Heart Rate Estimation Algorithm Research Articles

Accurate Heart Rate Monitoring During Physical Exercises Using PPG

The challenging task of heart rate (HR) estimation from the photoplethysmographic (PPG) signal, during intensive physical exercises, is tackled in this paper. The study presents a detailed analysis of a novel algorithm (WFPV) that exploits a Wiener filter to attenuate the motion artifacts, a phase vocoder to refine the HR estimate and user-adaptive post-processing to track the subject physiology. Additionally, an offline version of the HR estimation algorithm that uses Viterbi decoding is designed for scenarios that do not require online HR monitoring (WFPV+VD). The performance of the HR estimation systems is rigorously compared with existing algorithms on the publically available database of 23 PPG recordings. On the whole dataset of 23 PPG recordings, the algorithms result in average absolute errors of 1.97 and 1.37 BPM in the online and offline modes, respectively. On the test dataset of 10 PPG recordings which were most corrupted with motion artifacts, WFPV has an error of 2.95 BPM on its own and 2.32 BPM in an ensemble with two existing algorithms. The error rate is significantly reduced when compared with the state-of-the art PPG-based HR estimation methods. The proposed system is shown to be accurate in the presence of strong motion artifacts and in contrast to existing alternatives has very few free parameters to tune. The algorithm has a low computational cost and can be used for fitness tracking and health monitoring in wearable devices. The MATLAB implementation of the algorithm is provided online.

A Novel Non-contact Heart Rate Estimation Algorithm and System with User Identification

In these days, the wearable devices have been developed for measuring biological data effectively. However, wearable devices have tissue allege and noise problem. Also, it is impossible for a remote center to identify the person whose data are measured by wearable devices, which could trigger a communication problem over treatment. To solve these problems, biometric measurement based on a non-contact method, such as face image sequencing is necessary. This makes it possible to measure biometric data without any operation and side effects. This system can monitor the biological signals of people in real time without allege and noise and simultaneously identify them. In this paper, we propose an authentication process while measuring biometric data, through a non-contact method.

Real-Time Robust Heart Rate Estimation From Wrist-Type PPG Signals Using Multiple Reference Adaptive Noise Cancellation

Heart rate (HR) monitoring using photoplethysmographic (PPG) signals recorded from wearers' wrist greatly facilitates design of wearable devices and maximizes user experience. However, placing PPG sensors in wrist causes much stronger and complicated motion artifacts (MA) due to loose interface between sensors and skin. Therefore, developing robust HR estimation algorithms for wrist-type PPG signals has significant commercial values. In this paper, we propose a robust HR estimation algorithm for wrist-type PPG signals using multiple reference adaptive noise cancellation (ANC) technique-termed here as "MURAD." The main challenge of using ANC for MA reduction is to devise a qualified reference noise signal (RNS) to the adaptive filter. We propose a novel solution by using four RNSs, namely, the three-axis accelerometer data and the difference signal between the two PPG signals. For each RNS, we get a different version of the cleaned PPG signal. Then, a set of probable HR values is estimated using all of the cleaned PPG signals, and then, the value that is closest to the estimated HR of the previous time window is chosen to be the HR estimate of the current window. Then, some peak verification techniques are employed to ensure accurate HR estimations. The proposed technique gives lower average absolute error compared to state-of-the art methods. So, MURAD method provides a promising solution to the challenge of HR monitoring using PPG in wearable devices during severe MA conditions.

Poster #215 COMPARISON OF OUTCOMES IN PATIENTS WITH EARLY PHASE VERSUS LATER PHASE SCHIZOPHRENIA

The heart rate of subjects is extracted from video data during exercises and is treated as a negative feedback parameter for the control of fitness devices. The probability distribution in RGB space of the parameters of the skin reflection model is analyzed based on the experimental data taken under a wide range of environmental settings. A novel pulse extraction algorithm is proposed based on this distribution, which sets aside distribution centers as the model parameters for preliminary pulse separation and adopts cascade least mean square adaptive filters to compensate for modeling errors. A Gaussian distribution-based heart rate estimation algorithm is employed for heart rate stabilization. In addition, the concept of heart rate negative feedback control is introduced, e.g., the treadmill speed and incline can be adjusted interactively based on the subject's heart rate. The heart rate measurement accuracy and the control scheme have been evaluated by a self-collected benchmark dataset and a prototype system. The heart rate measurement accuracy is increased over the state-of-the-art algorithms by more than 10% on average. The heart rate negative feedback control improves human–machine interaction performance compared with a traditional control scheme that uses the predefined program regardless of the subjects’ physical state. The proposed technique pioneers new possibilities for the usage of fitness devices.