Journal article
Collegial activity learning between heterogeneous sensors
Knowledge and information systems, Vol.53(2), pp.337-364
11/2017
Handle:
https://hdl.handle.net/2376/108558
PMCID: PMC5627625
PMID: 28989212
Abstract
Activity recognition algorithms have matured and become more ubiquitous in recent years. However, these algorithms are typically customized for a particular sensor platform. In this paper, we introduce PECO, a Personalized activity ECOsystem, that transfers learned activity information seamlessly between sensor platforms in real time so that any available sensor can continue to track activities without requiring its own extensive labeled training data. We introduce a multi-view transfer learning algorithm that facilitates this information handoff between sensor platforms and provide theoretical performance bounds for the algorithm. In addition, we empirically evaluate PECO using datasets that utilize heterogeneous sensor platforms to perform activity recognition. These results indicate that not only can activity recognition algorithms transfer important information to new sensor platforms, but any number of platforms can work together as colleagues to boost performance.
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Details
- Title
- Collegial activity learning between heterogeneous sensors
- Creators
- Kyle Feuz - Department of Computer Science Weber State University Ogden UT 84408 USADiane Cook - School of Electrical Engineering and Computer Science Washington State University Pullman WA 99164 USA
- Publication Details
- Knowledge and information systems, Vol.53(2), pp.337-364
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- Springer London; London
- Grant note
- 1262814 / National Science Foundation (http://dx.doi.org/10.13039/100000001)
- Identifiers
- 99900547003001842
- Language
- English
- Resource Type
- Journal article