Harmonic sum-based method for heart rate estimation using PPG signals affected with motion artifacts

Abstract

Wearable photoplethysmography (WPPG) has recently become a common technology in heart rate (HR) monitoring. General observation is that the motion artifacts change the statistics of the acquired PPG signal. Consequently, estimation of HR from such a corrupted PPG signal is challenging. However, if an accelerometer is also used to acquire the acceleration signal simultaneously, it can provide helpful information that can be used to reduce the motion artifacts in the PPG signal. By dint of repetitive movements of the subjects hands while running, the accelerometer signal is found to be quasi-periodic. Over short-time intervals, it can be modeled by a finite harmonic sum (HSUM). Using the harmonic sum (HSUM) model, we obtain an estimate of the instantaneous fundamental frequency of the accelerometer signal. Since the PPG signal is a composite of the heart rate information (that is also quasi-periodic) and the motion artifact, we fit a joint harmonic sum (HSUM) model to the PPG signal. One of the harmonic sums corresponds to the heart-beat component in PPG and the other models the motion artifact. However, the fundamental frequency of the motion artifact has already been determined from the accelerometer signal. Subsequently, the HR is estimated from the joint HSUM model. The mean absolute error in HR estimates was 0.7359 beats per minute (BPM) with a standard deviation of This material is presented to ensure timely dissemination of scholarly and technical work. 0.8328 BPM for 2015 IEEE Signal Processing (SP) cup data. The ground-truth HR was obtained from the simultaneously acquired ECG for validating the accuracy of the proposed method. The proposed method is compared with four methods that were recently developed and evaluated on the same dataset.

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

“…We note that most of the previous works refer to the accelerometer signal for handling motion artifacts or integrate multiple PPG sensor signals [19], [27]- [32]. However, in this paper, we discuss why the accelerometer-based approach might not be sufficient to measure the reliability of heart rate measurement directly on a smartwatch.…”

Wearable Hardware Design for the Internet of Medical Things (IoMT)

“…Transmittance detects the light transmitted through the tissues by a photodiode that is kept opposite of the light source, and is commonly used in fingertip-type sensors. Reflectance detects the intensity of reflected light using a photodiode that is kept on the same side as the light source, and is commonly used in wrist band applications [ 17 ]. Some common examples of this are the iHeart device that clamps on to your finger and the Fitbit device that detects the signal from the wrist shown in Figure 2 below.…”

“…These motion artifacts can be caused by respiration, blood pressure oscillation, circadian biorhythm, and thermoregulation. Some popular techniques used to measure HRV are: performing a power spectral analysis of the peak-to-peak interval and the finite harmonic sum model using an accelerometer to capture motion artifacts [ 15 , 17 ].…”

Smart fog: Fog computing framework for unsupervised clustering analytics in wearable Internet of Things

“…Both systems were tested on real world pathological speech data from telemonitoring of patients with Parkinson's disease. The increasing use of wearables in smart telehealth system led to the generation of huge medical big data [7,8,9,10]. The telehealth services leverage these data for assisting clinical procedures.…”