Automated vs. manual pain coding and heart rate estimations based on videos of older adults with and without dementia

Abstract

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.

Noninvasive monitoring technologies to identify discomfort and distressing symptoms in persons with limited communication at the end of life: A scoping review

“…They could recognize grimaces in simulated videos and emotions in people with mild cognitive impairment and were moderately to highly correlated with manual scoring of pain based on facial expressions in community-dwelling older adults and nursing home residents with dementia. (137,140)…”

“…(131) Clinimetrics of the pressure mats in measuring other symptoms were not reported in the included manuscripts.Several algorithms have been developed to recognize agitation, emotions, and pain by analyzing videos taken by commercially available cameras. Using remote photoplethysmography (rPPG), some systems can also estimate heart rhythm based on the change in light transmission and re ection of the skin due to changes in blood ow (137).…”

Remote Facial Expression and Heart Rate Measurements to Assess Human Reactions in Glass Structures

“…e latter relates to cardiac cycles and changes in blood volume based on video captured by the camera [40]. Different literature studies proved that the remote HR analysis can be powerful and efficient [41][42][43][44]. e limit requirement is that setup conditions for the experiments are properly addressed.…”

Analysis of a Video-Based Pain Monitoring System in Raspberry Pi

“…Recent works based on Computer Vision, automatically and non-invasively assess pain, based on different characteristics, such as facial expressions and movements [9], skin temperature detected by infrared cameras [15] and Heart Rate (HR) recognized by Remote PhotoPlethysmoGraphy (rPPG) [16].…”