2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 2 , PP: 60-77, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Prabhat Kr. Srivastava 1 , Ram Kinkar Pandey 2 , Gaurav Kumar Srivastava 3 , Nishant Anand 4 , Kunchanapalli Rama Krishna 5 , Prateek Singhal 6 , Aditi Sharma 7 *
Doi: https://doi.org/10.54216/JISIoT.130205
The amalgamation of wearable sensor technologies and artificial intelligence (AI) presents a transformative paradigm for optimising athletic performance in real time. This paper explores the integration of cutting-edge sensors - including bioimpedance sensors, accelerometers, and gyroscopes - with advanced AI algorithms such as machine learning and decision support systems. By capturing diverse physiological, biomechanical, and environmental data, the proposed framework aims to offer personalized, actionable insights for athletes. This research synthesizes the current landscape of wearable sensor technology in sports and highlights the evolving role of AI in interpreting data for enhancing athletic performance. It delineates an innovative framework designed for real-time analysis, personalized feedback, and training optimization. The seamless interaction between sensors and AI models empowers athletes and coaches to make informed decisions, optimizing training regimens and minimizing injury risks. The paper discusses the practical implications, challenges, and ethical considerations associated with this integration, emphasizing its potential benefits in diverse sports disciplines. Results from real-world trials underscore the efficacy of the proposed framework in providing dynamic guidance to athletes, thereby augmenting their performance through tailored interventions.
Wearable Sensors , Artificial Intelligence , Athletic Performance Enhancement, Real-time Analysis , Personalized Feedback , Training Optimization , Injury Prevention , Sports Technology.
[1] Y. Wang, L. Wang, J. Li, "A Secure and Efficient Data Transmission Protocol for Wearable Sensors in Athletic Training," IEEE Transactions on Mobile Computing, 2023.
[2] T. Wang, R. Zhang, X. Zhang, "A Personalized Exercise Prescription System using Wearable Sensors and Machine Learning," Journal of Personalized Medicine, 2023.
[3] S. Sun, D. Yang, Z. Wang, "A Wearable Sensor-Based System for Real-Time Monitoring of Mental Stress and Performance Optimization in Athletes," Sensors, 2023.
[4] Gajender Kumar,Vinod Patidar , Prolay Biswas, Mukta Patel,Chaur Singh Rajput,Anita Venugopa ,Aditi Sharma, IOT enabled Intelligent featured imaging Bone Fractured Detection System, Journal of Intelligent Systems and Internet of Things Vol. 09, No. 02, PP. 08-22, 2023 DOI: https://doi.org/10.54216/JISIoT.090201
[5] Q. Qu, J. Li, Y. Zhang, "A Deep Learning-Based Approach for Real-Time Monitoring of Technique Errors and Performance Optimization in Swimming," Frontiers in Sports and Active Living, 2023.
[6] Prashant K. Jamwal, Aibek Niyetkaliyev, Shahid Hussain, Aditi Sharma, Paulette Van Vliet, Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home,MethodsX,Volume 11,2023,102312,ISSN 2215-0161,https://doi.org/10.1016/j.mex.2023.102312.
[7] Atassi, Reem. , Sharma, Aditi. Intelligent Traffic Management using IoT and Machine Learning. Journal of Journal of Intelligent Systems and Internet of Things, vol. 8, no. 2, 2023, pp. 08-19. DOI: https://doi.org/10.54216/JISIoT.080201
[8] P. Patel, A. Singh, H. Patel, "A Wearable Sensor-based System for Personalized Sleep Monitoring and Performance Optimization in Athletes," IEEE Transactions on Biomedical Circuits and Systems, 2023.
[9] M. Li, Z. Sun, S. Wu, "A Blockchain-based Secure Data Sharing Platform for Athletes and Coaches using Wearable Sensors," IEEE Transactions on Information Forensics and Security, 2023.
[10] L. Li, J. Xu, M. Yang, "A Multi-Sensor Fusion Framework for Real-Time Monitoring of Physical Stress and Recovery in Athletes," Sensors, 2023.
[11] K. Kim, Y. Kim, S. Lee, "A Graph Neural Network-Based Approach for Gait Pattern Analysis and Performance Optimization in Running," IEEE Transactions on Human-Machine Systems, 2023.
[12] J. Jang, S. Park, K. Kim, "An Explainable AI Framework for Personalized Exercise Prescription and Injury Risk Assessment in Athletes," Healthcare, 2023.
