Smart Movement Science Using Smart Kinetics Wearable Tech to Quantify Human Performance
Keywords:
Smart Movement Science, Wearable Technology, Biomechanical Data, Joint Angles, Injury PreventionAbstract
Smart Movement Science represents a revolutionary approach to understanding and optimizing human movement through the use of advanced wearable technology and real-time data analytics. By integrating motion sensors, accelerometers, and gyroscopes into wearable devices, Smart Movement Science captures detailed biomechanical data during physical activities, such as joint angles, force generation, and muscle activation. These devices collect continuous movement data, which is then analyzed using machine learning algorithms to provide insights into an individual's posture, coordination, balance, and performance efficiency. Movement science is the study of how humans move and how to optimize performance. With advancements in wearable technology, this research explores a novel technique known as SmartKinetics. SmartKinetics leverages real-time motion sensors embedded in
wearables to capture detailed biomechanical data during physical activities. The technique uses advanced algorithms to analyze joint angles, force generation, and muscle activity, offering valuable insights for athletes, rehabilitation professionals, and health experts. The integration of AI-powered feedback systems enables personalized performance enhancement and injury prevention strategies. In a simulation study designed to assess the effectiveness of the SmartKinetics system, a group of 150 athletes used wearable devices embedded with motion sensors, accelerometers, and gyroscopes to capture detailed biomechanical data during physical activities such as running, lifting, and cycling. The system analyzed key metrics such as joint angles, force generation, and muscle activation to provide real-time feedback. Results from the study demonstrated notable improvements in both performance and injury prevention. Athletes using SmartKinetics exhibited an average 15% improvement in overall performance efficiency,
measured by reduced time to complete physical tasks and increased power output. Specifically, those engaged in strength training showed a 12% increase in force generation and muscle activation, while runners experienced a 10% reduction in joint strain and injuries, compared to baseline measurements taken before using the system. The personalized feedback provided by the system helped users optimize their movements, with 80% of participants reporting fewer injuries and 85% indicating improved movement efficiency. Additionally, 90% of users stated that the AI-powered feedback system was helpful in refining their techniques, further demonstrating the impact of real-time analytics on performance enhancement and injury prevention.
References
[1] J. Yao and Y. Li, “Youth sports special skills’ training and evaluation system based on machine learning,” Mobile Information Systems, vol.2022, 2022.
[2] M. Şimşek and I. Kesilmiş, “Predicting athletic performance from physiological parameters using machine learning: Example of bocce ball,” Journal of Sports Analytics, vol.8, no.4, pp.299-307, 2022.
[3] O. Bodemer, “Enhancing Individual Sports Training through Artificial Intelligence: A Comprehensive Review,” Authorea Preprints, 2023.
[4] I. Ghosh, S.R. Ramamurthy, A. Chakma and N. Roy, “Decoach: Deep learning-based coaching for badminton player assessment,” Pervasive and Mobile Computing, vol.83, pp.101608, 2022.
[5] J. W. Teunissen, I. R. Faber, J. De Bock, M. Slembrouck, S. Verstockt et al., “A machine learning approach for the classification of sports based on a coaches’ perspective of environmental, individual and task requirements: A sports profile analysis,” Journal of Sports Sciences, pp.1-10, 2023.
[6] E. Dorschky, V. Camomilla, J. Davis, P. Federolf, J. Reenalda et al., “Perspective on “in the wild” movement analysis using machine learning,” Human movement science, vol.87, pp.103042, 2023.
[7] J. Yao and Y. Li, “Research Article Youth Sports Special Skills’ Training and Evaluation System Based on Machine Learning,” Mobile Information System, vol.2022, no.1, 2022
[8] X. Jiang, “Obstacles and regulation deconstruction of athletes’ psychological training based on machine learning algorithm in physical education teaching,” International Transactions on Electrical Energy Systems, vol.2022, 2022.
[9] R.S. Nagovitsyn, R. A. Valeeva and L.A. Latypova, “Artificial Intelligence Program for Predicting Wrestlers’ Sports Performances,” Sports, vol.11, no.10, pp.196, 2023.
[10] H. Su, Z. Su and Y. Xia, “The effect of physical training of athletes based on parametric bayesian estimation in the context of big data,” Mathematical Problems in Engineering, vol.2022, pp.1-10, 2022.
[11] P. E. Dandrieux, L. Navarro, D. Blanco, A. Ruffault, C. Ley et al., “Relationship between a daily injury risk estimation feedback (I-REF) based on machine learning techniques and actual injury risk in athletics (track and field): protocol for a prospective cohort study over an athletics season,” BMJ open, vol.13, no.5, pp.e069423, 2023.
[12] G. Li and X. Huang, “Developing a personalized chat-based AI model for enhanced sports education: a case study,” In Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), vol. 12709, pp. 1648-1655, 2023E.
[13] B. Giles, P. Peeling, S. Kovalchik and M. Reid, “Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach,” European Journal of Sport Science, vol.23, no.1, pp.44-53, 2023.
[14] D.A. Bonilla, I. A. Sánchez-Rojas, D. Mendoza-Romero, Y. Moreno, J. Kočí et al., “Profiling physical fitness of physical education majors using unsupervised machine learning,” International Journal of Environmental Research and Public Health, vol.20, no.1, pp.146, 2022.
[15] G. Wang and T. Ren, “Design of sports achievement prediction system based on U-net convolutional neural network in the context of machine learning,” Heliyon, 2024.
[16] A. Rossi, E. Perri, L. Pappalardo, P. Cintia, G. Alberti et al., “Wellness forecasting by external and internal workloads in elite soccer players: a machine learning approach,” Frontiers in Physiology, vol.13, pp.896928, 2022.
[17] W. Ren, X. Chen, F. Zhang, J. Alfred Daniel and D. Praveen Kumar, “The supervised learning model for analyzing the sportsperson training efficiency,” Journal of Interconnection Networks, vol.22, no.Supp01, pp.2141011, 2022.
[18] H. Liu and X. Zhu, “Design of the physical fitness evaluation information management system of sports athletes based on artificial intelligence,” Computational Intelligence and Neuroscience, vol.2022, 2022.
[19] A. Chatterjee, N. Pahari, A. Prinz and M. Riegler, “Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation,” Scientific Reports, vol.12, no.1, pp.19825, 2022.
[20] S. Wei, K. Wang and X. Li, “Design and implementation of college sports training system based on artificial intelligence,” International Journal of System Assurance Engineering and Management, vol.13, no.Suppl 3, pp.971-977, 2022.
[21] S. Zhang and F. Shan, “Feature Extraction of Athlete’s Post-Match Psychological and Emotional Changes Based on Deep Learning,” Computational Intelligence and Neuroscience, vol.2022, 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Computing in Education, Sports and Health (JCESH)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Fringe Global Scientific Press publishes all the papers under a Creative Commons Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) license. Authors have the liberty to replicate and distribute their work. Authors have the ability to use either the whole or a portion of their piece in compilations or other publications that include their own work. Please see the licensing terms for more information on reusing the work.
