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车内空气质量优化和代谢评估的无阻碍感测方法。

An Unobstructive Sensing Method for Indoor Air Quality Optimization and Metabolic Assessment within Vehicles.

机构信息

School of Engineering for Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85281, USA.

Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA.

出版信息

Sensors (Basel). 2020 Dec 16;20(24):7202. doi: 10.3390/s20247202.

DOI:10.3390/s20247202
PMID:33339222
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7766572/
Abstract

This work investigates the use of an intelligent and unobstructive sensing technique for maintaining vehicle cabin's indoor air quality while simultaneously assessing the driver metabolic rate. CO accumulation patterns are of great interest because CO can have negative cognitive effects at higher concentrations and also since CO accumulation rate can potentially be used to determine a person's metabolic rate. The management of the vehicle's ventilation system was controlled by periodically alternating the air recirculation mode within the cabin, which was actuated based on the CO levels inside the vehicle's cabin. The CO accumulation periods were used to assess the driver's metabolic rate, using a model that considered the vehicle's air exchange rate. In the process of the method optimization, it was found that the vehicle's air exchange rate (λ [h]) depends on the vehicle speeds, following the relationship: λ = 0.060 × (speed) - 0.88 when driving faster than 17 MPH. An accuracy level of 95% was found between the new method to assess the driver's metabolic rate (1620 ± 140 kcal/day) and the reference method of indirect calorimetry (1550 ± 150 kcal/day) for a total of N = 16 metabolic assessments at various vehicle speeds. The new sensing method represents a novel approach for unobstructive assessment of driver metabolic rate while maintaining indoor air quality within the vehicle cabin.

摘要

本研究旨在开发一种智能且无干扰的传感技术,用于在保持车内空气质量的同时评估驾驶员的代谢率。CO 积累模式是研究的重点,因为 CO 在较高浓度下会对认知产生负面影响,并且 CO 积累率可以用来确定人的代谢率。通过周期性地在车内切换空气再循环模式来控制车辆通风系统的管理,这是基于车内 CO 水平来驱动的。使用考虑车辆空气交换率的模型,利用 CO 积累周期来评估驾驶员的代谢率。在方法优化过程中,发现车辆的空气交换率(λ [h])取决于车速,其关系为:当车速大于 17 MPH 时,λ = 0.060 ×(速度)-0.88。新方法评估驾驶员代谢率(1620 ± 140 kcal/day)和间接量热法的参考方法(1550 ± 150 kcal/day)之间的准确性为 95%,总共有 N = 16 种在不同车速下的代谢评估。这种新的传感方法代表了一种在不干扰车内空气质量的情况下,对驾驶员代谢率进行非侵入性评估的新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/4f021e2fab65/sensors-20-07202-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/171d7e306779/sensors-20-07202-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/9548389dd764/sensors-20-07202-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/1f2dcdf60474/sensors-20-07202-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/ed7ed634ec31/sensors-20-07202-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/ae561bd716d1/sensors-20-07202-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/46ce82098dc8/sensors-20-07202-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/4f021e2fab65/sensors-20-07202-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/171d7e306779/sensors-20-07202-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/9548389dd764/sensors-20-07202-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/1f2dcdf60474/sensors-20-07202-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/ed7ed634ec31/sensors-20-07202-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/ae561bd716d1/sensors-20-07202-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/46ce82098dc8/sensors-20-07202-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfda/7766572/4f021e2fab65/sensors-20-07202-g007.jpg

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