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Advances in Nano Research Volume 20, Number 1, January 2026 , pages 1-15 DOI: https://doi.org/10.12989/anr.2026.20.1.001 |
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Nano-sensor data and machine learning for predictive sports injury risk management and crisis communication |
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Yuelin Si, Zicheng Zhao, Pengfei Wei
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| Abstract | ||
| With the rapid maturation process of nanotechnology, it is now possible to develop extremely sensitive, low-weight, and nondestructive nano-sensors capable of delivering physiological and biomechanical measurements with the highest level of accuracy ever. This paper discusses how data on nano-sensors can be incorporated alongside machine learning to forecast sports injury at risk and communicate an emergency during a professional and mass sporting event. Nano-sensors in wearable devices are employed to provide rich data sets in dynamically changing settings to ensure the ongoing detection of significant indicators such as joint loading, muscle fatigue, hydration, and micro-level effects. Machine intelligence models developed on these datasets will identify signs of a potential injury early and take proactive measures to intervene on behalf of the athletes and organizers of the event. In addition, the framework can be applied to sports crisis communication by using predictive understanding to provide information to the stakeholders to minimize the danger of adverse publicity to the event following a crisis associated with injuries at a major event. The results indicate that machine learning tools operating on nano- sensors lead to significantly increased accurate and timely injury prediction and data-driven evidence on transparent and effective crisis communication. The article attracts attention to the two-fold usefulness of nano-enabled technologies to increase the safety of athletes and the confidence of people in organization of sport events. | ||
| Key Words | ||
| crisis communication in sports events; machine learning; nano-sensors; sports injury prediction; wearable technology | ||
| Address | ||
| Yuelin Si: Institute for Sport Business, Loughborough University, London, E20 3BS, UK Zicheng Zhao: Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USA Pengfei Wei: College of Physical Education and Health, Changsha Medical University, 410219, China | ||