Thermal comfort is an important factor in evaluating indoor environmental quality. However, accurately evaluating thermal comfort conditions is challenging owing to the lack of suitable methods for measuring individual factors such as the metabolic rate (M value). In this study, a M value evaluation method was developed using deep learning. The metabolic equivalent of task was measured for eight typical indoor tasks based on the ASHRAE Standard 55 (lying down, sitting, cooking, walking, eating, house cleaning, folding clothes, and handling 50 kg books) in 31 subjects (males: 16; and females: 15); the measurements were analyzed in terms of gender and body mass index (BMI). The experimental results were assessed using the reliability of the measured data, the M value difference in terms of gender and BMI, and the measurement accuracy. We developed a M value self-evaluation model using artificial intelligence, which achieved an average coefficient of variation (CV) of 12%. A third-party evaluation model was used to evaluate the M value of one subject based on the learning data acquired from the other 30 subjects; this model yielded a low CV of 54%. For high-activity tasks, males generally had higher M values than females, and the higher the BMI was, the higher was the M value. Contrarily, for low-activity tasks, the lower the BMI was, the higher was the M value. The breakthrough M value evaluation method presented herein is expected to improve thermal comfort control.

中文翻译：

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基于图像深度学习的代谢率估计方法

热舒适性是评估室内环境质量的重要因素。但是，由于缺乏合适的方法来测量代谢率（M值）等单个因素，因此准确评估热舒适性条件具有挑战性。在这项研究中，使用深度学习开发了一种M值评估方法。根据ASHRAE标准55（躺下，坐着，做饭，走路，吃饭，打扫卫生，折叠衣服和搬运50公斤书本），对31名受试者（男性：16岁；男性； 16岁； 18岁）进行了8次典型的室内任务的代谢当量测量。和女性：15）; 根据性别和体重指数（BMI）分析测量结果。实验结果使用实测数据的可靠性进行评估，M性别和BMI以及测量准确性方面的价值差异。我们使用人工智能开发了M值自我评估模型，该模型实现了12％的平均变异系数（CV）。第三方评估模型用于基于从其他30个主题中获取的学习数据来评估一个主题的M值；该模型产生了54％的低CV。对于高强度活动，男性通常比女性具有更高的M值，并且BMI越高，M值就越高。相反，对于低强度活动，BMI越低，M值越高。突破M 预期本文提出的数值评估方法将改善热舒适性控制。