Estimation of respiration rate using an accelerometer and thermal camera in eGlasses

Rumiński, Jacek; Bujnowski, Adam; Czuszynski, Krzysztof; Kocejko, Tomasz

doi:10.15439/2016f329

Cited by 15 publications

(10 citation statements)

References 16 publications

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“…For data capture, the FLIR ® Lepton thermal camera was used. Due to the small form factor of the device (a circuit board is smaller than 1 cm 2 ), this camera module can be incorporated in embedded platforms, allowing for application in various medical oriented solutions [45,46]. The Lepton camera is equipped with a thermal sensor capable of recording data in the 14-bit dynamic range and the 80 × 60 spatial resolution at 9 FPS.…”

Section: Datasetsmentioning

confidence: 99%

Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks

2019

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Estimation of vital signs using image processing techniques have already been proved tohave a potential for supporting remote medical diagnostics and replacing traditional measurementsthat usually require special hardware and electrodes placed on a body. In this paper, we furtherextend studies on contactless Respiratory Rate (RR) estimation from extremely low resolution thermalimagery by enhancing acquired sequences using Deep Neural Networks (DNN). To perform extensivebenchmark evaluation, we acquired two thermal datasets using FLIR® cameras with a spatialresolution of 80 × 60 and 320 × 240 from 71 volunteers in total. In-depth analysis of the proposedConvolutional-based Super Resolution model showed that for images downscaled with a factor of 2and then super-resolved using Deep Learning (DL) can lead to better RR estimation accuracy thanfrom original high-resolution sequences. In addition, if an estimator based on a dominating peakin the frequency domain is used, SR can outperform original data for a down-scale factor of 4 andimages as small as 20 × 15 pixels. Our study also showed that RR estimation accuracy is betterfor super-resolved data than for images with color changes magnified using algorithms previouslyapplied in the literature for enhancing vital signs patterns.

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Section: Datasetsmentioning

confidence: 99%

Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks

2019

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“…In [113], J. Ruminski et al introduced a smart glasses prototype and two algorithms to measure the respiratory rate from the accelerometer and thermal-camera data, respectively. The eGlasses prototype was equipped with an OMAP 4460 processor, a 5 MP camera, various sensors (including accelerometer and thermal sensor), Bluetooth and WiFi modules, extension slot and eye-tracker.…”

Section: Overview Of Innovative Wearable Systems Based On Inertial Sensors To Monitor the Respiratory Activitymentioning

confidence: 99%

“…Like those in [74,75], the belt-type devices offer greater reliability since they reduce the artifact since the IMUs are in solidarity with the body but lack wearability since the band could obstruct the user's movements. Besides, the devices in [113,115], applied on the nasal septum, represent an optimal solution for monitoring respiratory activity during daily life or sleep to detect respiratory dysfunctions. Finally, the in-ear device in [116] is a practical and accurate solution for detecting RR by processing the data provided by a product already present on the market (Nokia eSense).…”

Section: Overview Of Innovative Wearable Systems Based On Inertial Sensors To Monitor the Respiratory Activitymentioning

confidence: 99%

An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring

et al. 2021

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The demand for wearable devices to measure respiratory activity is constantly growing, finding applications in a wide range of scenarios (e.g., clinical environments and workplaces, outdoors for monitoring sports activities, etc.). Particularly, the respiration rate (RR) is a vital parameter since it indicates serious illness (e.g., pneumonia, emphysema, pulmonary embolism, etc.). Therefore, several solutions have been presented in the scientific literature and on the market to make RR monitoring simple, accurate, reliable and noninvasive. Among the different transduction methods, the piezoresistive and inertial ones satisfactorily meet the requirements for smart wearable devices since unobtrusive, lightweight and easy to integrate. Hence, this review paper focuses on innovative wearable devices, detection strategies and algorithms that exploit piezoresistive or inertial sensors to monitor the breathing parameters. At first, this paper presents a comprehensive overview of innovative piezoresistive wearable devices for measuring user’s respiratory variables. Later, a survey of novel piezoresistive textiles to develop wearable devices for detecting breathing movements is reported. Afterwards, the state-of-art about wearable devices to monitor the respiratory parameters, based on inertial sensors (i.e., accelerometers and gyroscopes), is presented for detecting dysfunctions or pathologies in a non-invasive and accurate way. In this field, several processing tools are employed to extract the respiratory parameters from inertial data; therefore, an overview of algorithms and methods to determine the respiratory rate from acceleration data is provided. Finally, comparative analysis for all the covered topics are reported, providing useful insights to develop the next generation of wearable sensors for monitoring respiratory parameters.

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“…Smart glasses could be used to create a better user experience while getting access to information and internet in a timely, accessible and safe manner along with video streams, tracking eye movement to determine the state of an individual and the list goes on. [7] Smart glasses fall under the category of smart wear which is a general term for combining everyday wear things to technology. Early prototypes of wearable smart devices emerged in the 1960s and devices embedded with this technology emerged in the 1970s and 1980s.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Smart Glasses Technology- Future of Eyewear

Aman¹

2021

TURCOMAT

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This paper breaks down the technology behind smart glasses along with the advancement in this emerging field keeping in mind the hardware, software and research work which major companies have put in to develop a real-life model. This paper also comprises an analysis on the experience of users with different glasses. We have also talked about the new benchmarks which this technology has set in sectors such as medicine, gaming, corporate, sports, entertainment and many others.

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Estimation of respiration rate using an accelerometer and thermal camera in eGlasses

Cited by 15 publications

References 16 publications

Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks

Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks

An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring

A Comprehensive Review of Smart Glasses Technology- Future of Eyewear

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