Key result
Compared to an adaptive filter, the proposed hangover-time filter for real-time remote heart rate estimation achieved a lower mean error of ~4.484 BPM and a higher CAND of 95.05%.
Why the study?
Heart rate estimation allows pre-emptive action in emergencies, leading to techniques addressing motion artifacts to increase detection robustness.
A novel hangover-time filter combined with FastICA improves the accuracy of real-time, contactless heart rate estimation using a standard laptop camera.
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Offers technical gains in contactless HR estimation; leaves open clinical validation before any adoption.
Heart rate is one of the physiological parameters which is measured for our health and well-being. By estimating it one can act pre-emptively in case of emergencies. In the past few years, various techniques were proposed, subjecting motion artifacts to increase the robustness of the system. This paper presents a real-time method of detecting the human heart rate, remotely, using a laptop camera. This paper also proposes on using a Hangover-Time filter for robust detection. Following the recent research [1], this project also utilizes OpenCV for face detection and isolating the forehead region, Fast Fourier Transform Analysis and Band-Pass Filter. Compared to the adaptive filter, the hangover-time filter has a difference in a mean error of ~0.26BPM and the corresponding Complement of the Absolute Normalized Difference (CAND) of 95.05% is achieved.
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Mansour Tahernezhadi (2019) studied Heart rate estimation. Fast Independent Component Analysis and Hangover-Time Filter vs. Adaptive filter was evaluated on Mean error in BPM. Compared to an adaptive filter, the proposed hangover-time filter for real-time remote heart rate estimation achieved a lower mean error of ~4.484 BPM and a higher CAND of 95.05%.
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