Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics
TLDR
This paper uses an attention-based LSTM with wearable data to predict heat stress in construction workers, achieving 95.4% accuracy.
Key contributions
- Developed deep learning models (LSTM, attention-based LSTM) to predict heat stress among workers.
- Utilized Garmin Vivosmart 5 smartwatches to monitor physiological data from 19 construction workers.
- Attention-based LSTM achieved 95.4% accuracy, outperforming baseline and reducing false positives/negatives.
- Offers interpretable results suitable for integration into IoT safety systems and BIM dashboards.
Why it matters
This research offers a crucial step towards proactive heat stress management in construction, leveraging real-time data. By integrating predictive analytics with wearable technology, it significantly enhances worker safety and operational efficiency in extreme heat environments.
Original 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.
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