kidkid168/bounded-kalman-filter-method-for-motion-robust-non-contact-heart-rate-estimation

Bounded Kalman filter method for motion-robust, non-contact heart rate estimation

Bounded Kalman filter method for motion-robust, non-contact heart rate estimation

Introduction

Rhythmic pulsating action of the heart causes blood volume changes all over the body. This pulsating action results in the generation of cardiac pulse, which can be tracked/observed in the skin, wrist, and fingertips. Photo- plethysmography (PPG) is an optic based plethysmography method, based on the principle that blood absorbs more light than surrounding tissue and hence, variations in blood volume affect transmission or reflectance correspondingly. Prior rPPG methods of pulse-rate measurement from face videos attain high accuracies under well controlled uniformly illuminated and motion-free situations, however, their performance degrades when illumination variations and subjects’ motions are involved.

Contribution

In this paper (A Bounded Kalman Filter Method for Motion-Robust, Non-Contact Heart Rate Estimation), a HR measurement method is presented that utilizes facial key-point data to overcome the challenges presented in real world settings as described earlier. In summary, our contributions are:

1. The ability to identify motion blur and to dynamically (algorithmically) denoise blurred frames to enable frame to frame face capture
2. The ability to enable motion estimation of feature points with higher accuracy in terms of range and speed
3. The ability to accurately capture heart rate at distances up to 4ft

Citation

If you use any of the resources provided on this page in any of your publications we ask you to cite the following work.

Prakash, S. K. A., & Tucker, C. S. (2018). Bounded Kalman filter method for motion-robust, non-contact heart rate estimation. Biomedical Optics Express, 9(2), 873-897. DOI: 10.1364/BOE.9.000873.