TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise

• Published in: IEEE transactions on biomedical engineering Vol. 62; no. 2; pp. 522 - 531
• Main Authors: Zhang, Zhilin, Pi, Zhouyue, Liu, Benyuan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2015
• Subjects: Acceleration; Adolescent; Adult; Algorithms; Estimation; Heart rate; Heart Rate - physiology; Humans; Male; Monitoring; Monitoring, Ambulatory - methods; Pattern Recognition, Automated - methods; Photoplethysmography - methods; Physical Endurance - physiology; Physical Exertion - physiology; Reproducibility of Results; Running - physiology; Sensitivity and Specificity; Signal resolution; Spectral analysis; Time series analysis; Wrist - physiology; Young Adult; photoplethysmograph (PPG); singular spectrum analysis (SSA); sparse signal reconstruction (SSR); Ambulatory monitoring; signal decomposition; heart rate monitoring; wearable computing
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2014.2359372

Heart rate monitoring using wrist-type photoplethysmographic signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this study, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, and the Pearson correlation between the estimates and the ground truth of heart rate was 0.992. This framework is of great values to wearable devices such as smartwatches which use PPG signals to monitor heart rate for fitness.