Stroke is a disabling disease that requires extensive work of rehabilitation to improve the quality of life of patients. In order to increase the compliance and motivation of the patients, stroke rehabilitation exercises have been developed in a game-like structure using a smartphone. These games were designed to promote and evaluate different movements of the upper limbs and their level of difficulty is adaptable to each patient's impairment level. The feasibility of the use of smartphone built-in inertial sensors to monitor the execution of stroke rehabilitation exercises has been assessed. The accuracy of the angles measured decreased along time and for higher angles, however the differences between real and measured angles are within acceptable limits. The usability tests in a post-stroke patient case demonstrate the applicability and motivational potential of the developed games. Gamification of stroke rehabilitation exercises using a smartphone is feasible and may be valuable for stroke rehabilitation.

According to statistics, one in every three adults ageing 65 or older falls every year. Every fall may lead to long-term consequences due to fractures or even neurological damages. These consequences have severe impact in their quality of life, independence and confidence, ultimately increasing the risk of early death. Moreover, the risk of falling increases as age advances. Fortunately, several studies reveal that specific exercise programmes may help in reducing the risk of falling if performed correctly and frequently. However, user engagement and adherence to these programmes are still low mainly due to motivational factors, since interventions are usually long, unadapted and unchallenging. In this paper, a new solution is presented, which uses the concept of interactive games using motion sensors to tackle low adherence (through gaming motivation) and help in physical rehabilitation and reduce fall risk on elderly people by improving balance, muscle strength and mobility. It is intended to be used in community or domestic unsupervised contexts and supports relatively inexpensive sensing equipment (currently Kinect R , Leap Motion R , Orbotix Sphero R and Smartphones) and common platforms (desktop and mobile). Tests were already undertaken with several individuals ageing 65 or more and the results were analysed and discussed, being generally positive, despite some issues in the movement detection algorithms.

— Hand impairment severely limits basic activities of daily living (ADL). The use of motorized hand orthoses may provide enough functional assistance to perform basic tasks, such as grasping objects. Several prototypes have been proposed in the last decade, but there are still no solutions with the desired features regarding the weight, wearability and functionality. This paper describes the overall implementation of a prototype of an assistive robotic hand orthosis (ARHO) for object grasping, that can be triggered manually or by the detection of muscular activity in the forearm using surface electromyography (sEMG). The system is being specifically designed for a person with hemiplegia resulting from a hemispherectomy. The proposed orthosis is a very preliminary but functional prototype, still far from the desired features mentioned above, but serves to show all the modules composing a low-cost implementation, and most of all to understand all the constraints and difficulties in designing such a system.

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