TR2020-161

Near-Infrared Imaging Photoplethysmography During Driving


    •  Nowara, E., Marks, T., Mansour, H., Veeraraghavan, A., "Near-Infrared Imaging Photoplethysmography During Driving", IEEE Transactions on Intelligent Transportation Systems, December 2020.
      BibTeX TR2020-161 PDF
      • @article{Nowara2020dec,
      • author = {Nowara, Ewa and Marks, Tim and Mansour, Hassan and Veeraraghavan, Ashok},
      • title = {Near-Infrared Imaging Photoplethysmography During Driving},
      • journal = {IEEE Transactions on Intelligent Transportation Systems},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-161}
      • }
  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Computer Vision, Machine Learning

Imaging photoplethysmography (iPPG) could greatly improve driver safety systems by enabling capabilities ranging from identifying driver fatigue to unobtrusive early heart failure detection. Unfortunately, the driving context poses unique challenges to iPPG, including illumination and motion. First, drastic illumination variations present during driving can overwhelm the small intensity-based iPPG signals. Second, significant driver head motion during driving, as well as camera motion (e.g., vibration) make it challenging to recover iPPG signals. To address these two challenges, we present two innovations. First, we demonstrate that we can reduce most outside light variations using narrow-band near-infrared (NIR) video recordings and obtain reliable heart rate estimates. Second, we present a novel optimization algorithm, which we call AutoSparsePPG, that leverages the quasi-periodicity of iPPG signals and achieves better performance than the stateof-the art methods. In addition, we release the first publicly available driving dataset that contains both NIR and RGB video recordings of a passenger’s face with simultaneous ground truth pulse oximeter recordings.