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 . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.