Towards intelligent workstations: Investigating the feasibility of Doppler radar sensors for personal respiratory quantification in thermal comfort

Building and Environment(2023)

引用 0|浏览0
暂无评分
摘要
Along the line of research efforts utilizing occupant thermophysiological responses in operation of Heating, Ventilation, and Air-Conditioning (HVAC), this study assessed feasibility of Doppler Radar Sensors (DRSs) in quantifying respiration. Respiration has been known as one of the physiological processes which gets adjusted based on metabolic rate and DRSs are capable of capturing characteristics of periodic movements of chest/ abdomen area induced by respiration. Accordingly, we have devised two approaches of quantifying respiration in context of thermoregulation states - by checking (1) frequency and intensity of the signal representing respiration (i.e., respiration state) and (2) ratio between exhale and inhale (i.e., respiratory ratio). With these, two experimental studies have been conducted with human subjects to evaluate reliability, applicability/sensitivity. Results showed that 60 s of a measurement time could provide reliability, which opens a chance to eliminate a validation process and respiration might not be a physiological response that rapidly gets activated for heat adjustment. Also, it has been shown that, with sufficient acclimation time (20 min), five out of eight participants increased respiration states in a high temperature setup and seven out of eight participants raised their respiratory ratios. In other words, individuals have different ways of utilizing respiration to deal with heat adjustment and acclimation time plays a key role in the use of respiration. Given DRSs' conventional application is occupancy detection, the versatility of DRSs has been shown and it contributes to realizing an intelligent workstation that is capable of contextualizing user's behaviors and perceptions.
更多
查看译文
关键词
Human building interaction,Doppler radar sensors,Respiration,Thermal comfort,Comfort -aware HVAC system
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要