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On-Device Filter Design for Self-Identifying Inaccurate Heart Rate Readings on Wrist-Worn PPG Sensors

Title
On-Device Filter Design for Self-Identifying Inaccurate Heart Rate Readings on Wrist-Worn PPG Sensors
Authors
Ahn, JungmoRa, Ho-KyeongYoon, Hee JungSon, Sang HyukKo, Jeonggil
DGIST Authors
Ahn, Jungmo; Ra, Ho-Kyeong; Yoon, Hee Jung; Son, Sang Hyuk; Ko, Jeonggil
Issue Date
2020-09
Citation
IEEE Access, 8, 184774-184784
Type
Article
Article Type
Article
Author Keywords
Heart rate monitoringPPG sensorreliable healthcare sensingsmartwatchwearable devices
Keywords
PHOTOPLETHYSMOGRAPHIC SIGNALSMOTION ARTIFACTS
ISSN
2169-3536
Abstract
The ubiquitous deployment of smart wearable devices brings promises for an effective implementation of various healthcare applications in our everyday living environments. However, given that these applications ask for accurate and reliable sensing results of vital signs, there is a need to understand the accuracy of commercial-off-the-shelf wearable devices’ healthcare sensing components (e.g., heart rate sensors). This work presents a thorough investigation on the accuracy of heart rate sensors equipped on three different widely used smartwatch platforms. We show that heart rate readings can easily diverge from the ground truth when users are actively moving. Moreover, we show that the accelerometer is not an effective secondary sensing modality of predicting the accuracy of such smartwatch-embedded sensors. Instead, we show that the photoplethysmography (PPG) sensor’s light intensity readings are an plausible indicator for determining the accuracy of optical sensor-based heart rate readings. Based on such observations, this work presents a light-weight Viterbi-algorithm-based Hidden Markov Model to design a filter that identifies reliable heart rate measurements using only the limited computational resources available on smartwatches. Our evaluations with data collected from four participants show that the accuracy of our proposed scheme can be as high as 98%. By enabling the smartwatch to self-filter misleading measurements from being healthcare application inputs, we see this work as an essential module for catalyzing novel ubiquitous healthcare applications. © 1991 BMJ Publishing Group. All rights reserved.
URI
http://hdl.handle.net/20.500.11750/12820
DOI
10.1109/ACCESS.2020.3025776
Publisher
Institute of Electrical and Electronics Engineers Inc.
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