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

Jungmo Ahn,Hee Jung Yoon,Jeonggil Ko,Ho-Kyeong Ra,Sang Hyuk Son

doi:10.1109/access.2020.3025776

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.

Highlights

Smartwatches are a ubiquitously deployed mobile device and can be considered a representative form of wearable platforms
Results from our preliminary study suggest that when the user stays still the accuracy of the heart rate measurements are very high with an error of only 1.23 bpm, but as the user actively moves the smartwatch heart rate measurements start to deviate from the ground truth
In spite of the such promising potential of enabling important applications, we noticed that the performance of heart rate measurements on wearable platforms may not be reliable due to unavoidable motion artifacts

Summary

Introduction

Smartwatches are a ubiquitously deployed mobile device and can be considered a representative form of wearable platforms. Given that most smartwatches offer heart rate sensor readings they can be used to continuously monitor critical events that may occur to patients with various chronic cardiac disorders or even accurately track and quantify the activity levels of patients with maladies such as diabetes [2]. PPG sensors exploit LEDs that emits light to the skin and a photodiode that captures the reflected light from the skin. This process can be formulated by following equation [5], [6], the Beer-Lambart law which defines the attenuation of light to the properties of the penetrated material

Objectives

Results

Conclusion