热舒适性
可穿戴计算机
工作温度
模拟
平均绝对误差
空气温度
计算机科学
热感觉
个性化
心率变异性
皮肤温度
环境科学
汽车工程
工程类
均方误差
心率
统计
生物医学工程
数学
气象学
嵌入式系统
医学
物理
万维网
血压
放射科
作者
Lorenzo Scalise,Vittoria Cipollone,Sara Casaccia,Gian Marco Revel
出处
期刊:Measurement
[Elsevier]
日期:2024-01-01
卷期号:224: 113897-113897
标识
DOI:10.1016/j.measurement.2023.113897
摘要
This paper presents an experiment for assessing thermal comfort of occupants in the built environment, from a subjective perspective, focusing on office environment; a dedicated measurement campaign using sensors for acquisition of physiological and environmental parameters was conducted. Skin temperature was measured with two sensors: a minimally invasive sensor for measuring wrist temperature, and a thermal camera to retrieve forehead temperature; simultaneously, heart rate variability was measured using a wearable device. 15 participants were exposed to dynamic changes of air temperature. Data was collected to measure the participants’ thermal sensation vote, with machine learning algorithms. Decision Tree provided higher performances, using a dataset made of wrist temperature, heart rate variability features and air temperature, with mean average error and mean absolute percentage error of 0.86 and 20.9%. The research contributes to thermal comfort personalization in the built environment, to improve well-being and productivity of occupants using minimally invasive sensor network.
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