Infrared thermography based human respiration monitoring

Author(s):  
Preeti Jagadev ◽  
Lalat Indu Giri
2020 ◽  
Vol 11 (9) ◽  
pp. 4848
Author(s):  
Ilde Lorato ◽  
Sander Stuijk ◽  
Mohammed Meftah ◽  
Deedee Kommers ◽  
Peter Andriessen ◽  
...  

2018 ◽  
Vol 6 (16) ◽  
pp. 4549-4554 ◽  
Author(s):  
Bintian Li ◽  
Gang Xiao ◽  
Feng Liu ◽  
Yan Qiao ◽  
Chang Ming Li ◽  
...  

In this study, a silk fabric-based human respiration sensor was fabricated by successive electroless plating of conductive interdigital electrodes and spray-coating of a graphene oxide sensing layer.


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xiaoyi Wang ◽  
Yang Deng ◽  
Xingru Chen ◽  
Peng Jiang ◽  
Yik Kin Cheung ◽  
...  

AbstractThe humidity sensor is an essential sensing node in medical diagnosis and industrial processing control. To date, most of the reported relative humidity sensors have a long response time of several seconds or even hundreds of seconds, which would limit their real application for certain critical areas with fast-varying signals. In this paper, we propose a flexible and low-cost humidity sensor using vertically aligned carbon nanotubes (VACNTs) as electrodes, a PDMS-Parylene C double layer as the flexible substrate, and graphene oxide as the sensing material. The humidity sensor has an ultrafast response of ~20 ms, which is more than two orders faster than most of the previously reported flexible humidity sensors. Moreover, the sensor has a high sensitivity (16.7 pF/% RH), low hysteresis (<0.44%), high repeatability (2.7%), good long-term stability, and outstanding flexibility. Benefiting from these advantages, especially the fast response, the device has been demonstrated in precise human respiration monitoring (fast breathing, normal breathing, deep breathing, asthma, choking, and apnea), noncontact electrical safety warning for bare hand and wet gloves, and noncontact pipe leakage detection. In addition, the facile fabrication of the flexible platform with the PDMS-Parylene C double layer can be easily integrated with multisensing functions such as pH sensing, ammonium ion sensing, and temperature sensing, all of which are useful for more pattern recognition of human activity.


2009 ◽  
Author(s):  
Cheng Zhang ◽  
Chang-yun Miao ◽  
Hong-qiang Li ◽  
Hui-chao Song ◽  
Fan-jie Xu

Author(s):  
Jinyi Liu ◽  
Youwei Zeng ◽  
Tao Gu ◽  
Leye Wang ◽  
Daqing Zhang

Recent years have witnessed a trend of monitoring human respiration using Channel State Information (CSI) retrieved from commodity WiFi devices. Existing approaches essentially leverage signal propagation in a Line-of-Sight (LoS) setting to achieve good performance. However, in real-life environments, LoS can be easily blocked by furniture, home appliances and walls. This paper presents a novel smartphone-based system named WiPhone, aiming to robustly monitor human respiration in NLoS settings. Since a smartphone is usually carried around by one subject, leveraging directly-reflected CSI signals in LoS becomes infeasible. WiPhone exploits ambient reflected CSI signals in a Non-Line-of-Sight (NLoS) setting to quantify the relationship between CSI signals reflected from the environment and a subject's chest displacement. In this way, WiPhone successfully turns ambient reflected signals which have been previously considered "destructive" into beneficial sensing capability. CSI signals obtained from smartphone are usually very noisy and may scatter over different sub-carriers. We propose a density-based preprocessing method to extract useful CSI amplitude patterns for effective respiration monitoring. We conduct extensive experiments with 8 subjects in a real home environment. WiPhone achieves a respiration rate error of 0.31 bpm (breaths per minute) on average in a range of NLoS settings.


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