[13] I. Hassan, J. Lee, K. Kim, "A Transfer Learning Approach for Activity Recognition and Performance Analysis in Different Sports," Journal of Ambient Intelligence and Humanized Computing, 2023.
[14] G. Gao, Y. Zhao, Y. Zhang, "A Deep Reinforcement Learning Approach for Adaptive Training Recommendation in Sports Performance Enhancement," IEEE Transactions on Cybernetics, 2023.
[15] F. Fang, M. Ding, S. Li, "A Bioimpedance Sensor-Based System for Muscle Fatigue Assessment and Training Optimization in Athletes," Sensors and Actuators B: Chemical, 2023.
[16] C. Chen, J. Zhang, Z. Li, "An Unsupervised Learning Framework for Anomaly Detection and Fatigue Prediction in Athletes," IEEE Access, 2023.
[17] Goar, V., Sharma, A., Yadav, N.S. et al. IoT-Based Smart Mask Protection against the Waves of COVID-19. J Ambient Intell Human Comput 14, 11153–11164 (2023). https://doi.org/10.1007/s12652-022-04395-7
[18] A. Alzahrani, A. Aljohani, A. Alyami, "An AI-powered Smart System for Real-Time Monitoring and Performance Enhancement of Athletes," Journal of Healthcare Engineering, 2023.
[19] M. A. Razzaq, S. Zahir, M. Shakeel, and T. Khan, "A survey on intelligent healthcare monitoring systems using wearable sensors and machine learning," Pervasive and Mobile Computing, vol. 81, p. 101535, 2022.
[20] A. K. Sharma, J. P. Singh, and K. L. Somani, "Wearable sensor technology for athletic performance improvement: A review of recent advancements and challenges," Materials Today: Proceedings, vol. 58, pp. 901-913, 2022.
[21] J. Xu, X. Tang, T. Zhou, and L. Yu, "Wearable sensors and artificial intelligence for athlete training and performance optimization: A review," Artificial Intelligence Review, vol. 55, no. 4, pp. 3653-3704, 2022.
[22] J. He, D. Chen, L. Li, and S. He, "A review of wearable sensor technology in sports biomechanics research: Gait analysis and beyond," Sports Medicine, vol. 51, no. 9, pp. 1641-1661, 2021.
[23] J. M. Hausenblas, E. M. Andrews, and C. L. Noonan, "Wearable sensors for athletic performance monitoring and injury prevention: a review of the literature," Journal of Sport and Health Science, vol. 10, no. 3, pp. 143-157, 2021.
[24] Z. Zhang, M. Zhang, Z. Wang, and K. Zhang, "Wearable sensors and AI-based athlete monitoring and performance enhancement: A review," International Journal of Human-Computer Interaction, vol. 37, no. 10, pp. 871-892, 2021.
[25] A. C. Neto, D. M. Silva, C. A. Silva, W. R. de Paula, and T. R. Oliveira, "Wearable sensors and artificial intelligence for personalized athletic training: A comprehensive review," Sensors, vol. 21, no. 9, p. 3135, 2021.
[26] Venkatesan, R.. , Santhosh, Gokul. , Preiya, Sathya. , Kumar, V.D.Ashok. Smart Wheelchair-An Effective Transport for Handicapped and Aged Citizens. Journal of Journal of Cognitive Human-Computer Interaction, vol. 4, no. 2, 2022, pp. 08-19. DOI: https://doi.org/10.54216/JCHCI.040201
[27] Y. Zhang, Q. Li, Y. Liu, Z. He, and X. Li, "A survey on wearable sensors for athletic performance monitoring," IEEE Sensors Journal, vol. 20, no. 2, pp. 540-552, 2020.
[28] M., D. M., Y. J., L. G., F. "Social sports Competition Scoring System Design Using Single Value Neutrosophic Environment," Journal of International Journal of Neutrosophic Science, vol. 19, no. 1, pp. 389-402, 2022. DOI: https://doi.org/10.54216/IJNS.190135
[29] S. Lee, Y. Kim, and C. Park, "Machine learning-based prediction of athletic performance using wearable sensor data," IEEE Transactions on Human-Machine Systems, vol. 49, no. 2, pp. 172-182, 2019.
[30] D. H. Kim, S. M. Won, and J. H. Lee, "A wearable sensor-based system for real-time monitoring of athletic performance," Sensors, vol. 19, no. 7, p. 1798, 2019.
