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Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture

Author

Listed:
  • Jorge Brieva

    (Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, Mexico
    These authors contributed equally to this work.)

  • Hiram Ponce

    (Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, Mexico
    These authors contributed equally to this work.)

  • Ernesto Moya-Albor

    (Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Ciudad de México 03920, Mexico
    These authors contributed equally to this work.)

Abstract

The breathing rate monitoring is an important measure in medical applications and daily physical activities. The contact sensors have shown their effectiveness for breathing monitoring and have been mostly used as a standard reference, but with some disadvantages for example in burns patients with vulnerable skins. Contactless monitoring systems are then gaining attention for respiratory frequency detection. We propose a new non-contact technique to estimate the breathing rate based on the motion video magnification method by means of the Hermite transform and an Artificial Hydrocarbon Network (AHN). The chest movements are tracked by the system without the use of an ROI in the image video. The machine learning system classifies the frames as inhalation or exhalation using a Bayesian-optimized AHN. The method was compared using an optimized Convolutional Neural Network (CNN). This proposal has been tested on a Data-Set containing ten healthy subjects in four positions. The percentage error and the Bland–Altman analysis is used to compare the performance of the strategies estimating the breathing rate. Besides, the Bland–Altman analysis is used to search for the agreement of the estimation to the reference.The percentage error for the AHN method is 2.19 ± 2.1 with and agreement with respect of the reference of ≈99%.

Suggested Citation

  • Jorge Brieva & Hiram Ponce & Ernesto Moya-Albor, 2023. "Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture," Mathematics, MDPI, vol. 11(3), pages 1-23, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:645-:d:1048427
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    References listed on IDEAS

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    1. Yawu Zhao & Yihui Liu, 2021. "OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-14, February.
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