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

Abstract

Heart Rate (HR) is a fundamental vital sign, monitoring which provides essential information for automated healthcare systems. The emerging technology of Photoplethysmograph (PPG) is shown as a feasible candidate for such applications; however, Motion Artifacts (MA) hinder efficient HR estimation using PPG, especially in situations involving physical activities. It is previously shown that even in the presence of sever MA, HR is still traceable with the help of simultaneous acceleration data although at high computational expenses. In this paper, we propose a novel framework, that not only improves the accuracy in HR estimation, but also achieves realtime performance by significantly reducing the complexity of system; mainly due to alleviation of the need for computationally demanding MA cancellation methods. Utilizing an spectrum estimation model (autoregressive) that suits well to the inherent PPG generation process, and benefiting from further intrinsic properties of the environment (e.g., the venous pulsation phenomenon); our framework achieves realtime and delayed (post-processed) Average Absolute Errors (AAE) of 1.19 and 0.99 Beats Per Minute (BPM) respectively, on the 12 benchmark recordings in which subjects run at speeds of up to 15 km/h maximum. Moreover, the system makes standalone implementation feasible by processing input frames (2 channel PPG and 3D ACC) in < 0.004 times of the frame duration, operating on a 3.2 GHz processor. This study provides wearable healthcare technologies with a robust framework for accurate HR monitoring; at considerably low computational costs.

Heart rate variability and heart rate monitoring of nurses using PPG and ECG signals during working condition: A pilot study

“…Both devices used power spectral density analysis to identify and remove the motion artifacts from the raw data to calculate the HRV parameters. 9,27 The stress values are calculated based on HRV param- day and night rhythm, and acute and chronic diseases) 28 and mechanical factors such as motion artifacts 17 and sampling devices. 18,22 Overall, in conjunction with existing self-evaluation mechanisms, PPG wearable devices that are capable of collecting HRV parameters in real working conditions will contribute to the stress management of nurses to reliably assess their conditions, and to evaluate the effectiveness of interventions.…”

“…Both devices used power spectral density analysis to identify and remove the motion artifacts from the raw data to calculate the HRV parameters. 9 , 27 The stress values are calculated based on HRV parameters from both devices. The LF/HF ratio and percent LF were used to assess the accuracy and reliability of the two devices.…”

Review on Heart-Rate Estimation from Photoplethysmography and Accelerometer Signals During Physical Exercise

“…Cumulative spectrum (CUMSPEC) technique sparsifies the signal using iterative method adaptive thresholding, followed by median filtering of HR estimates. Genetic algorithm was used for HR tracking [39]. These algorithm were compared with TROIKA.…”