Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/30100
Autoria: | Ribeiro, G. Postolache, O. |
Editor: | Nguyen, N. T., Botzheim, J., Gulyás, L., Núñez, M., Treur, J., Vossen, G., Kozierkiewicz, A. |
Data: | 2023 |
Título próprio: | New approaches to monitoring respiratory activity as part of an intelligent model for stress assessment |
Volume: | 14162 |
Título e volume do livro: | Computational collective intelligence. Lecture Notes in Computer Science |
Paginação: | 726 - 740 |
Título do evento: | 15th International Conference on Computational Collective Intelligence, ICCCI 2023 |
Referência bibliográfica: | Ribeiro, G., & Postolache, O. (2023). New approaches to monitoring respiratory activity as part of an intelligent model for stress assessment. In N. T. Nguyen, J. Botzheim, L. Gulyás, M. Núñez, J. Treur, G. Vossen, & A. Kozierkiewicz (Eds.), Computational collective intelligence. Lecture Notes in Computer Science (vol. 14162, pp. 726-740. Springer. https://doi.org/10.1007/978-3-031-41456-5_55 |
ISSN: | 0302-9743 |
ISBN: | 978-3-031-41456-5 |
DOI (Digital Object Identifier): | 10.1007/978-3-031-41456-5_55 |
Palavras-chave: | Contactless health status monitoring Wearable sensors Infrared temperature Thermography Digital signal processing Fuzzy logic Photoplethysmography Respiratory rate Stress classification |
Resumo: | Abnormal breathing patterns have been linked to many diseases and stress-related effect. Visually counting breaths per minute is the gold standard for measuring respiratory rate. In hospital research, most nurses recognize the physiological importance of respiratory rate however its measurement it is not considered mandatory. Current research studies offer viable options for continuous monitoring of respiratory activity, although with degraded performance due to artefact. This paper proposes five new respiratory rate estimation methods considering their strengths and drawbacks to determine the most suitable one for various activities. Photoplethysmography, accelerometry, infrared temperature and pressure sensors are therefore used to monitor respiratory activity. In addition, we present a method for estimating respiratory rate via thermographic video image processing. In terms of novelty and innovation, we highlight the intelligent algorithms developed for real-time respiratory rate extraction from Photoplethysmography signals, the mechanical sensor prototype based on pressure sensors, and the facial recognition, focus zone identification, and image pixel analysis algorithms for thermographic image processing. In addition, a multichannel sensing system characterized by distributed platform computation is utilized to extract physiological parameters forming the basis for the proposed Fuzzy Logic-based model to detect and classify stress levels. To validate the suggested approaches, an experimental protocol was established to monitor the volunteers’ respiratory activity in a controlled setting, as well as health monitoring throughout the induction of thermal stress and its classification, yielding excellent indications of efficiency and accuracy. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | IT-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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conferenceobject_98355.pdf | 725,97 kB | Adobe PDF | Ver/Abrir |
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