Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140790
Title: A novel feature incremental learning method for sensor-based activity recognition
Authors: Hu, Chunyu
Chen, Yiqiang
Peng, Xiaohui
Yu, Han
Gao, Chenlong
Hu, Lisha
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Hu, C., Chen, Y., Peng, X., Yu, H., Gao, C., & Hu, L. (2019). A novel feature incremental learning method for sensor-based activity recognition. IEEE Transactions on Knowledge and Data Engineering, 31(6), 1038-1050. doi:10.1109/tkde.2018.2855159
Journal: IEEE Transactions on Knowledge and Data Engineering
Abstract: Recognizing activities of daily living is an important research topic for health monitoring and elderly care. However, most existing activity recognition models only work with static and pre-defined sensor configurations. Enabling an existing activity recognition model to adapt to the emergence of new sensors in a dynamic environment is a significant challenge. In this paper, we propose a novel feature incremental learning method, namely the Feature Incremental Random Forest (FIRF), to improve the performance of an existing model with a small amount of data on newly appeared features. It consists of two important components - 1) a mutual information based diversity generation strategy (MIDGS) and 2) a feature incremental tree growing mechanism (FITGM). MIDGS enhances the internal diversity of random forests, while FITGM improves the accuracy of individual decision trees. To evaluate the performance of FIRF, we conduct extensive experiments on three well-known public datasets for activity recognition. Experimental results demonstrate that FIRF is significantly more accurate and efficient compared with other state-of-the-art methods. It has the potential to allow the dynamic exploitation of new sensors in changing environments.
URI: https://hdl.handle.net/10356/140790
ISSN: 1041-4347
DOI: 10.1109/TKDE.2018.2855159
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TKDE.2018.2855159
Fulltext Permission: none
Fulltext Availability: No Fulltext
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