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Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. This work presents the LifeChamps solution built on top of cutting-edge Big Data architecture and HPC infrastructure concepts. An innovative architecture was envisioned supported by the Big Data Value Reference Model and answering the system requirements from high to low level and from logical to physical perspective, following the "4+1 architectural model".

Our work exhibits how previous projects on the Active and Healthy Ageing field have advanced to the conception of CAPTAIN, a radically new approach towards increased end-user acceptance. The goal is to create intuitive technology that does not require specific skills for interaction and blends in with real life. CAPTAIN will be co-designed by all types of stakeholders, including older adults, involved in all stages, from the initial design to delivery of the final system.

Chronic pain is one of the most common health problems affecting daily activity, employment, relationships and emotional functioning. Unfortunately, limited access to pain experts, the high heterogeneity in terms of clinical manifestation and treatment results, contribute in failure to manage efficiently and effectively pain. Information and Communication Technology (ICT) can be a valuable tool, enabling better self-management and self-empowerment of pain. To this direction, this paper reports on the design of a novel technical infrastructure for chronic pain self-management based on an Intelligent Personal Health Record (PHR) platform. The designed platform targets, among others, at improving the knowledge on the patient data, effectiveness and adherence to treatment and providing effective communication channels between patients and clinicians.

We aim to solve which off-the-shelf motion sensor device is the most suitable for extensive usage in PC open-source exergames for the elderly. To solve this problem, we studied the specifications of the market-available sensors to reduce the initial, broad set of sensors to only two candidates: the Nintendo Wii controllers and the Microsoft© Kinect™ camera. The capabilities of these two are tested with a demo implementation. We take into account both the accuracy in the movement-detection of the sensors, and the software-related issues. Our outcome indicates that the Microsoft© Kinect™ camera is the option that currently provides the best solution for our purpose. This study can be helpful for researchers to choose the device that suits their project needs better, removing the sensor-choosing task time from their schedule.