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

TL;DR

This paper develops deep learning models using wearable sensor data to accurately predict heat stress in construction workers, enabling proactive safety management in extreme heat conditions.

Contribution

It introduces an attention-based LSTM model that outperforms baseline models in predicting heat stress from physiological data collected via smartwatches.

Findings

Attention-based LSTM achieved 95.40% accuracy.

Model significantly reduced false positives and negatives.

High precision, recall, and F1 scores of 0.982.

Abstract

Construction workers are highly vulnerable to heat stress, yet tools that translate real-time physiological data into actionable safety intelligence remain scarce. This study addresses this gap by developing and evaluating deep learning models, specifically a baseline Long Short-Term Memory (LSTM) network and an attention-based LSTM, to predict heat stress among 19 workers in Saudi Arabia. Using Garmin Vivosmart 5 smartwatches to monitor metrics such as heart rate, HRV, and oxygen saturation, the attention-based model outperformed the baseline, achieving 95.40% testing accuracy and significantly reducing false positives and negatives. With precision, recall, and F1 scores of 0.982, this approach not only improves predictive performance but also offers interpretable results suitable for integration into IoT-enabled safety systems and BIM dashboards, advancing proactive, informatics-driven safety management in the construction industry.