Abstract
Data visualization is key in the Big Data context, enabling different cognitive perspectives over large datasets. These visual perspectives can prompt relevant advantages with regard to healthcare records, because they may contribute to a faster, more understandable, and adequate way to capture patients' health history and overall condition, thus improving healthcare quality. Timelines are well-known visual artifacts that help healthcare professionals to visualize patients' electronic health records (EHR) over a time period. As data stored in EHR tend to quickly grow with each interaction between patient and healthcare system both number and size, traditional linear timelines are can increasingly become more difficult to manage visually, as they often span over different screens. Considering that a holistic analysis is desirable to provide proper and quality health services, data visualization should enable a seamless understanding of patients' health history and overall condition. When dealing with critical episodes - such as an emergency - where time is an important factor, this is even more decisive. Furthermore, traditional timelines do not support multidimensional data representation. This paper presents a new visual model of time-dependent EHR based on radial models. It is capable of simultaneously displaying several data categories that characterize patients' medical history, enabling medical professionals to be aware of different data categories over time in a single display, without the need to scroll between screens.

Abstract
Falls, especially in the elderly, are one of the main factors of hospitalization. Time-consuming intervention can be fatal or cause irreversible damages to the victims. On the other hand, there is currently a significant amount of smart clothing equipped with various sensors, particularly gyroscopes and accelerometers, which can be used to detect accidents. The creation of a tool that automatically detects eventual falls allows helping the victims as soon as possible. This works focuses in the automatic fall detection from sensors signals using long short-term memory networks. To train and test this approach, the Sisfall dataset is used, which considers the simulation of 23 adults and 15 older people. These simulations are based on everyday activities and the falls that may result from their execution. The results indicate that the procedure provides an accuracy score of 97.1% on the test set.

Abstract
This paper reports a scoping review of the literature to identify the inquiry instruments being used to evaluate the usability of AAL solutions, which resulted in the inclusion of 35 studies. The results show that a significant number of the included studies reported the use of non-valid inquiry instruments, such as ad-hoc questionnaires. Among the studies using valid and reliable inquiry instruments, System Usability Scale (SUS) emerged as the most used one. In general, valid and reliable inquiry instruments are being used together with additional data gathering methods, to perform comprehensive usability evaluations. Moreover, in terms of the quality of the design of the included studies, it should be pointed the adequacy of the participants' characteristics and the tasks they performed. In turn, these studies did not present evidence of the preparation and independence of the evaluators.

Abstract
This paper presents a scoping review of the literature on user-centred usability evaluation of Embodied Communication Agents (ECAs) embedded in digital solutions to support older adults. Twenty-nine studies were included in this scoping review. The included studies reported the implementation of digital solutions to promote: (i) secure and supportive environment; (ii) healthcare provision; (iii) healthy lifestyles; and (iv) social involvement and active participation. In terms of usability evaluation, it was not possible to identify a common understanding on the methods and instruments being used since there is high heterogeneity in terms of experimental design. Moreover, some methodological flaws were identified when analysing the experimental design of the included studies. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper presents a systematic mapping review of the literature on innovative digital solutions to detect cognitive impairment of community-dwelling older adults. Seventy-six articles were included in this mapping review. Most of the included articles (i.e., 65 articles) reported the implementation and validation of computerized versions of paper-based neuropsychological tests. In turn, 11 studies are related to the application of emerging technologies to detect cognitive decline, including serious games, virtual reality, and data analytics (e.g., algorithms to analyse data from ubiquitous daily activity and interaction sensing) approaches. From these 11 studies, four include experimental setups to determine if the developed digital solutions can discriminate cognitive impairments. Based on the mapping review findings is possible to conclude that further research is required to develop cognitive screening approaches alternative to computerized versions of paper-based neuropsychological tests.

Abstract
During the last decades, local, regional, and national governments promoted the development of smart cities, aiming the integration of traditional urban infrastructures and information technologies to provide high quality and sustainable urban services. Smart cities' implementations may change the way the individuals experience the urban spaces. Looking specifically to older adults, smart cities' applications have the potential of promoting their autonomy, independence, safety, well-being, social participation, and inclusion. This paper presents a survey of the scientific literature aiming to analyse current evidence related to smart cities' applications to support older adults and to identify issues for future research.