Optimizing Wearable Technology Selection for Injury Prevention in Ice and Snow Athletes Using Interval-Valued Bipolar Fuzzy Programming

The growing importance of wearable technology in ice and snow sports highlights its role in injury prevention, where environmental hazards elevate injury risks. To address this, we propose a decision-making model using interval-valued bipolar fuzzy programming (IVBFP) for the optimal selection of wearable devices focused on athlete safety. The model employs multi-criteria decision-making (MCDM) methods to evaluate critical factors such as comfort, safety, durability, and real-time monitoring. Fuzzy logic enhances the precision and consistency of decision-making. The IVBFP model addresses vital challenges, including the diverse performance metrics of wearable devices and the uncertainty in expert evaluations. In comparison analyses, the model exhibited a 15% enhancement in judgment accuracy and a 12% decrease in uncertainty relative to conventional techniques. The results underscore the model’s proficiency in correctly forecasting devices that mitigate injury risks, providing improved athlete protection. The approach effectively incorporates expert viewpoints and subjective evaluations, diminishing harm risk in simulated and actual datasets. This research is significant both theoretically and practically. It offers a comprehensive framework to guarantee athlete safety in extreme conditions, connecting scholars and practitioners.

@article{Li2024,
  title     = {Optimizing Wearable Technology Selection for Injury Prevention in Ice and Snow Athletes Using Interval-Valued Bipolar Fuzzy Programming},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Aichen Li},
  doi       = {10.14569/IJACSA.2024.01511113},
  url       = {https://doi.org/10.14569/IJACSA.2024.01511113}
}