Machine Learning-based Risk Prediction and Safety Management for Outdoor Sports Activities

Abstract

Participant safety is becoming increasingly important as outdoor sports activities gain popularity. A machine learning-based strategy for risk assessment and safety control in outdoor sports activities is presented in this paper. Our framework uses predictive modelling, sophisticated algorithms, and historical data analysis to identify potential dangers and improve safety procedures. It also considers participant profiles and environmental conditions. Comprehensive testing and validation are used to examine the model’s efficacy, showing that it can offer risk evaluations in real-time and support preventive safety measures. Our approach entails placing sensor-based Internet of Things (IoT) devices at building sites to gather extremely detailed temporal and geographic weather, building, and labour data. This data is then cooperatively used on the edge nodes to train Deep Neural Network (DNN) models in a cross-silos way. The present study makes a valuable contribution to sports safety by offering a clever approach that integrates technology and outdoor leisure to ensure participants have a safe and pleasurable experience. The experiment’s outcomes show how well the suggested strategy works to increase the adoption of construction safety management systems and lower the likelihood of future mishaps and fatalities. As a result, the system has improved speed and responsiveness, an important feature for time-sensitive applications like safety prediction.