A steady rise has been observed in the percentage of elderly people who want and are still able to contribute to society. Therefore, early retirement or exit from the labour market, due to health-related issues, poses a significant problem. Nowadays, thanks to technological advances and various data from different populations, the risk factors investigation and health issues screening are moving towards automation. In the context of this work, a worker-centric, IoT enabled unobtrusive users health, well-being and functional ability monitoring framework, empowered with AI tools, is proposed. Diabetes is a high-prevalence chronic condition with harmful consequences for the quality of life and high mortality rate for people worldwide, in both developed and developing countries. Hence, its severe impact on humans' life, e.g., personal, social, working, can be considerably reduced if early detection is possible, but most research works in this field fail to provide a more personalized approach both in the modeling and prediction process. In this direction, our designed system concerns diabetes risk prediction in which specific components of the Knowledge Discovery in Database (KDD) process are applied, evaluated and incorporated. Specifically, dataset creation, features selection and classification, using different Supervised Machine Learning (ML) models are considered. The ensemble WeightedVotingLRRFs ML model is proposed to improve the prediction of diabetes, scoring an Area Under the ROC Curve (AUC) of 0.884. Concerning the weighted voting, the optimal weights are estimated by their corresponding Sensitivity and AUC of the ML model based on a bi-objective genetic algorithm. Also, a comparative study is presented among the Finnish Diabetes Risk Score (FINDRISC) and Leicester risk score systems and several ML models, using inductive and transductive learning. The experiments were conducted using data extracted from the English Longitudinal Study of Ageing (ELSA) database.

Serious games are computer games used as educational technology or as a vehicle for presenting or promoting a point of view. Serious games are intended to provide an engaging, self-reinforcing context in which to motivate and educate the players towards non-game events or processes, including business operations, training, marketing and advertisement. The potential of games for entertainment and learning has been demonstrated thoroughly from research and clearly in the market place. Unfortunately, the investments committed to entertainment dwarfs that which is committed for more serious purposes. Furthermore, game development has become more complex, expensive, and burdened with a long development cycle. In this paper we introduce PlayMancer, a work in progress that aims to overcome such barriers by augmenting existing 3D gaming engines with new possibilities and thusly creating a novel development framework. In section I of this paper we briefly survey the serious games market. In section II we introduce the PlayMancer project and its objectives, whereas in section III we present the platform architecture. Section VI describes an application scenario where a PlayMancer-based game is being used as additional therapeutic tool to treat chronic mental disorders, such as eating disorders and behavioral addiction.

Medical film digitizers play an important transitory role as digital-to-analogue bridges in radiology. Their use requires performance evaluation to assure medical image quality. A complete quality control protocol is presented, based on a set of test objects adaptable to the specification of various digitizers. The protocol includes parameters such as uniformity, input-output response, noise, geometric distortion, spatial resolution, low contrast discrimination, film slippage and light leakage, as well as associated measurement methods. The applicability of the protocol is demonstrated with two types of medical film digitizers; a charge-coupled device (CCD) digitizer and a laser digitizer. The potential value of the protocol is also discussed.