Improved remote photoplethysmography using machine learning-based filter bank

HIGHLIGHTS

• What: The authors propose a machine learning-based filter bank (MLFB) noise reduction algorithm to improve the quality of rPPG signals. This approach has been used to monitor heart rate during sleeping, driving, and exercising, where a physical contact sensor is not appropriate. In this study, a machine_learning approach was developed to refine the rPPG signal by effectively isolating signal components with prominent cardiac activity.
• Who: Jukyung Lee and colleagues from the Department of Biomedical Engineering, University of Ulsan, Ulsan, Republic of Korea have published the article: Improved Remote Photoplethysmography Using Machine Learning-Based Filter . . .
  

   

  

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