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

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.