.png){kind=icon}
返回
Metabolic rate estimation method using image deep learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s12273-020-0707-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 (Mvalue). In this study, aMvalue 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, theMvalue difference in terms of gender and BMI, and the measurement accuracy. We developed aMvalue 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 theMvalue 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 higherMvalues than females, and the higher the BMI was, the higher was theMvalue. Contrarily, for low-activity tasks, the lower the BMI was, the higher was theMvalue. The breakthroughMvalue evaluation method presented herein is expected to improve thermal comfort control.
Keyword:
thermal comfort
predicted mean vote
deep learning
gender
body mass index
wearable device
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
1.6K
被引数:
4.5K
机构
引用论文
Introduction to thermal comfort standards and to the proposed new version of EN ISO 7730.png)
ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏Implementing Direct Access to Low-Dose Computed Tomography in General Practice—Method, Adaption and OutcomePLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏
Post-occupancy evaluation of 20 office buildings as basis for future IEQ standards and guidelinesENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏A Kinetic Model with Ordered Cytoplasmic Dissociation for SUC1, an Arabidopsis H + /Sucrose Cotransporter Expressed in Xenopus Oocytes在非洲爪蟾卵母细胞中表达的拟南芥H/蔗糖协同转运蛋白SUC1的有序细胞质解离动力学模型
PREAI
分享收藏The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls人工智能 (AI) 方法在建筑物热舒适性预测中的应用: 基于AI的热舒适性控制的能源含义ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏Do Perceptions of Competence Mediate The Relationship Between Fundamental Motor Skill Proficiency and Physical Activity Levels of Children in Kindergarten?能力的感知是否可以介导幼儿园儿童的基本运动技能熟练程度与身体活动水平之间的关系？
PREAI
分享收藏
查看更多内容