Video based actigraphy and breathing monitoring from the bedside table of shared beds

Heinrich, Adrienne; Heesch, Frank van; Puvvula, B Bhargava; Rocque, Mukul

doi:10.1007/s12652-014-0247-6

Cited by 12 publications

(8 citation statements)

References 37 publications

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“…The capability of custom-made infrared imaging system for sleep monitoring had been also studied. A CMOS camera coupled with an infrared light operating at wavelength of 825 nm was applied for detecting the movement pattern during sleep [ 8 ]. Martinez and Stiefelhagen captured the fixed infrared dot matrix, and subsequently, the BR was estimated from the displacements of some dots caused by chest movements [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation

Zhai

et al. 2018

PLoS ONE

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To achieve the simultaneous and unobtrusive breathing rate (BR) and heart rate (HR) measurements during nighttime, we leverage a far-infrared imager and an infrared camera equipped with IR-Cut lens and an infrared lighting array to develop a dual-camera imaging system. A custom-built cascade face classifier, containing the conventional Adaboost model and fully convolutional network trained by 32K images, was used to detect the face region in registered infrared images. The region of interest (ROI) inclusive of mouth and nose regions was afterwards confirmed by the discriminative regression and coordinate conversions of three selected landmarks. Subsequently, a tracking algorithm based on spatio-temporal context learning was applied for following the ROI in thermal video, and the raw signal was synchronously extracted. Finally, a custom-made time-domain signal analysis approach was developed for the determinations of BR and HR. A dual-mode sleep video database, including the videos obtained under environment where illumination intensity ranged from 0 to 3 Lux, was constructed to evaluate the effectiveness of the proposed system and algorithms. In linear regression analysis, the determination coefficient (R2) of 0.831 had been observed for the measured BR and reference BR, and this value was 0.933 for HR measurement. In addition, the Bland-Altman plots of BR and HR demonstrated that almost all the data points located within their own 95% limits of agreement. Consequently, the overall performance of the proposed technique is acceptable for BR and HR estimations during nighttime.

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Section: Introductionmentioning

confidence: 99%

Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation

Zhai

et al. 2018

PLoS ONE

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“…Additionally, non-contact methods have been documented using low-cost cameras based on motion or remote PPG (rPPG). Motion-based methods [8][9][10] detect the respiratory induced movements of the chest and/or abdomen. The challenge with these methods is to differentiate between respiratory-induced movements and other movements which are not related to respiration.…”

Section: Introductionmentioning

confidence: 99%

Robust respiration detection from remote photoplethysmography

Gastel

Stuijk

Haan

2016

Biomed. Opt. Express

105

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Continuous monitoring of respiration is essential for early detection of critical illness. Current methods require sensors attached to the body and/or are not robust to subject motion. Alternative camera-based solutions have been presented using motion vectors and remote photoplethysmography. In this work, we present a non-contact camera-based method to detect respiration, which can operate in both visible and dark lighting conditions by detecting the respiratory-induced colour differences of the skin. We make use of the close similarity between skin colour variations caused by the beating of the heart and those caused by respiration, leading to a much improved signal quality compared to single-channel approaches. Essentially, we propose to find the linear combination of colour channels which suppresses the distortions best in a frequency band including pulse rate, and subsequently we use this same linear combination to extract the respiratory signal in a lower frequency band. Evaluation results obtained from recordings on healthy subjects which perform challenging scenarios, including motion, show that respiration can be accurately detected over the entire range of respiratory frequencies, with a correlation coefficient of 0.96 in visible light and 0.98 in infrared, compared to 0.86 with the best-performing non-contact benchmark algorithm. Furthermore, evaluation on a set of videos recorded in a Neonatal Intensive Care Unit (NICU) shows that this technique looks promising as a future alternative to current contact-sensors showing a correlation coefficient of 0.87.

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“…Naturally, contact-less vital signs monitoring technologies have attracted significant interest, which mainly include Audio [10], [11], [12], Video [13], [14], Bed sensors (e.g. Ballistocardiography (BCG), pressure and/or motion sensors based techniques) [15], [16], [17], [18], [19], [20] and RF sensing based techniques -e.g., mmWave, Frequency Modulated Continuous Wave (FMCW) radar, Pulse-Doppler radar, RFIDs and WiFi based techniques [21], [22], [23], [24], [25], [26], [27], [28].…”

Section: Introductionmentioning

confidence: 99%

On Goodness of WiFi based Monitoring of Vital Signs in the Wild

Ali¹,

Alloulah²,

Kawsar³

et al. 2020

Preprint

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WiFi channel state information (CSI) has emerged as a plausible modality for sensing different human vital signs, i.e. respiration and body motion, as a function of modulated wireless signals that travel between WiFi devices. Although a remarkable proposition, most of the existing research in this space struggles to withstand robust performance beyond experimental conditions. To this end, we take a careful look at the dynamics of WiFi signals under human respiration and body motions in the wild. We first characterize the WiFi signal components -multipath and signal subspace -that are modulated by human respiration and body motions. We extrapolate on a set of transformations, including first-order differentiation, max-min normalization and component projections, that faithfully explains and quantifies the dynamics of respiration and body motions on WiFi signals. Grounded in this characterization, we propose two methods: 1) a respiration tracking technique that models the peak dynamics observed in the time-varying signal subspaces and 2) a body-motion tracking technique built with a multi-dimensional clustering of evolving signal subspaces. Finally, we reflect on the manifestation of these techniques in a practical sleep monitoring application. Our systematic evaluation with over 550 hours of data from 5 users covering both line-of-sight (LOS) and non-line-of-sight (NLOS) settings shows that the proposed techniques can achieve comparable performance to purpose-built pulse-Doppler radar.

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Video based actigraphy and breathing monitoring from the bedside table of shared beds

Cited by 12 publications

References 37 publications

Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation

Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation

Robust respiration detection from remote photoplethysmography

On Goodness of WiFi based Monitoring of Vital Signs in the Wild

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