Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/179836
Title: | Activities of Daily Living Monitoring via a WearableCamera: Toward Real-World Applications |
Author: | Cartas Ayala, Alejandro Radeva, Petia Dimiccoli, Mariella |
Keywords: | Anàlisi de conducta Sistemes persona-màquina Behavioral assessment Human-machine systems |
Issue Date: | 27-Apr-2020 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract: | Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications. |
Note: | Reproducció del document publicat a: https://doi.org/10.1109/ACCESS.2020.2990333 |
It is part of: | IEEE Access, 2020, vol. 8, p. 77344-77363 |
URI: | http://hdl.handle.net/2445/179836 |
Related resource: | https://doi.org/10.1109/ACCESS.2020.2990333 |
ISSN: | 2169-3536 |
Appears in Collections: | Articles publicats en revistes (Matemàtiques i Informàtica) |
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