RehabNet: A Distributed Architecture for Motor and Cognitive Neuro-Rehabilitation Understanding the Human Brain through Virtual Environment Interaction Athanasios Vourvopoulos 1,2 , Ana Lúcia Faria 1,3 , Mónica S Cameirão 1 , Sergi Bermúdez i Badia 1,2 1 Madeira Interactive Technologies Institute, Universidade da Madeira (UMa) Funchal, Portugal 2 Centro de Ciências Exatas e da Engenharia, Universidade da Madeira (UMa) Funchal, Portugal 3 Faculdade de Psicologia e de Ciências da Educação, Universidade de Coimbra Coimbra, Portugal athanasios.vourvopoulos@m-iti.org Abstract— Every year millions of people worldwide suffer from stroke, resulting in motor and/or cognitive disability. As a result, patients experience an increased loss of independence, autonomy and low self-esteem. Evolving to a chronic condition, stroke requires of continuous rehabilitation and therapy. Current ICT approaches, with the use of robotics and Virtual Reality, show some benefits over conventional therapy. However, most of the novel approaches are suitable only for a reduced subset of patients. RehabNet proposes an inclusive approach towards an open and distributed architecture for ‘in-home’ neuro- rehabilitation and monitoring by means of non-invasive ICT. In this paper we present the RehabNet architecture, its design and the implementation of a combined motor-and-cognitive system for post-stroke rehabilitation. Keywords—stroke rehabilitation; serious games; brain- computer interfaces; neurofeedback; virtual reality I. INTRODUCTION Stroke is currently one of the main causes of adult disability, with about 16 million new strokes worldwide every year [1]. Stroke survivors very often suffer from chronic conditions, which result in a significant psychosocial and economical impact [2]. In order to minimize their loss of independency, stroke survivors require continuous rehabilitation and therapy [3]. Moreover, the implementation of effective treatments in the first weeks following stroke, even those with small reductions in disability, can produce significant public health benefits despite the high cost of these treatments [4]. In this context, ICT based neurorehabilitation systems can provide novel and effective rehabilitation solutions. Innovative approaches that are based on neuroscientific hypotheses of brain recovery through Virtual Reality (VR) and serious games show great potential in stroke rehabilitation because these technologies can support requirements for an effective re-training of the patient [5]. VR allows creating fully controlled environments that define training tasks specifically designed to target the individual needs of the patients, and intensive movement training can be embedded in motivating tasks, making use of augmented feedback and reward [6]. Specifically, personalised VR approaches have been shown to accelerate the recovery process compared to control groups with non-ICT based interventions [7]. Despite evidence on the benefits of VR training [8], accessibility to these therapies still remains a challenge because most VR approaches are suitable only to reduced subsets of patients, generally those with better recovery prognostics [8]. In this context, the RehabNet project aims at expanding modern VR rehabilitation approaches to (1) include patients with a broad range of impairments (motor and cognitive); (2) provide low cost at-home rehabilitation solutions; and (3) develop a better understanding on the brain recovery process and the effectiveness derived from these solutions. II. BACKGROUND Two recent meta-analysis of virtual reality studies in stroke rehabilitation included 29 studies comparing the impact of virtual reality with alternative or no intervention [8][9]. The goal of those systematic reviews was to provide a comprehensive overview of the available evidence on a specifically identified health related question, allowing for a rigorous analysis with limited bias. The VR studies that were included evaluated the effect of VR training on upper limb function, grip strength, gait speed and daily living functions. Training tasks mostly involved everyday life activities, like shopping, sport activities, driving simulations and the use of public transportation simulation. In the case of upper limb re- training, 16 studies were analysed with a total sample size of 392 patients. In most of the upper-limb studies, motion capture was used as input to the VR systems, either tracked from a camera or by using controllers with 3D space positioning such as the Nintendo Wii remote (Nintendo, Kyoto, Japan). A minority of those studies used robotic devices and arm exoskeletons with position sensors. All the above mentioned 16 upper-limb studies required a minimum cognitive and/or motor control for the patient to interact with the VR systems and complete the desired tasks. The average Mini–Mental State Examination [10] score required was as high as 21 (mild cognitive impairment) and a large percentage of studies excluded patients with perceptual deficits (43%), aphasia (35%), apraxia (29%) or pain (29%). On the motor control side, all VR systems included in these reviews for upper-limb