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Thermal comfort measurement using thermal-depth images for robotic monitoring

机译:热舒适测量使用热深图像进行机器人监控

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This paper describes an application of thermal-depth images to human thermal comfort measurement. A mobile monitoring of the elderly and residents of care houses is one of the promising applications of mobile assistive robots. Monitoring if a person feels comfortable is an important task of such robots. We rely on an established comfort measure in the architecture domain, namely, predicted mean vote (PMV). PMV is calculated mainly by six factors and one of which is the clothing insulation or do-value. Clo-values are usually measured by a thermal mannequin, a specially-designed apparatus for the purpose. We apply human recognition techniques in thermal-depth images to efficiently measure do-values, thereby enabling on-line assessment of thermal comfort. We evaluate the method and develop a mobile robot system for experimental testing. (C) 2019 Elsevier B.V. All rights reserved.
机译:本文介绍了热深图像对人热舒适度测量的应用。对Care Houses的老年人和居民的移动监控是移动辅助机器人的有希望的应用之一。监测如果一个人感到舒适是此类机器人的重要任务。我们依靠建筑领域的建立舒适度,即预测的平均投票(PMV)。 PMV主要由六个因素计算,其中一个是衣物绝缘或待价值。 CLO-值通常通过热身模特,专门设计的设备来测量。我们在热深图像中应用人类识别技术以有效地测量DO值,从而能够在线评估热舒适度。我们评估该方法并开发一种用于实验测试的移动机器人系统。 (c)2019 Elsevier B.v.保留所有权利。

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