Psoriatic Arthritis (PsA) is a chronic, inflammatory disease affecting joints, substantially impacting patients' quality of life, with European guidelines for managing PsA emphasizing the importance of assessing hand function. Here, we present a set of novel digital biomarkers (dBMs) derived from a touchscreen-based serious game approach, DaktylAct, intended as a proxy, gamified, objective assessment of hand impairment, with emphasis on fine motor skills, caused by PsA. This is achieved by its design, where the user controls a cannon to aim at and hit targets using two finger pinch-in/out and wrist rotation gestures. In-game metrics (targets hit and score) and statistical features (mean, standard deviation) of gameplay actions (duration of gestures, applied pressure, and wrist rotation angle) produced during gameplay serve as informative dBMs. DaktylAct was tested on a cohort comprising 16 clinically verified PsA patients and nine healthy controls (HC). Correlation analysis demonstrated a positive correlation between average pinch-in duration and disease activity (DA) and a negative correlation between standard deviation of applied pressure during wrist rotation and joint inflammation. Logistic regression models achieved 83% and 91% classification performance discriminating HC from PsA patients with low DA (LDA) and PsA patients with and without joint inflammation, respectively. Results presented here are promising and create a proof-of-concept, paving the way for further validation in larger cohorts.

This paper presents the NAct (Nutrition & Activity) Ontology, designed to drive personalised nutritional and physical activity recommendations and effectively support healthy living, through a reasoning-based AI decision support system. NAct coalesces nutritional, medical, behavioural and lifestyle indicators with potential dietary and physical activity directives. The paper presents the first version of the ontology, including its co-design and engineering methodology, along with usage examples in supporting healthy nutritional and physical activity choices. Lastly, the plan for future improvements and extensions is discussed.

The primary manifestations of Parkinson Disease (PD) concern abnormalities of movement associated with the constant deterioration of motor skills. Such motor impairment affects patients' movement accuracy and coordination, disrupting their daily life. Taking into account recent studies stating that computer-based physical therapy games can be used as a PD rehabilitation option, we propose a novel Exergame, the iPrognosis Warming up Game (http://www.i-prognosis.eu/), as a user-friendly tool that could both serve as a computer-based physical therapy game, as well as a means of accurately and automatically identifying the severity of PD motor symptoms. To this regard, we propose a novel deep learning methodology for motor impairment stage prediction that relies solely on human body motion data extracted from the recorded game sessions. Experimental results using a dataset of both early and advanced PD patients reveal a good classification performance of the proposed methodology, predicting the motor impairment stage of PD patients and paving the way for additional research in the field.

The use of serious games in health care interventions sector has grown rapidly in the last years, however, there is still a gap in the understanding on how these types of interventions are used for the management of the Parkinson Disease (PD), in particular. Targeting intelligent early detection and intervention in PD area, the Personalized Game Suite (PGS) design process approach is presented as part of the H2020 i-PROGNOSIS project that introduces the integration of different serious games in a unified platform (i.e., ExerGames, DietaryGames, EmoGames, and Handwriting/Voice Games). From the methodological point of view, to facilitate the visualization of 14 game-scenarios, the system interface and the PD contexts, the storyboarding technique was adopted here. Overall, the realization of the PGS sets the basis for establishing a holistic framework that could aim at improving motor and non-motor symptoms, in order to inform health care providers and policy makers for its inclusion in routine management for PD.

As a decision support tool, a hybrid modelling can offer the ability to better understand the dynamics of a particular ecosystem. This paper proposes a hybrid approach that may serve as a means to synthesize/represent knowledge obtained from the data, in order to explore online learning environment (OLE) states, based on different scenarios. The potentiality of the quality of collaboration (QoC) within an Internet-based computer-supported collaborative learning environment and the quality of interaction (QoI) with a learning management system (LMS), both involving fuzzy logic-based modeling, as vehicles to improve the personalization and intelligence of an OLE is explored. In this approach, a novel framework could be established, when bridging the fields of blended- and collaborative-learning into an enhanced educational environment. The combined measures (i.e., QoC, QoI) can form the basis for a more realistic approach of OLEs within the concept of semantic Web and the associated Web 3.0 features, as they effectively capture the behaviour of the stakeholders involved in the context of Higher Education. Finally, a potential case study of the examined hybrid modelling (FuzzyQoC/I), referring to the “i-Treasures” European FP7 Programme, is discussed, to explore its functionality/applicability under pragmatic learning scenarios, serving as a proof of concept.