Abstract
Recently, the human stroke is gathering the focus as one of the diseases with higher mortality and social impact. In addition, it has a long-term treatment and high rehabilitation costs. Therefore, the early diagnosis of stroke can take advantage in avoiding the stroke itself or highly reducing its effects. Up to our knowledge, no previous study on stroke early diagnosis has been published in the literature. This study deals with the early detection of the stroke based on accelerometers and mobile devices. First, a discussion on the problem is presented and the design of the approach is outlined. In a first stage, it is necessary to determine what is the subject doing at any moment; thus, human activity recognition is performed. Afterwards, once the current activity is estimated, the detection of anomalous movements is proposed. Nevertheless, as there is no data available to learn the problem, a realistic proposal for simulating stroke episodes is presented, which lead us to draw the conclusions.
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González, S., Villar, J.R., Sedano, J., Chira, C. (2013). A Preliminary Study on Early Diagnosis of Illnesses Based on Activity Disturbances. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_62
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DOI: https://doi.org/10.1007/978-3-319-00551-5_62
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