Abstract
In this paper, we present a methodology for Real-Time Activity Recognition of Interleaved Activities based on Fuzzy Logic and Recurrent Neural Networks. Firstly, we propose a representation of binary-sensor activations based on multiple Fuzzy Temporal Windows. Secondly, an ensemble of activity-based classifiers for balanced training and selection of relevant sensors is proposed. Each classifier is configured as a Long Short-Term Memory with self-reliant detection of interleaved activities. The proposed approach was evaluated using well-known interleaved binary-sensor
datasets comprised of activities of daily living.
datasets comprised of activities of daily living.
Original language | English |
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Title of host publication | MDPI Proceedings |
Publisher | MDPI |
Number of pages | 10 |
DOIs | |
Publication status | Accepted/In press - 27 Jul 2018 |
Event | 12h International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI) 2018 - Punta Cana, Dominican Republic Duration: 4 Dec 2018 → 7 Dec 2018 |
Publication series
Name | The 12th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2018) |
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Publisher | MDPI AG, Basel, Switzerland |
ISSN (Electronic) | 2504-3900 |
Conference
Conference | 12h International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI) 2018 |
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Abbreviated title | UCAmI |
Country/Territory | Dominican Republic |
City | Punta Cana |
Period | 4/12/18 → 7/12/18 |
Keywords
- Real-Time Activity Recognition
- Interleaved Activities
- Fuzzy TemporalWindows
- Long Short-Term Memory