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Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review.

Martin Clinton Tosima ManullangYuan-Hsiang LinSheng-Jie LaiNai-Kuan Chou
Published in: Sensors (Basel, Switzerland) (2021)
Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.
Keyphrases
  • machine learning
  • heart rate
  • healthcare
  • high speed
  • deep learning
  • convolutional neural network
  • heart rate variability
  • primary care
  • current status
  • quality improvement
  • case control