用可穿戴设备和AI预测建筑工人中暑，准确率达95.4%
Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics
- 用智能手表采集心率等生理数据，训练注意力LSTM模型预测热应激
- 模型测试准确率达95.4%，误报漏报显著降低，各项指标超0.98
- 结果可解释，适合接入工地物联网与BIM系统，助力主动安全管理
建筑工人易受热应激影响，但能将实时生理数据转化为可操作安全信息的工具仍稀缺。本研究针对沙特阿拉伯19名工人，开发并评估了基于深度学习的长短期记忆（LSTM）网络及注意力增强型LSTM模型，用于预测热应激。通过Garmin Vivosmart 5智能手表监测心率、心率变异性（HRV）和血氧饱和度等指标，注意力模型表现更优，测试准确率达95.40%，精度、召回率和F1分数分别达0.982，显著减少假阳性和假阴性。该方法不仅提升预测性能，还提供可解释结果，适用于集成至物联网安全系统与BIM可视化平台，推动建筑业向信息化、主动式安全管理演进。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。