Adaptive Activity and Environment Recognition for Mobile Phones

作者：Parviainen Jussi^*; Bojja Jayaprasad; Collin Jussi; Leppanen Jussi; Eronen Antti

来源：Sensors, 2014, 14(11): 20753-20778.

DOI：10.3390/s141120753

• mobile sensing
• classifier design and evaluation
• activity recognition
• environment recognition
• Bayes classifier
• adaptation
• pervasive computing

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.

• 出版日期2014-11