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<p>Environmental internet of things can detect people's behavior through mobile phones or home sensors, providing a new way for early warning of psychological crisis. The existing methods are difficult to capture the change process of behavioral rhythm and rely on a large number of manually labeled data. This paper proposed a physics‑informed joint simulation‑recognition framework, which incorporates the biological clock law into the model to identify pre-crisis behavior patterns (e.g., increased nighttime activity and decreased socialization), and reduce label dependence through self-generated hard example training. The results show that the accuracy of the proposed method is 91.2% and 87.4%, which is about 8% and 6% higher than the best existing method, and the ability to correctly identify crisis individuals is increased by more than 10%. This provides a new idea for using internet of thing to achieve low-cost mental health monitoring.</p>

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Behavioral rhythm conversion calculation and psychological crisis early warning based on environmental internet of things perception

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月25日

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  • Tongtong Zhang

摘要

Environmental internet of things can detect people's behavior through mobile phones or home sensors, providing a new way for early warning of psychological crisis. The existing methods are difficult to capture the change process of behavioral rhythm and rely on a large number of manually labeled data. This paper proposed a physics‑informed joint simulation‑recognition framework, which incorporates the biological clock law into the model to identify pre-crisis behavior patterns (e.g., increased nighttime activity and decreased socialization), and reduce label dependence through self-generated hard example training. The results show that the accuracy of the proposed method is 91.2% and 87.4%, which is about 8% and 6% higher than the best existing method, and the ability to correctly identify crisis individuals is increased by more than 10%. This provides a new idea for using internet of thing to achieve low-cost mental health monitoring.