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Sensor-based activity recognition with dynamically added context

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journal contribution
posted on 2015-01-01, 00:00 authored by J Wen, Seng LokeSeng Loke, J Indulska, M Zhong
An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods.

History

Journal

EAI endorsed transactions on energy web

Volume

15

Issue

7

Article number

e4

Pagination

1 - 10

Publisher

European Alliance for Innovation

Location

[Ghent, Belgium]

eISSN

2032-944X

Language

eng

Publication classification

C Journal article; C1.1 Refereed article in a scholarly journal

Copyright notice

2015, J. Wen et al.