TY - GEN
T1 - Remote HeartRate Measurement based on Signal Feature Detection in Time Domain
AU - Wu, Bing Fei
AU - Yang, Yin Yin
AU - Tsai, Bing Ruei
AU - Huang, Po Wei
AU - Tsai, Yin Cheng
AU - Chen, Kuan Hung
PY - 2019/7
Y1 - 2019/7
N2 - Remote-photoplethysmography (rPPG) is a contactless method which can measure Blood Volume Pulse (BVP) and several vital signals from human faces. Until now, most existing techniques depend on frequency analysis and regard the signal component with largest energy as Heart Rate (HR). Nonetheless, the property, that frequency analysis could not identify the instantaneous HR changes restricts the applications on Heart Rate Variability (HRV) analysis and Cardiac Arrhythmia Detection. Consequently, this paper proposes a time domain systolic peak selection algorithm. It reaches 89% Suc5 in stationary case and 90% Suc10 in indoor scenario which are neck and neck with frequency domain analysis. At the same time, the comparison method, Rei-En, gets Suc10 lower than 80% in both situations. We investigate the influence of dicrotic notch and utilize a concise method to prevent wrong Inter-beat interval (IBI) detection for HR and HRV estimation. Benefiting from the characteristic of time domain analysis, the first HR output could be estimated within five seconds, which is much shorter than seventeen seconds in frequency domain analysis. The results suggest that the proposed algorithm improves instantaneity and provides faster first output of rPPG in HR, HRV estimation.
AB - Remote-photoplethysmography (rPPG) is a contactless method which can measure Blood Volume Pulse (BVP) and several vital signals from human faces. Until now, most existing techniques depend on frequency analysis and regard the signal component with largest energy as Heart Rate (HR). Nonetheless, the property, that frequency analysis could not identify the instantaneous HR changes restricts the applications on Heart Rate Variability (HRV) analysis and Cardiac Arrhythmia Detection. Consequently, this paper proposes a time domain systolic peak selection algorithm. It reaches 89% Suc5 in stationary case and 90% Suc10 in indoor scenario which are neck and neck with frequency domain analysis. At the same time, the comparison method, Rei-En, gets Suc10 lower than 80% in both situations. We investigate the influence of dicrotic notch and utilize a concise method to prevent wrong Inter-beat interval (IBI) detection for HR and HRV estimation. Benefiting from the characteristic of time domain analysis, the first HR output could be estimated within five seconds, which is much shorter than seventeen seconds in frequency domain analysis. The results suggest that the proposed algorithm improves instantaneity and provides faster first output of rPPG in HR, HRV estimation.
KW - biomedical monitoring
KW - blood volume pulse (BVP)
KW - heart rate variability (HRV)
KW - remote-photoplethysmography (rPPG)
UR - http://www.scopus.com/inward/record.url?scp=85072928456&partnerID=8YFLogxK
U2 - 10.1109/ICSSE.2019.8823111
DO - 10.1109/ICSSE.2019.8823111
M3 - Conference contribution
AN - SCOPUS:85072928456
T3 - Proceedings of 2019 International Conference on System Science and Engineering, ICSSE 2019
SP - 88
EP - 93
BT - Proceedings of 2019 International Conference on System Science and Engineering, ICSSE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 July 2019 through 21 July 2019
ER -