Browsing by Author "Ramzan, N"

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    PublicationOpen Access
    A comparative study of common steps in video-based remote heart rate detection methods
    (Elsevier Ltd, 2022-06-22) Malasinghe, L; Katsigiannis, S; Dahal, K; Ramzan, N
    Video-based remote heart rate detection is a promising technology that can offer convenient and low-cost heart rate monitoring within, but not limited to, the clinical environment, especially when attaching electrodes or pulse oximeters on a person is not possible or convenient. In this work, we examined common steps used in video-based remote heart rate detection algorithms, in order to evaluate their effect on the overall performance of the remote heart rate detection pipeline. Various parameters of the examined methods were evaluated on three public and one proprietary dataset in order to establish a video-based remote heart rate detection pipeline that provides the most balanced performance across various diverse datasets. The experimental evaluation demonstrated the effect and contribution of each step and parameter set on the estimation of the heart rate, resulting in an optimal configuration that achieved a best RMSE value of 9.51.
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    PublicationOpen Access
    Remote heart rate extraction using microsoft kinecttm v2. 0
    (acm.org, 2018-05-16) Malasinghe, L. P; Katsigiannis, S; Ramzan, N
    Remote and contactless heart rate detection is still an open research issue of great clinical importance. Available approaches lack the necessary accuracy and reliability for acceptance by medical experts. In this study, we propose a new method for remote heart rate extraction using the Microsoft KinectTM v2.0 image sensor. The proposed approach relies on signal processing and machine learning methods in order to create a model for accurate estimation of the heart rate via RGB and infrared face videos. Electrocardiography (ECG) recordings and RGB and infrared face videos, captured using the KinectTM v2.0 image sensor, were acquired from 17 subjects and used to create a machine learning model for remote heart rate detection. Experimental evaluation through supervised regression experiments showed that the proposed approach achieved a mean absolute error of 6.972 bpm, demonstrating the capabilities of the underlying technology.