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Classifying Accurate Heart Rate Measurements From Smartwatches

With the wide-distribution of smart wearables, it seems as though ubiquitous healthcare can finally permeate into our everyday lives, opening the possibility to realize clinical-grade applications. However, given that clinical applications require reliable sensing, there is a need to understand how accurate healthcare sensors on wearable devices (e.g., heart rate sensors) are. To answer this question, this work starts with a thorough investigation on the accuracy of widely used wearable devices’ heart rate sensors. Specifically, we show that when actively moving, heart rate readings can diverge far from the ground truth, and also show that such inaccuracies cannot be easily correlated, nor predicted, using accelerometer and gyroscope measurements. Rather, we point out that the light intensity readings at the photoplethysmography (PPG) sensor can be an effective indicator of heart rate accuracy. Using a Viterbi algorithm-based Hidden Markov Model, we show that it is possible to design a filter that allows smartwatches to self-classify measurement quality with ~98% accuracy.


Project Members: 
  • Jungmo Ahn
  • JeongGil Ko
  • Ho-Kyeong Ra (DGIST)
  • Hee Jung Yoon (DGIST)
  • Sang Hyuk Son (DGIST)


All software components used in this project is available through our Git Repository.


  • Ho-Kyeong Ra, Jungmo Ahn, Hee Jung Yoon, Sang Hyuk Son, JeongGil Ko, "Accurately Measuring Heart Rate using a Smartwatch", ACM SenSys 2016 (poster).
  • Ho-Kyeong Ra, Jungmo Ahn, Hee Jung Yoon, Dukyong Yoon, Sang Hyuk Son, JeongGil Ko, "I am a "Smart"watch,, Smart Enough to Know the Accuracy of My Own Heart Rate Sensor", ACM HotMobile 2017.

ACM HotMobile 2017 Presentation