Abstract
How to deal with multi-modality data from different types of devices is a challenging issue for accurate recognition of human activities in a smart environment. In this paper, we propose a multimodal fusion enabled ensemble approach. Firstly, useful features collected from Bluetooth beacons, binary sensors, and smart floor are extracted and presented by fuzzy logic based-method with variable-size temporal windows. Secondly, a group of support vector machine classifiers are used to perform the classification task. Finally, a weighted ensemble method is used to obtain the final prediction. Especially, by applying the geometric framework, we are able to obtain the optimal weights for the ensemble. The proposed approach is evaluated on the UJAmI dataset. The experimental results demonstrate the efficacy and robustness of the proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 1-18 |
| Number of pages | 19 |
| Journal | Health Informatics Journal |
| Volume | 29 |
| Issue number | 2 |
| Early online date | 28 Apr 2023 |
| DOIs | |
| Publication status | Published (in print/issue) - 30 Jun 2023 |
Bibliographical note
Funding Information:The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research received the support from the Science and Technology Development Project of Weifang City 2020, China, Grant No. 2020GX006
Publisher Copyright:
© The Author(s) 2023.
Keywords
- Original Research Article
- Ensemble learning
- Feature-level fusion
- Geometric framework
- Human activity recognition
- Multimodal fusion
