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Video-based HR measurement using adaptive facial regions with multiple color spaces

Panigrahi Arpita, Sharma Hemant, Mukherjee Atin

Biocybernetics and Biomedical Engineering

2024

Vol. 44, no. 1

68--82

Driven by the desire for feasible and convenient healthcare, non-contact heart rate (HR) monitoring based on consumer-grade cameras has gained significant recognition among researchers. However, this technology suffers from performance reliability and consistency in realistic situations of motion artifacts, illumination variations, and skin tones, limiting it to emerge as an alternative to conventional methods. Considering these challenges, this paper suggests an effective technique for HR measurement from facial RGB videos. The face being the region of interest (ROI) is divided into several small sub-ROIs of even size. A group of quality sub-ROIs is formed and weighted based on the fundamental periodicity coefficient to handle spatial non-uniform illumination and facial motions. Five different color spaces are considered, and the most suitable color component from each space is chosen to alleviate the influence of temporal illumination variation and other factors. The resultant color signals are denoised using the ensemble empirical mode decomposition and integrated using the principal component analysis to derive a pulsating component representing the blood volumetric changes for HR computation. Experiments are conducted over three standard datasets, namely PURE, UBFC, and COHFACE. The obtained mean absolute error values are 1.16 beats per minute (bpm), 1.56 bpm, and 2.10 bpm for PURE, UBFC, and COHFACE datasets, respectively, indicating the performance of the technique well above the clinically acceptable threshold. In comparison, the technique showed performance superiority over the state-of-art methods. These outcomes substantiate the potential of alternative color spaces for accurate and reliable HR monitoring from facial videos in challenging scenarios.

Heart rate extraction from PPG signals using variational mode decomposition

Sharma Hemant

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 1

75--86

Monitoring of vital signs using the photoplethysmography (PPG) signal is desirable for the development of home-based healthcare systems in the aspect of feasibility, mobility, comfort, and cost-effectiveness of the PPG device. In this paper, a new technique based on the variational mode decomposition (VMD) for estimating heart rate (HR) from the PPG signal is proposed. The VMD decomposes an input PPG signal into a number of modes or sub-signals. Afterward, the modes which are dominantly influenced by the HR information are selected and further processed for extracting HR of the patient. The proposed scheme is validated over a large number of recordings acquired from three independent databases, namely the Capnobase, MIMIC, and University of Queens Vital Sign (UQVS). Experiments are performed over different data length segments of the PPG recordings. Using the data length of 30 s, the proposed technique outperformed the existing techniques by achieving the lower median (1st quartile, 3rd quartile) values of root mean square error (RMSE) as 0.23 (0.19, 0.31) beats per minute (bpm), 0.41 (0.31, 0.56) bpm and 1.1 (0.9, 1.22) bpm for the Capnobase, MIMIC, and UQVS datasets, respectively. Since the shorter data length is more suitable for the clinical applications, the proposed technique also provided satisfactory agreement between the derived and reference HR values for the shorter data length segments. Perfor-mance results over three independent datasets suggest that the proposed technique can provide accurate and reliable HR information using the PPG signal recorded from the patients suffering from dissimilar problems.