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Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease that affects ~7 million people worldwide, without any cure to date; however, it can be symptomatically treated. In this vein, innovative technologies can be used for the objective assessment of clinical symptoms and to provide supportive therapies at home. The present work explores the processes and the outcomes of the i-PROGNOSIS (www.i-prognosis.eu) intervention deployment in three PD clinical centres (Greece, UK, and Germany). For that purpose, 36 PD patients were recruited to voluntarily participate in the i-PROGNOSIS feasibility study, spread across the three different countries. The PD patients interacted with the i-PROGNOSIS system for up-to-three months, mainly within the clinical environment, using the provided iPrognosis Games in dedicated gaming stations that were setup in the corresponding clinical centres. Overall, the results show that the iPrognosis Games were positively evaluated by medical experts. Moreover, based on the collected feedback, the iPrognosis Games have achieved their main goals of providing an innovative, objective and usable system for the monitoring of early PD (motor and non-motor) symptomatology, by providing tools for complementing existing clinical interventions for the improvement of PD patients' quality of life.

In this paper, we introduce the i-Treasures Intangible Cultural Heritage (ICH) dataset, a freely available collection of multimodal data captured from different forms of rare ICH. More specifically, the dataset contains video, audio, depth, motion capture data and other modalities, such as EEG or ultrasound data. It also includes (manual) annotations of data, while in some cases additional features and metadata are provided, extracted using algorithms and modules developed within the i-Treasures project. We describe the creation process (sensors, capture setups and modules used), the dataset content and the associated annotations. An attractive feature of this ICH Database is that it's the first of its kind, providing annotated multimodal data for a wide range of rare ICH types. Finally, some conclusions are drawn and the future development of the dataset is discussed.

The goal of this work is to provide an overview of existing approachesregarding AI nutrition recommender systems. A breakdown of such systems into task-specific components is presented, as well as methodologies concerned with each individual component. The components of an idealized AI nutrition recommendersystem are presented and compared to state-of-the-art approachesin the corresponding area of research. Finally, identified issues insome of these areas are also discussed.