Next Article in Journal
Design of a Lossless Image Compression System for Video Capsule Endoscopy and Its Performance in In-Vivo Trials
Next Article in Special Issue
Face Recognition System for Set-Top Box-Based Intelligent TV
Previous Article in Journal
A Target Model Construction Algorithm for Robust Real-Time Mean-Shift Tracking
Previous Article in Special Issue
Laser Spot Tracking Based on Modified Circular Hough Transform and Motion Pattern Analysis
Article Menu

Export Article

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

Adaptive Activity and Environment Recognition for Mobile Phones

Department of Pervasive Computing, Tampere University of Technology, FI-33101 Tampere, Finland
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)
View Full-Text   |   Download PDF [767 KB, uploaded 5 November 2014]   |  


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

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top