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. This work was supported in part by the TIN2018-095232-B-C21, in part by the SGR-2017 1742, in part by the Nestore ID: 769643, in partby the Validithi EIT Health Program, in part by the CERCA Programme/Generalitat de Catalunya, in part by the Spanish Ministry of Economy and Competitiveness, and in part by the European Regional Development Fund (MINECO/ERDF, EU) through the program Ramon y Cajal. The work of Alejandro Cartas was supported by a doctoral fellowship from the Mexican Council of Science andTechnology (CONACYT) under Grant 366596. Peer Reviewed