Abstract
In recent years, machine learning techniques have been the main techniques used for the early detection of various vital signals. With the integration of machine learning and body sensors, and with the widespread use of smartwatches and cellphones, it has been possible to keep track of a variety of physical parameters along with the possibility to give easy visualization of the obtained data. In this paper, a multiagent medical assistance system is proposed for the detection of cardio-respiratory abnormalities in older adults. In the data acquisition stage, heart rate and blood oxygen saturation parameters are acquired with a pulse oximeter. Once the information is obtained, it is stored, filtered, and processed on the edge with an embedded computer. For the classification stage, a random forest algorithm is used, using a public database for the training. The body signals and the classification results are displayed on a GUI.
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Hernandez-Leal, F., Alanis, A., Patiño, E. (2020). Multiagent Monitoring System for Oxygen Saturation and Heart Rate. In: Jezic, G., Chen-Burger, J., Kusek, M., Sperka, R., Howlett, R., Jain, L. (eds) Agents and Multi-Agent Systems: Technologies and Applications 2020. Smart Innovation, Systems and Technologies, vol 186. Springer, Singapore. https://doi.org/10.1007/978-981-15-5764-4_23
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DOI: https://doi.org/10.1007/978-981-15-5764-4_23
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