Abstract
This paper describes an experimental evaluation of the main machine learning supervised techniques to be used for the human activities recognition in the context of technological education using data collected from smartphones sensors. The overall goal is to use the recognition of activities to identify students with attention deficit or hyperactivity problems, by recognizing three activities: walking, standing and sitting. Hence, this work focuses on developing activities recognition method of the students. The methodology consists in: collecting data where the user explicitly states what activity he/she is doing; applying various techniques to automatically recognize the activities; and measuring the degree of accuracy of each technique. The results shows that techniques such as Bayesian inference and SVM (Support Vector Machine) have smaller accuracy than techniques based on decision tree and kNN (k-nearest neighbors). Furthermore, the techniques based on decision trees have a constant computational cost, while the kNN depends on the number of samples.
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Acknowledgements
The results presented in this paper were obtained through the project entitled “Systems for Behavior Assessment and Intelligent Recommendation for Educational Environments and e-Health” sponsored by Samsung Eletrônica da Amazônia Ltda under Brazilian Federal Law No. 8248/91; and FAPEAM through projects 1135/2011 (PRONEX) and 582/2014 (PROTI-PESQUISA).
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Leitão, G. et al. (2016). Experimental Evaluation on Machine Learning Techniques for Human Activities Recognition in Digital Education Context. In: Koch, F., Koster, A., Primo, T. (eds) Social Computing in Digital Education. SOCIALEDU 2015. Communications in Computer and Information Science, vol 606. Springer, Cham. https://doi.org/10.1007/978-3-319-39672-9_9
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DOI: https://doi.org/10.1007/978-3-319-39672-9_9
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