloud computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 32(3): 692-707.
27. Hintaw A J, Manickam S, Aboalmaaly M F, Karuppayah S. MQTT vulnerabilities, attack vectors and solutions in the internet of things (IoT)[J]. IETE Journal of Research, 2023, 69(6): 3368-3397.
28. Salman A D. IoT monitoring system based on MQTT publisher/subscriber protocol[J]. IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2020, 20(3): 75-83.
29. Muttaqin K, Rahmadoni J. Analysis and design of file security system AES (advanced encryption standard) cryptography-based [J]. Journal of Applied Engineering and Technological Science (JAETS), 2020, 1(2): 113-123.
30. Teng L, Li H, Yin S, Sun Y. A Modified Advanced Encryption Standard for Data Security[J]. Int. J. Netw. Secur., 2020, 22(1): 112-117.
31. Santoso M H. Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom[J]. Brilliance: Research of Artificial Intelligence, 2021, 1(2): 54-66.
32. Kusak L, Unel F B, Alptekin A, Celik M O, Yakar M. Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping[J]. Open Geosciences, 2021, 13(1): 1226-1244.
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