Sahoh Bukhoree, Kliangkhlao Mallika, Haruehansapong Kanjana, Yeranee Kirttayoth, Punsawad Yunyong
IEEE J Biomed Health Inform. 2024 Dec;28(12):7565-7576. doi: 10.1109/JBHI.2024.3432766. Epub 2024 Dec 5.
Personal thermal comfort affects occupants' health, well-being, and productivity. Its satisfaction is subjective, based on individual characteristics and dynamic environments, and challenging to understand, requiring predicted outcomes and explanations of how and why the outcomes happen. This research fulfills this concern using a personal thermal comfort model based on causal artificial intelligence. It encodes personal thermal comfort satisfaction based on a new causal-and-effect framework to connect the human mind and environmental factors. Random variables encode relevant factors, and the structural causal model performs cause-and-effect relationships. The do-calculus (e.g., d-separated and d-connected) draws the common sense of the model based on causal structure representation, contributing to a human-intelligent understanding. A directed acyclic graph and exact-inference-based variable elimination quantify the model parameters based on real-world observational data. The strength of causal relationships is verified based on causal odd ratio, causal sensitivity, and causal impact. The results highlight that our proposed model can encode physiological and physical factors to predict and explain personal thermal comfort satisfaction. It can predict and explain such satisfaction reasonably and robustly, converging to human-like interpretation. It can be applied to intelligent systems to understand personal thermal comfort.
个人热舒适度会影响居住者的健康、幸福感和工作效率。其满意度是主观的，取决于个人特征和动态环境，难以理解，需要预测结果以及对结果如何发生和为何发生的解释。本研究使用基于因果人工智能的个人热舒适度模型来解决这一问题。它基于一个新的因果框架对个人热舒适度满意度进行编码，以连接人类思维和环境因素。随机变量对相关因素进行编码，结构因果模型执行因果关系。干预演算（例如，d-分离和d-连接）基于因果结构表示得出模型的常识，有助于实现人类智能理解。有向无环图和基于精确推理的变量消除基于实际观测数据对模型参数进行量化。基于因果优势比、因果敏感性和因果影响来验证因果关系的强度。结果表明，我们提出的模型可以对生理和物理因素进行编码，以预测和解释个人热舒适度满意度。它能够合理且稳健地预测和解释这种满意度，趋向于类似人类的解释。它可应用于智能系统以理解个人热舒适度。
