Abstract
Due to the current population aging around the world, it is a fact that a good amount of the technology should be focused on the care of these people, improving their living conditions. In this work, we propose a methodology to classify in-bed human posture using pressure mat sensors for the prevention of pressure ulcers. First, we provide a visual representation using fuzzy processing from raw pressure data to grayscale. Second, we enable the generation of a large dataset from a limited dataset using ad hoc data augmentation, generating new synthetic sleeping positions. Third, we define 2 CNN models to evaluate the impact of layers on the performance of in-bed posture classification. The results show an encouraging performance in a small dataset using a leave-one-participant-out cross-validation.
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Acknowledgments
Funding for this research is provided by EU Horizon 2020 Pharaon Project ‘Pilots for Healthy and Active Ageing’, Grant agreement no. 857188. Moreover, this research has received funding under the REMIND project Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020, under grant agreement no. 734355. Furthermore, this contribution has been supported by the Andalusian Health Service by means of the project PI-0387-2018.
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Rodríguez, A.P., Gil, D., Nugent, C., Quero, J.M. (2020). In-Bed Posture Classification from Pressure Mat Sensors for the Prevention of Pressure Ulcers Using Convolutional Neural Networks. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_30
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DOI: https://doi.org/10.1007/978-3-030-45385-5_30
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