As the proportion of elderly individuals in developed countries continues to rise globally, addressing their healthcare needs, particularly in preserving their autonomy, is of paramount concern. A growing body of research focuses on Ambient Assisted Living (AAL) systems, aimed at alleviating concerns related to the independent living of the elderly. This systematic review examines the literature pertaining to fall detection and Human Activity Recognition (HAR) for the elderly, two critical tasks for ensuring their safety when living alone. Specifically, this review emphasizes the utilization of Deep Learning (DL) approaches on computer vision data, reflecting current trends in the field. A comprehensive search yielded 2,616 works from five distinct sources, spanning the years 2019 to 2023 (inclusive). From this pool, 151 relevant works were selected for detailed analysis. The review scrutinizes the employed DL models, datasets, and hardware configurations, with particular emphasis on aspects such as privacy preservation and real-world deployment. The main contribution of this study lies in the synthesis of recent advancements in DL-based fall detection and HAR for the elderly, providing insights into the state-of-the-art techniques and identifying areas for further improvement. Given the increasing importance of AAL systems in enhancing the quality of life for the elderly, this review serves as a valuable resource for researchers, practitioners, and policymakers involved in developing and implementing such technologies.Graphical

Recognizing facial expressions is a challenging task both for computers and humans. Although recent deep learning-based approaches are achieving high accuracy results in this task, research in this area is mainly focused on improving results using a single dataset for training and testing. This approach lacks generality when applied to new images or when using it in in-the-wild contexts due to diversity in humans (e.g., age, ethnicity) and differences in capture conditions (e.g., lighting or background). The cross-datasets approach can overcome these limitations. In this work we present a method to combine multiple datasets and we conduct an exhaustive evaluation of a proposed system based on a CNN analyzing and comparing performance using single and cross-dataset approaches with other architectures. Results using the proposed system ranged from 31.56% to 61.78% when used in a single-dataset approach with different well-known datasets and improved up to 73.05% when using a cross-dataset approach. Finally, to study the system and humans’ performance in facial expressions classification, we compare the results of 253 participants with the system. Results show an 83.53% accuracy for humans and a correlation exists between the results obtained by the participants and the CNN.

When we develop an input device for users to communicate with computers, we have to take into account that end-users must consider the utilization of the device to be effective, efficient and satisfactory. Users whose expectations are unmet by the interface will tend to abandon it. In this paper we present a vision-based interface for motor-impaired users; a multidisciplinary group developed this interface. The user’s preferences are a critical issue when selecting an access device; therefore, user requirements should be included in the design. Usability evaluation should be integrated into relevant phases of software development. In order to evaluate the design, we present a process with multiple user studies at different development stages. We describe the combination of a development project and its implementation, with user experience considerations embedded in the process. Finally, we studied the performance of the interface through several tests, paying special attention to satisfaction and fatigue. From our results we observed that although several users found the interface tiring, their satisfaction level was encouraging, suggesting the interface is usable.