Abstract
Tracking heart rate for fitness using wrist-type wearables is challenging, because of the significant noise caused by intensive wrist movements. In this paper, we present FitBeat - a lightweight system that enables accurate heart rate tracking on wrist-type wearables during intensive exercises. Unlike existing approaches that rely on computation- intensive signal processing, FitBeat integrates and augments standard filter and spectral analysis tool, which achieves comparable accuracy while significantly reducing computational overhead. FitBeat integrates contact sensing, motion sensing and simple spectral analysis algorithms to suppress various error sources. We implement FitBeat on a COTS smartwatch, and evaluate the performance of FitBeat for typical workouts of different intensities, including walking, running and riding. Experimental results involving 10 subjects show that the average error of FitBeat is around 4 beats per minute, which improves heart rate accuracy of the default heart rate tracker of Moto 360 by 10x.
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