Human pulse rate (PR) can be estimated in several ways, including measurement instruments that directly count the PR through contact- and noncontact-based approaches. Over the last decade, computer-vision-assisted noncontact-based PR estimation has evolved significantly. Such techniques can be adopted for clinical purposes to mitigate some of the limitations of contact-based techniques. However, existing vision-guided noncontact-based techniques have not been benchmarked with respect to a challenging dataset. In view of this, we present a systematic review of such techniques implemented over a uniform computing platform. We have simultaneously recorded the PR and video of 14 volunteers. Five sets of data have been recorded for every volunteer using five different experimental conditions by varying the distance from the camera and illumination condition. Pros and cons of the existing noncontact image- and video-based PR techniques have been discussed with respect to our dataset. Experimental evaluation suggests that image- or video-based PR estimation can be highly effective for nonclinical purposes, and some of these approaches are very promising toward developing clinical applications. The present review is the first in this field of contactless vision-guided PR estimation research.

In this paper, we propose a method for detecting variations in the Pulse Rate (PR) of infants undergoing the Hammersmith Infant Neurological Examinations (HINE) using video data. As in every other medical examination the measurement of the PR is critical to underpin the physiological state of living beings. During HINE, measuring the infant's PR is important as its variations against physical conditions, age and other factors must be studied and correlated against developmental scores. However, this becomes highly complicated with active infants where their movements often lead to inconsistent PR estimation. We propose the use of a non-linear dimensionality reduction technique, called Laplacian Eigenmap (LE), to uncover the pulse information encapsulated within the high dimensional visual manifold characterized by normalized RGB feature vectors. Furthermore, low-level image filtering is applied to accurately detect PR within a chosen region-of-interest (ROI) from different parts of the infant's body. For validation and analysis, a set of 14 video sequences of infants undergoing five important tests of HINE have been chosen. Experimental results suggest that a bi-parametrized combination of color features from the RG and GB channels provide more valuable information in comparison to the RB and RGB channels. Results have demonstrated that this contactless method of PR detection has promising prospects for its future use in other clinical examinations of infants.

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