Key result
Facial infrared thermography combined with machine learning demonstrated strong correlations with photoplethysmography for estimating heart rate (r = 0.70), RR intervals (r = 0.67), TINN (r = 0.71), and pNN50 (r = 0.70).
Why the study?
Contactless heart rate measurements from facial videos degrade under poor illumination, prompting evaluation of infrared thermography to overcome this limitation.
Does facial infrared thermal imaging combined with machine learning accurately estimate heart rate variability parameters compared to photoplethysmography?
Cross-Sectional (n=32)
No
Does facial infrared thermal imaging combined with machine learning accurately estimate heart rate variability parameters compared to photoplethysmography?
Facial infrared thermal imaging combined with machine learning provides a viable contactless method for estimating heart rate variability parameters, showing strong to moderate correlations with standard photoplethysmography.
May enable contactless HRV estimation; leaves open prospective validation against ECG.
Heart rate variability (HRV) is a reliable tool for the evaluation of several physiological factors modulating the heart rate (HR). Importantly, variations of HRV parameters may be indicative of cardiac diseases and altered psychophysiological conditions. Recently, several studies focused on procedures for contactless HR measurements from facial videos. However, the performances of these methods decrease when illumination is poor. Infrared thermography (IRT) could be useful to overcome this limitation. In fact, IRT can measure the infrared radiations emitted by the skin, working properly even in no visible light illumination conditions. This study investigated the capability of facial IRT to estimate HRV parameters through a face tracking algorithm and a cross-validated machine learning approach, employing photoplethysmography (PPG) as the gold standard for the HR evaluation. The results demonstrated a good capability of facial IRT in estimating HRV parameters. Particularly, strong correlations between the estimated and measured HR ( r = 0.7), RR intervals ( r = 0.67), TINN ( r = 0.71), and pNN50 (%) ( r = 0.70) were found, whereas moderate correlations for RMSSD ( r = 0.58), SDNN ( r = 0.44), and LF/HF ( r = 0.48) were discovered. The proposed procedure allows for a contactless estimation of the HRV that could be beneficial for evaluating both cardiac and general health status in subjects or conditions where contact probe sensors cannot be used.
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Credico et al. (2022) conducted a cross-sectional in Healthy (n=32). Facial Infrared Thermal Imaging (IRT) vs. Photoplethysmography (PPG) was evaluated on Correlation between IRT-estimated and PPG-measured heart rate (HR) (r = 0.70). Facial infrared thermography combined with machine learning demonstrated strong correlations with photoplethysmography for estimating heart rate (r = 0.70), RR intervals (r = 0.67), TINN (r = 0.71), and pNN50 (r = 0.70).
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