Personalized and Trust-Aware Health Recommendation Policies for a Construction Workplace

2026-09-11

AI Summary

本文提出一种信任感知的健康推荐系统模型，通过基于模型的短期控制和无模型强化学习方法，为建筑工人提供个性化健康干预，平衡健康、生产力与信任。

Abstract

Construction workers face workplace risks such as fatigue, heat stress, and other physically demanding conditions that can negatively affect their health and safety. Although monitoring these risks is important, timely and personalized health interventions are also needed to help prevent negative impacts on workers' well-being and productivity. To this end, in this paper, we propose a model to capture the interactions between a trust-aware health recommender system and workers who differ in health and trust sensitivity. Specifically, in our proposed dynamic model, worker health evolves over time, worker trust is affected by both health and recommendation dynamics, and trust in turn affects compliance with future recommendations. Given this model, we characterize the recommender policy, including a health-based recommendation triggering threshold and the recommendation frequency. We do so using both model-based short-horizon control and model-free reinforcement learning. We then investigate how recommendation frequencies are adjusted for different workers to balance their health, productivity, and trust. Our findings provide insight into the design of personalized health recommendation policies in construction workplaces and beyond.

Problem

• construction workers
• health risks
• personalized health interventions
• trust-aware

Innovation

• trust-aware health recommender system
• dynamic model
• reinforcement learning
• recommendation frequency