Assessment of psychophysiological characteristics using heart rate from naturalistic face video data

Author(s):  
Abhijit Sarkar ◽  
A. Lynn Abbott ◽  
Zachary Doerzaph
Keyword(s):  
Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 442
Author(s):  
Meiqing Wang ◽  
Ali Youssef ◽  
Mona Larsen ◽  
Jean-Loup Rault ◽  
Daniel Berckmans ◽  
...  

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.


2020 ◽  
Vol 7 ◽  
pp. 205566832095019
Author(s):  
Louise IR Castillo ◽  
M Erin Browne ◽  
Thomas Hadjistavropoulos ◽  
Kenneth M Prkachin ◽  
Rafik Goubran

Introduction Technological advances have allowed for the estimation of physiological indicators from video data. FaceReader™ is an automated facial analysis software that has been used widely in studies of facial expressions of emotion and was recently updated to allow for the estimation of heart rate (HR) using remote photoplethysmography (rPPG). We investigated FaceReader™-based heart rate and pain expression estimations in older adults in relation to manual coding by experts. Methods Using a video dataset of older adult patients with and without dementia, we assessed the relationship between FaceReader’s™ HR estimations against a well-established Video Magnification (VM) algorithm during baseline and pain conditions. Furthermore, we examined the correspondence between the Facial Action Coding System (FACS)-based pain scores obtained through FaceReader™ and manual coding. Results FaceReader’s™ HR estimations were correlated with VM algorithm in baseline and pain conditions. Non-verbal FaceReader™ pain scores and manual coding were also highly correlated despite discrepancies between the FaceReader™ and manual coding in the absolute value of scores based on pain-related facial action coding of the events preceding and following the pain response. Conclusions Compared to expert manual FACS coding and optimized VM algorithm, FaceReader™ showed good results in estimating HR values and non-verbal pain scores.


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