Abstract
Estimating Heart Rate (HR) value is one of the great important things to determine a person’s physiological data and monitor the physiological signs. Nowadays, many kinds of research have demonstrated that the most physiological data “e.g., heart rate and respiration rate” can be accurately measured remotely and collected from photoplethysmographic (PPG) signals via digital cameras or webcams. The PPG signal will be subjected to several signals processing algorithms such as EMD, PCA/ICA, FFT, and others, in order to evaluate and extract the appropriate signs that help to extract the vital parameters. Furthermore, many related works have been made to improve the detection of this non-contact technique with the same result measured using contact sensors. In this paper, we present a general approach with familiar processing signal algorithms and their uses to extract heart rate values. Indeed, the current paper is mainly based on three commons signal processing algorithms widely used in the related field, which are the Empirical Mode Decomposition for a signal decomposition, Principal Component Analysis for the signal’s dimensionality reduction, moving average algorithm for signal denoising, and Fourier Transform for converting the PPG signal from the time domain into to frequency domain. Our proposed heart rate estimation systems show a good correlation relationship and closeness with a mean of error around of 1.59.
Supported by organization Laboratory of Systems Engineering and Information Technology LiSTi.
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El khadiri, Z., Latif, R., Saddik, A. (2023). Remote Heart Rate Measurement Using Plethysmographic Wave Analysis. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_23
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DOI: https://doi.org/10.1007/978-3-031-29313-9_23
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