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Vol. 252, Issue 5, October 2021, pp. 42-49

 

Bullet

 

Deep Learning-Based Detection of Abnormal Respiration Based on Abdominal 3D Shape
Dynamically Measured by Moiré Topography
 

1 Yuki MOCHIZUKI and 1, *Norio TAGAWA

1 Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo, 191-0065, Japan
1 Tel.: +81-42-585-8416, fax: +81-42-583-5119

* E-mail: tagawa@tmu.ac.jp

 

Received: 2021 /Accepted: 2021 /Published: 31 October 2021

 

 

Abstract: Currently, paramedics cannot make triage judgments in ambulances during emergency transport of infants. If this can be done, it will be possible to select an appropriate destination hospital to provide the most suitable medical care. In this study, we developed a system to detect abnormal respiration by measuring and analyzing three-dimensional abdominal movement using moiré topography and a deep learning model. The performance of the model used in this system was evaluated, and the model was found to be more robust than conventional machine learning models with respect to changes in camera viewpoint.

 

Keywords: Moiré topography, Active stereo, Deep learning, LSTM, Abdomen shape, Triage, Abnormal respiration.

 

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This work is licensed under a Creative Commons 4.0 International License

 

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