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

By Zhilin Zhang, Zhouyue Pi, Benyuan Liu. IEEE Transactions on Biomedical Engineering, 2014

732 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine

View Paper: https://www.semanticscholar.org/paper/f2f9c868bf74c2747ace75b1b213548026626e36

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Kristof Van Laerhoven
2. Philip Schmidt
3. Ina Indlekofer
4. Attila Reiss
5. Zhilin Zhang

In Cardiology and Cardiovascular Medicine

1. Ina Indlekofer
2. Kristof Van Laerhoven
3. Philip Schmidt
4. Attila Reiss
5. Zhilin Zhang

Citations in the corpus

• Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks (2019, methodology): “We adapt this sliding window approach (window length: 8 s, window shift: 2 s) for heart rate estimation, as was done in previous work [5,15,30–32].”
• Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction (2015, methodology): “As in [4], a time window of 8 s was used to slide the simultaneous PPG signal and acceleration signals, with a step of 2 s.”
• Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, background): “While most datasets are used to recognize physical activity or daily activities [19, 20, 30, 31, 47, 59, 61, 143, 155, 188, 228, 251, 252, 264, 279, 282, 299, 327, 335], there are also a few datasets dedicated to hand gestures [49, 156], breathing patterns [58], and car assembly line activities [316”
• Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort (2016, unknown): “Microelectromechanical systems such as accelerometers and Light Emitting Diode (LED)-based physiological monitoring have been available for decades [2–7].”