Adaptive Activity and Environment Recognition for Mobile Phones

Sensors

Jussi Parviainen²  Jayaprasad Bojja²  Jussi Collin²  Jussi Leppänen¹ 

[1] Nokia Technologies, FI-33721 Tampere, Finland; E-Mails:;Department of Pervasive Computing, Tampere University of Technology, FI-33101 Tampere, Finland; E-Mails:

关键词: mobile sensing;    classifier design and evaluation;    activity recognition;    environment recognition;    Bayes classifier;    adaptation;    pervasive computing;   

DOI  :  10.3390/s141120753

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.