Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics

Summary

Researchers in Saudi Arabia developed and evaluated deep learning models, specifically LSTM and attention-based LSTM, to predict heat stress among construction workers. The study monitored physiological data like heart rate and oxygen saturation from 19 workers using Garmin Vivosmart 5 smartwatches. The attention-based model achieved 95.40% testing accuracy with superior precision, recall, and F1 scores of 0.982, significantly outperforming the baseline. Why it matters: This approach offers a proactive, data-driven solution for enhancing worker safety in extreme heat conditions, particularly relevant for the construction sector in the Middle East.