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

2026-04-21 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices, Data Science & Analytics · Depth: Advanced, quick

Summary

This study introduces and evaluates deep learning models designed to predict heat stress among construction workers, a demographic highly susceptible to heat-related illnesses. Researchers monitored 19 workers in Saudi Arabia using Garmin Vivosmart 5 smartwatches, collecting real-time physiological data including heart rate, heart rate variability (HRV), and oxygen saturation. The attention-based Long Short-Term Memory (LSTM) network developed for this purpose achieved a 95.40% testing accuracy, outperforming a baseline LSTM model. This advanced model also demonstrated high precision, recall, and F1 scores of 0.982, significantly reducing prediction errors. The approach offers interpretable results, making it suitable for integration into IoT-enabled safety systems and Building Information Modeling (BIM) dashboards to enhance proactive safety management in construction.

Key takeaway

For AI Scientists developing predictive health analytics for high-risk occupations, this research demonstrates the efficacy of attention-based LSTM networks with wearable sensor data. You should consider implementing similar deep learning architectures to achieve high accuracy and interpretability in real-time health monitoring systems. This approach can significantly reduce false positives and negatives, improving the reliability of safety interventions.

Key insights

Wearable-derived physiological data, analyzed by attention-based LSTMs, can accurately predict heat stress in construction workers.

Principles

• Real-time physiological monitoring enhances safety.
• Attention mechanisms improve LSTM predictive power.

Method

The method involves collecting real-time physiological data via Garmin Vivosmart 5 smartwatches, then training and evaluating deep learning models (baseline LSTM and attention-based LSTM) to predict heat stress among construction workers.

In practice

• Integrate smartwatch data into safety systems.
• Utilize attention-based LSTMs for health prediction.

Topics

• Construction Worker Safety
• Heat Stress Prediction
• Wearable Technology
• Deep Learning Models
• Predictive Health Analytics

Best for: AI Scientist, Machine Learning Engineer, AI Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.