The aging population creates the need to promote and encourage healthy and active lifestyles to help prevent chronic diseases or injuries and consequently alleviate the pressure on healthcare systems. Technological advancements present an opportunity to monitor people more continuously and provide them with useful and relevant information that can indicate potential problems or simply help them to stay or be more active and healthy. Smart mirrors offer the promising capability of monitoring individuals daily, in their own homes, without interfering with their usual routine, a key factor for user acceptance and widespread adoption. To obtain an overview of the state-of-the-art on smart mirrors for health/well-being monitoring, we performed a review of the literature on the topic, focusing on mirrors incorporating sensing capabilities. Relevant information was then extracted from the articles found and a critical analysis was carried out. Based on the results of this analysis, as well as our own experience, we provide recommendations we consider important to take into account in the future development of smart mirrors that monitor people's health and well-being.

People with language impairments can have difficulties expressing themselves to others, leading to major limitations to their safety, independence, and quality of life in general. Aphasia is an example of an acquired language impairment that affects many people (around 2 million in the United States), being commonly caused by stroke, but also by other brain injuries. Several augmentative and alternative communication solutions are available to help people with communication difficulties, but they are generally not suitable for all contexts of use (e.g., lying in bed). In the scope of the “APH-ALARM” project, which aimed at developing solutions to support people with Aphasia, we envision a system for the bedroom that enables conveying messages to be sent to a caregiver or relative, for example. Focusing on gesture input, in this contribution, we investigated if smartwatch sensors and machine learning (ML) can be used to recognise arm gestures executed while lying. We explored different factors, namely the feature set, size of the sliding window used for feature extraction, and ML classifier. The results obtained with data gathered from ten subjects are promising, with the best factor combinations for the user-independent solution leading to a mean macro F1 score of 94% or 95%. They demonstrate the potential of using wearables to develop a gesture input modality for the in-bed scenario, which can also potentially be extended to other contexts (e.g., sitting in a bed, chair, or sofa, or standing). This research also provides useful insights that inform future work, including the development and deployment of communication support systems that can benefit not only people with communication difficulties (e.g., more independence), but also those caring for them (e.g., more peace of mind). • A new gesture-based interaction modality for communication support is proposed. • The proposal is aligned with the needs of users with Aphasia while lying in bed. • The use of wearable sensors for arm gesture recognition is investigated. • The best achieved mean F1 score was of 95%, for a user-independent solution.

The environments we live in are becoming increasingly smarter, creating the need of finding suitable ways to interact with them. While interaction modes such as speech, text, and touch remain prevalent, there are associated challenges related to background noise interference, accessibility issues, and reliance on devices (e.g., mobile, wearable). Gesture-based interaction emerges as a promising alternative or complement to those modes. In this context, radars represent a good option for enabling gesture recognition, since they rely on radio waves to detect moving targets, not presenting the disadvantages of other sensors such as cameras and wearables, which can be considered too intrusive by the users. This contribution intends to investigate the capabilities of radars for gesture-based interaction in smart environments, focusing on the possibility of using data provided by a radar and transfer learning to recognize a set of pre-defined arm gestures regardless of the distance between the user and radar. The results obtained based on data collected from a subject standing at four different distances (1, 2, 3, and 4 m) from the radar, showed a poor performance when considering a distance-independent solution. For a distance- dependent solution, relatively good results were achieved when considering all four distances together (mean accuracy of 96 %). These results are very useful for future developments of gesture- based interaction with smart environments in real scenarios.

People with aphasia (PwA) face a set of communication barriers in several contexts, e.g., in social contexts. Since augmentative and alternative communication (AAC) tools are often too general and provide limited support to this audience, the aim of this work is to explore novel ways of supporting communication for PwA that take into account their needs and characteristics and move beyond the scope covered by current solutions. This work describes the design and development of the first proof-of-concept of Aphluentia, a tool to support communication in everyday social situations for people with fluent aphasia, designed by working with speech and language therapists with experience working with PwA to inform and evaluate its characteristics. At its current stage, Aphluentia establishes the grounds for discussion and assessment of the proposal by PwA.

Video can be a valuable source of information for monitoring first responders during operations in the field. In this paper we propose the MonitorMe, an application that supports online video monitoring of first responders. MonitorMe allows capturing video from a personal perspective, using a smartphone camera, and sending it to a remote observer. In order to reduce battery consumption and bandwidth usage, MonitorMe modulates the video frame rate according to the user activity/speed. The latter are estimated using the smartphone built-in accelerometer. The results have shown the potential of MonitorMe as a reliable non-GPS solution, which can be used for remote online monitoring of first responders in action.