The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise
Open accessAbstract
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.
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
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Cited by
Ghamari, M. (2018) · International Journal of Biosensors & Bioelectronics · doi:10.15406/ijbsbe.2018.04.00125 · Cited by 945
Shcherbina, A., Mattsson, C., Waggott, D., Salisbury, H., Christle, J., Hastie, T., Wheeler, M., & Ashley, E. (2017) · Journal of Personalized Medicine · doi:10.3390/jpm7020003 · Cited by 625
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625
“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], as well as those…”
BackgroundZhang, Z. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2406332 · Cited by 388
Self-citationA Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare
Alshehri, F., & Muhammad, G. (2021) · IEEE Access · doi:10.1109/access.2020.3047960 · Cited by 348
Metadata
- Authors
- Zhang, Z., Pi, Z., & Liu, B.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2015-02
- Volume
- 62
- Issue
- 2
- Pages
- 522-531
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2005741801
- Cited by
- 748
- References
- 4
- FWCI
- 24.66
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhang, Z., Pi, Z., & Liu, B. (2015). TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2014.2359372
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