Breath rate monitoring during sleep using near-ir imagery and PCA

We present a vision based method to estimate the respiration rate of subjects from their chest movements. In contrast to alternative approaches, our method is fully automated, non-invasive, robust to occlusions, and only depends on off-the-shelf hardware. We project a fixed infrared (IR) dot pattern. The dots are detected using a camera with a matching IR filter. We estimate the dots´ barycenters with sub-pixel precision and we track them over a 30 seconds sliding window. We merge all trajectories using Principal Component Analysis(PCA) and use Autoregressive (AR) Spectral Analysis to estimate the respiratory rate. The system was evaluated on 9 subjects and on a range of simulated scenarios using an artificial chest.