Vasu Jindal
ABSTRACT
Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However, current determination of heart rate through mobile applications suffers from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for selection of PPG signals using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%.
- B. S. Kim, and S. K Yoo," Motion artifact reduction in photoplethysmography using independent component analysis.," IEEE Trans. Biomed. Eng..53, (3), 566--568 (2006).Google Scholar
Cross Ref
- M. Kumar, A. Veeraraghavan, and A. Sabharwal, "DistancePPG: Robust non-contact vital signs monitoring using a camera," Biomed. Opt. Express 6, 1565-1588 (2015)Google ScholarCross Ref
- J. R. Kwapisz, G. M. Weiss, S. A. Moore, Activity recognition using cell phone accelerometers, SIGKDD Explor.Newsl. 12 (2011) 74--82. Google ScholarDigital Library
- Z. Zhang, Z. Pi, B. iu, "TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise," IEEE Trans. on Biomed. Engineering, vol. 62, pp. 522--531, 2015.Google ScholarCross Ref
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