This paper describes the analysis and design of a tablet-based gaming platform for seniors that promotes their quality-of-life and well-being by incorporating cognitive training mechanisms. A literature review of age-related changes and games for seniors indicated 'casual games' have the characteristics necessary to provide an enjoyable user experience for the senior audience. Having concluded that these games should target cognitive stimulation, the authors analysed mechanisms to achieve this purpose and compiled them into a matrix to be used as a starting point for the games design process. In parallel, the authors also gathered seniors' preferences and requirements regarding games, through observations and a game book. Low-, medium-, and high-fidelity prototypes for a gaming cognitive platform were developed, evaluated with end-users, and iteratively improved. Results showed that seniors easily interacted with the platform and were willing to use it in the future. Re sults and experience led to the identification of 10 rules of thumb that can be beneficial if applied to related projects. This paper concludes by identifying exciting areas for future research and development

All over the world there are millions of people who are living with long-term motor impairments caused by a stroke or any other kind of corticospinal tract injuries. The physical rehabilitation of these patients is usually slow and demotivating. In this paper we introduce Kinteract, a novel solution that applies the paradigm of using motion-based games in the rehabilitation process, with the added value of providing a motion sensor server that supports a growing array of motion sensors (currently Microsoft Kinect, Leap Motion and Orbotix Sphero) and merge their data into a single protocol that can be used for any purpose. The use of different sensors, even at the same time, allows the rehabilitation of specific parts of the body. This data can be stored in a server for physicians to analyse and can clearly reveal the evolution of the patient in the rehabilitation process

Due to the increasingly alarming consequences of climate change, pests are becoming a growing threat to grape quality and viticulture yields. Estimating the quantity and type of treatments to control these diseases is particularly challenging due to the unpredictability of insects’ dynamics and intrinsic difficulties in performing pest monitoring. Conventional pest monitoring programs consist of deploying sticky traps on vineyards, which attract key insects and allow human operators to identify and count them manually. However, this is a time-consuming process that usually requires in-depth taxonomic knowledge. This scenario motivated the development of EyesOnTraps, a novel AI-powered mobile solution for pest monitoring in viticulture. The methodology behind the development of the proposed system merges multidisciplinary research efforts by specialists from different fields, including informatics, electronics, machine learning, computer vision, human-centered design, agronomy and viticulture. This research work resulted in a decision support tool that allows winegrowers and taxonomy specialists to: (i) ensure the adequacy and quality of mobile-acquired sticky trap images; (ii) provide automated detection and counting of key insects; (iii) register local temperature near traps; and (iv) improve and anticipate treatment recommendations for the detected pests. By merging mobile computing and AI, we believe that broader technology acceptance for pest management in viticulture can be achieved via solutions that work on regular sticky traps and avoid the need for proprietary instrumented traps.