Data-Driven Contactless Health Monitoring System

Our focus on contactless health monitoring underscores its significant benefits, offering a safer and more accessible approach to healthcare. In our research, we're engaging in two distinct projects aimed at harnessing the potential of off-the-shelf cameras for regular health monitoring purposes. Firstly, our efforts revolve around remote photoplethysmography (rPPG), a technique through which we endeavor to extract heart rate data from skin videos captured by these cameras. By leveraging advanced algorithms, our goal is to accurately discern heart rate information, enabling non-invasive monitoring that can be seamlessly integrated into everyday life. Secondly, our research extends to breathing rate (BR) estimation, where we aim to analyze video data to infer breathing patterns. This entails detecting subtle movements indicative of breathing activity, thereby enabling the estimation of breathing rates without the need for physical sensors. Through this approach, we aim to develop a comprehensive monitoring system that provides valuable insights into two of the human vitals (heart and breathing rates), using readily available camera technology.

Figure (a) Developed RhythmEdge solution to estimate HR from video data using resource constraints devices. 

Figure (b) Self-supervised approaches to learn robust rPPG function from noisy rPPG-data labels. 

Figure (c): Breathing rate from video data. 


Principle Investigator

Research Assistant Professor

Ph.D Students



This material is based upon work supported by the National Science Foundation under Grant Numbers:  1750936 (University of Maryland Baltimore County)

Point of Contact

Nirmalya Roy, University of Maryland, Baltimore County