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Sensors 2014, 14(11), 20753-20778; doi:10.3390/s141120753

Adaptive Activity and Environment Recognition for Mobile Phones

1
Department of Pervasive Computing, Tampere University of Technology, FI-33101 Tampere, Finland
2
Nokia Technologies, FI-33721 Tampere, Finland
*
Author to whom correspondence should be addressed.
Received: 10 September 2014 / Revised: 16 October 2014 / Accepted: 20 October 2014 / Published: 3 November 2014
(This article belongs to the Special Issue HCI In Smart Environments)
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Abstract

In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these data sources were investigated, and a Bayesian maximum a posteriori classifier was used for classifying between several user activities and environments. The accuracy of the method was evaluated on a dataset collected in a real-life trial. In addition, comparison to other state-of-the-art classifiers, namely support vector machines and decision trees, was performed. To make the system adaptive for individual user characteristics, an adaptation algorithm for context model parameters was designed. Moreover, a confidence measure for the classification correctness was designed. The proposed adaptation algorithm and confidence measure were evaluated on a second dataset obtained from another real-life trial, where the users were requested to provide binary feedback on the classification correctness. The results show that the proposed adaptation algorithm is effective at improving the classification accuracy. View Full-Text
Keywords: mobile sensing; classifier design and evaluation; activity recognition; environment recognition; Bayes classifier; adaptation; pervasive computing mobile sensing; classifier design and evaluation; activity recognition; environment recognition; Bayes classifier; adaptation; pervasive computing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Parviainen, J.; Bojja, J.; Collin, J.; Leppänen, J.; Eronen, A. Adaptive Activity and Environment Recognition for Mobile Phones. Sensors 2014, 14, 20753-20778.

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