A Semi-supervised Learning Approach for Robust Indoor-outdoor Detection with Smartphones

Valentin Radu, Panagiota Katsikouli, Rik Sarkar, Mahesh K. Marina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The environmental context of a mobile device determines how it is used and how the device can optimize operations for greater efficiency and usability. We consider the problem of detecting if a device is indoor or outdoor. Towards this end, we present a general method employing semi-supervised machine learning and using only the lightweight sensors on a smartphone. We find that a particular semi-supervised learning method called co-training, when suitably engineered, is most effective. It is able to automatically learn characteristics of new environments and devices, and thereby provides a detection accuracy exceeding 90% even in unfamiliar circumstances. It can learn and adapt online, in real time, at modest computational costs. Thus the method is suitable for on-device learning. Implementation of the indoor-outdoor detection service based on our method is lightweight in energy use -- it can sleep when not in use and does not need to track the device state continuously. It is shown to outperform existing indoor-outdoor detection techniques that rely on static algorithms or GPS, in terms of both accuracy and energy-efficiency.
Original languageEnglish
Title of host publicationProceedings of the 12th ACM Conference on Embedded Network Sensor Systems
Place of PublicationNew York, NY, USA
Number of pages15
ISBN (Print)978-1-4503-3143-2
Publication statusPublished - 2014

Publication series

NameSenSys '14

Keywords / Materials (for Non-textual outputs)

  • co-training, context detection, mobile sensing, semi-supervised learning


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