Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics
Researchers in Saudi Arabia built attention-enhanced LSTM models to predict heat stress in construction workers using smartwatch data, achieving 95.4% accuracy and reducing false alarms. The work demonstrates how interpretable deep learning can translate wearable physiological signals into real-time safety alerts for high-risk outdoor labor.
MentionsLSTM · Attention-based LSTM · Garmin Vivosmart 5 · Saudi Arabia
Read full story at arXiv cs.LG →(arxiv.org)
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