Abstract
This paper presents a process for extracting knowledge for physical activity recognition, from accelerometer data provided by mobile devices. Starting from a dataset collected by three different users, knowledge discovery is performed through a phase of feature extraction from raw data, minimizing the number of statistical features and optimizing the classification process. The development and comparison of classifying models over this new dataset, using both offline and online algorithms, is also described. Phases of data acquisition, pre-processing and classification are detailed, and experimental results for different machine learning algorithms are provided. For these results, different evaluation criteria are used, and the best algorithm is selected according to these criteria. Final results show success rates around 98%, while other similar works offer rates around 87%.
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Duque, A., Ordóñez, F.J., de Toledo, P., Sanchis, A. (2012). Offline and Online Activity Recognition on Mobile Devices Using Accelerometer Data. In: Bravo, J., Hervás, R., Rodríguez, M. (eds) Ambient Assisted Living and Home Care. IWAAL 2012. Lecture Notes in Computer Science, vol 7657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35395-6_29
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DOI: https://doi.org/10.1007/978-3-642-35395-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35394-9
Online ISBN: 978-3-642-35395-6
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