@Article{AlejandroCartas2020,
author="Alejandro Cartas
and Petia Radeva
and Mariella Dimiccoli",
title="Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications",
journal="IEEE Access",
year="2020",
volume="8",
pages="77344--77363",
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.",
optnote="MILAB; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3436), last updated on Thu, 15 Sep 2022 10:22:44 +0200",
doi="10.1109/ACCESS.2020.2990333",
opturl="https://ieeexplore.ieee.org/document/9078767"
}