Detecting breathing rates and depth of breath using LPCs and Restricted Boltzmann Machines
| Autor: | Ramiro Jordan, Amir Raeisi Nafchi, Eric Hamke, Manel Martínez-Ramón |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Restricted Boltzmann machine
business.product_category Computer science business.industry 0206 medical engineering Boltzmann machine Firefighting Health Informatics Breathing sounds 02 engineering and technology Machine learning computer.software_genre 020601 biomedical engineering GeneralLiterature_MISCELLANEOUS 03 medical and health sciences 0302 clinical medicine Signal Processing Breathing Physical exhaustion Unsupervised learning Artificial intelligence Respirator business computer 030217 neurology & neurosurgery |
| Zdroj: | Biomedical Signal Processing and Control. 48:1-11 |
| ISSN: | 1746-8094 |
| DOI: | 10.1016/j.bspc.2018.09.009 |
| Popis: | This paper presents the use of a Restricted Boltzmann Machine to develop an unsupervised machine learning approach to process breathing sounds to predict breathing rates and depth or length of breaths. Breath detection and monitoring has been the subject of several studies involving the health monitoring of patients on respirators. We are proposing to extend the use of non-invasive techniques to provide measures of physical exhaustion or activity. The level of activity or exhaustion could be used to prevent accidents or manage exposure to physically demanding environments such as firefighting or working underwater. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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