Fine-Grained Mapping Between Daily Activity Features in Smart Homes
For daily activity recognition in smart homes, it is possible to reduce the effort required for labeling by transferring a trained model. This involves utilizing a labeled daily activity dataset from one smart home to recognize other activities in another. The foundation of this transfer lies in est...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/18/3/131 |
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| Summary: | For daily activity recognition in smart homes, it is possible to reduce the effort required for labeling by transferring a trained model. This involves utilizing a labeled daily activity dataset from one smart home to recognize other activities in another. The foundation of this transfer lies in establishing a shared common feature space between the two smart homes, achieved through a feature mapping approach for daily activities. However, existing heuristic feature mapping methods are often coarse, resulting in only moderate recognition performance. In this paper, we propose a fine-grained daily activity feature mapping approach. Sensors are ranked by their significance using the PageRank algorithm, and a novel alignment algorithm is introduced for sensor mapping. Experiments conducted on the publicly available CASAS dataset demonstrate that the proposed method significantly outperforms existing daily activity feature mapping approaches. |
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| ISSN: | 1999-4893 |