LCAS Thermal Physiological Monitoring Dataset


L-CAS Thermal Physiological Monitoring Dataset

Collected by the Optris PI-450 thermal imager


This dataset is recorded for evaluating thermal-based physiological monitoring algorithms that can measure respiration and heart beat rate. The dataset contains thermal images of different human faces acquired in the Lincoln Centre for Autonomous Systems (L-CAS) at the University of Lincoln, UK. Data were recorded into different rosbag files, each corresponding to a person. The thermal camera recorded each person for two minutes with a frequency of 27Hz. People were asked to keep static in the first one minute, then move their head up and down, forward and back, turning right and left, each action was held for 10 seconds.


This dataset provides:

  1. Robot Operating System (ROS) rosbags. Each rosbag contains about 3,000 continuous thermal images.
  2. Ground truth for respiration and heart beat


If you are considering using this data, please reference the following:

S. Cosar, Z. Yan, F. Zhao, T. Lambrou, S. Yue, N. Bellotto, (2018) Thermal camera based physiological monitoring with an assistive robot. In: IEEE International Engineering in Medicine and Biology Conference, 17-21 July 2018, Honolulu, HI

Recording platform

    The Optris PI-450 thermal imager was mounted at 1.3m from the floor, on the top of a Kompaï robot. The distance between the robot and the face was about 1.3m. Thermal images were recorded using the Optris official driver. Temperature data is encoded as:
float t = (float)(data - 1000) / 10.f.
Optris PI-450 thermal imager parameters
Optical resolution: 382 x 288 pixels
Frame rate: 10 Hz
Measurement range: 20 – 100 °C
Optics field of view: 38° x 29° / f = 15 mm oder


ROSBAG files

Ground Truth files


The physiological monitoring software developed by LCAS can be found on GitHub as a ROS node implementation.


This work was funded in part by the EU Horizon 2020 project ENRICHME, H2020-ICT-2014-1, Grant agreement no.: 643691.


Please send an email to or for questions regarding the dataset. 


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Copyright (c) 2016 Zhi Yan, Serhan Cosar, and Nicola Bellotto.