Abstract
A methodology for mining data coming from mobile phone accelerometers is proposed in order to discover movement patterns in Alzheimer patients and to explore the relation of these patterns with the stage of the disease. This methodology processes the data provided by the accelerometer to extract features of the patient movement patterns. This information is used to train a neural network that relates the patient movement patterns with the stage of the disease (early, middle or late). This proposal based on neural network classifiers is compared with other machine learning classifiers. Moreover, this methodology is applied in a case study with 35 patients. Initial experiments are promising with a success rate up to 83 percent. The projection and exploitation of the results of our analysis are subject to ulterior extensive validation of the proposed technique.
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Acknowledgments
This work was partially supported by project PAC::LFO of the Spanish Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia under grant MTM2014-55262-P, by project BASMATI of the Spanish Programa Nacional de Investigación, Ministerio de Ciencia e Innovación, under grant TIN2011-27479-C04-04 and by the Spanish Ministerio de Economía y Competitividad under grant MTM2014-56235-C2-2-P. We gratefully acknowledge the “Asociación de Familiares de Enfermos de Alzheimer en Cantabria” and Pablo Cobo García for their participation in the various studies.
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Duque, R., Nieto-Reyes, A., Martínez, C., Montaña, J.L. (2016). Detecting Human Movement Patterns Through Data Provided by Accelerometers. A Case Study Regarding Alzheimer’s Disease. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2016. Lecture Notes in Computer Science(), vol 10069. Springer, Cham. https://doi.org/10.1007/978-3-319-48746-5_6
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