Video photoplethysmography (videoPPG) has emerged as area of great interest thanks to the possibility of remotely assessment of cardiovascular parameters, as heart rate (HR), respiration rate (RR) and heart rate variability (HRV). The present article proposes a fully automated method based on chrominance model, that selects for each subject the best region of interest (ROI) to detect and evaluate the accuracy of beat detection and interbeat intervals (IBI) measurements. The experimental recordings were conducted on 26 subjects which underwent a rest-to-stand maneuver. The results show that the accuracy of beat detection is slightly better during supine position (95%) compared to the standing one (92%), due to the maintenance of the balance that introduces larger motion artifact in the signal dynamic. The error in the measurement (expressed as mean±sd) of instantaneous heart rate is of +0.04 ±3.29 bpm in rest and +0.01±4.26 bpm in stand.

Assessment of beat-to-beat heart rate detection method using a camera as contactless sensor

IOZZIA, LUCA;CERINA, LUCA;MAINARDI, LUCA
2016-01-01

Abstract

Video photoplethysmography (videoPPG) has emerged as area of great interest thanks to the possibility of remotely assessment of cardiovascular parameters, as heart rate (HR), respiration rate (RR) and heart rate variability (HRV). The present article proposes a fully automated method based on chrominance model, that selects for each subject the best region of interest (ROI) to detect and evaluate the accuracy of beat detection and interbeat intervals (IBI) measurements. The experimental recordings were conducted on 26 subjects which underwent a rest-to-stand maneuver. The results show that the accuracy of beat detection is slightly better during supine position (95%) compared to the standing one (92%), due to the maintenance of the balance that introduces larger motion artifact in the signal dynamic. The error in the measurement (expressed as mean±sd) of instantaneous heart rate is of +0.04 ±3.29 bpm in rest and +0.01±4.26 bpm in stand.
2016
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
9781457702204
Signal Processing; Biomedical Engineering; 1707; Health Informatics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1021089
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