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. Given that such capabilities allow the smartwatch to internally filter misleading values from being application input, we foresee this as an essential step in catalyzing novel clinical-grade wearable applications.
|Title of host publication||HotMobile 2017 - Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||6|
|Publication status||Published - 2017 Feb 21|
|Event||18th International Workshop on Mobile Computing Systems and Applications, HotMobile 2017 - Sonoma, United States|
Duration: 2017 Feb 21 → 2017 Feb 22
|Name||HotMobile 2017 - Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications|
|Conference||18th International Workshop on Mobile Computing Systems and Applications, HotMobile 2017|
|Period||17/2/21 → 17/2/22|
Bibliographical noteFunding Information:
This work was supported by the DGIST RandD program of the Ministry of Science, ICT and Future Planning (MSIP), Korea (CPS Global Center), the ICT RandD program of MSIPIITP (14-824-09-013, Resilient CPS Research), Korea Health Technology RandD Project through the Ministry of Health and Welfare and KHIDI (#HI16C0982), and by the Ministry of Trade, Industry and Energy and KIAT through the International Cooperative RandD Program (#N0002099; Eurostars-2 Project SecureIoT).
© 2017 ACM.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Human-Computer Interaction
- Computer Science Applications