Unobtrusive heart rate estimation during physical exercise using photoplethysmographic and acceleration data

Abstract

Photoplethysmography (PPG) is a non-invasive, inexpensive and unobtrusive method to achieve heart rate monitoring during physical exercises. Motion artifacts during exercise challenge the heart rate estimation from wrist-type PPG signals. This paper presents a methodology to overcome these limitation by incorporating acceleration information. The proposed algorithm consisted of four stages: (1) A wavelet based denoising, (2) an acceleration based denoising, (3) a frequency based approach to estimate the heart rate followed by (4) a postprocessing step. Experiments with different movement types such as running and rehabilitation exercises were used for algorithm design and development. Evaluation of our heart rate estimation showed that a mean absolute error 1.96 bpm (beats per minute) with standard deviation of 2.86 bpm and a correlation of 0.98 was achieved with our method. These findings suggest that the proposed methodology is robust to motion artifacts and is therefore applicable for heart rate monitoring during sports and rehabilitation.

Description of a Database Containing Wrist PPG Signals Recorded during Physical Exercise with Both Accelerometer and Gyroscope Measures of Motion

“…Very recently, a number of signal processing techniques have been proposed for separating true PPG components from motion artifact components in order to allow PPG-based heart monitoring during physical exercise for the first time. See [3,[5][6][7][8][9][10][11][12] as a small number of example algorithms. with no motion present shows clear peaks for each heart beat, here for a participant with a low resting heart rate of 42 beats per minute.…”

RealTime Heart Rate Monitoring Using Photoplethysmographic (PPG) Signals During Intensive Physical Exercises

“…This was observed to facilitate convergence of the adaptive filters and improved the estimation accuracy, by achieving an AAE of 1.25 BPM. Investigating the same problem [19]- [21] achieved AAE of 1.8, 1.77, and 1.96 BPM respectively.…”

How Nonlinear-Type Time-Frequency Analysis Can Help in Sensing Instantaneous Heart Rate and Instantaneous Respiratory Rate from Photoplethysmography in a Reliable Way

“…For the IHR, methods include time-frequency (TF) analyses (Gil et al, 2010; Mullan et al, 2015; Wu et al, 2016), adaptive filtering (Yousefi et al, 2014; Khan et al, 2015; Murthy et al, 2015; Schack et al, 2015; Mashhadi et al, 2016), Kalman filter (Frigo et al, 2015), sparse spectrum reconstruction (Zhang, 2015), blind source separation (Wedekind et al, 2015), a Bayesian approach (D'souza et al, 2015; Sun and Zhang, 2015), correntropy spectral density (CSD) (Garde et al, 2014), empirical mode decomposition (EMD) (Zhang et al, 2015), model fitting (Wadehn et al, 2015), deep learning (Jindal, 2016), fusion approaches (Temko, 2015; Zhu S. et al, 2015), etc. For the IRR, efforts include TF analysis (Chon et al, 2009; Orini et al, 2011; Dehkordi et al, 2015), sparse signal reconstruction (Zong and Jafari, 2015; Zhang and Ding, 2016), neural network (Johansson, 2003), modified multi-scale principal component analysis (Madhav et al, 2013), independent component analysis (Zhou et al, 2006), time-varying autoregressive regression (Lee and Chon, 2010b,a), fusion approaches (Karlen et al, 2013; Cernat et al, 2015), pulse-width variability (Lazaro et al, 2013; Cernat et al, 2014), CSD (Pelaez-Coca et al, 2013; Garde et al, 2014), EMD (Garde et al, 2013), a Bayesian approach (Pimentel et al, 2015; Zhu T. et al, 2015), etc.…”