This systematic review aims to analyze the state-of-the-art regarding interaction modalities used on serious games for upper limb rehabilitation. A systematic search was performed in IEEE Xplore and Web of Science databases. PRISMA and QualSyst protocols were used to filter and assess the articles. Articles must meet the following inclusion criteria: they must be written in English; be at least four pages in length; use or develop serious games; focus on upper limb rehabilitation; and be published between 2007 and 2017. Of 121 articles initially retrieved, 33 articles met the inclusion criteria. Three interaction modalities were found: vision systems (42.4%), complementary vision systems (30.3%), and no-vision systems (27.2%). Vision systems and no-vision systems obtained a similar mean QualSyst (86%) followed by complementary vision systems (85.7%). Almost half of the studies used vision systems as the interaction modality (42.4%) and used the Kinect sensor to collect the body movements (48.48%). The shoulder was the most treated body part in the studies (19%). A key limitation of vision systems and complementary vision systems is that their device performances might be affected by lighting conditions. A main limitation of the no-vision systems is that the range-of-motion in angles of the body movement might not be measured accurately. Due to a limited number of studies, fruitful areas for further research could be the following: serious games focused on finger rehabilitation and trauma injuries, game difficulty adaptation based on user's muscle strength and posture, and multisensor data fusion on interaction modalities.

In this paper is presented an interfacing of a robotic arm with a motion planner and a 3D viewer (RViz). The main purpose of this work, is to manipulate and grip objects to transport them from one location to another using Robot Operating System (ROS). In order to do this task, a motion planner, who deal with constraints, i.e. collisions among the arm joints and objects, is used. Also a 3D viewer, called RViz, is used to visualize (online and offline) the motion of the arm. To easily interface the 3 degree of freedom (DOF) arm, it was designed using FreeCAD, as the output files used to print the arm, are simultaneously exported to create the Unified Robot Description Format (URDF) files required for the planner and viewer. A position-force controller for the gripper, previously presented, has been used to pick up and hold the object during the manipulation. Some experiments where carried out to asses the result of the interface, the execution of the motion planning avoiding collision and the grasp to the object. These results are shown and discussed.

Medical conditions and accidents might cause immobility in certain parts of the body. In order to assist people in the rehabilitation process, sensors obtaining bio-signals from the body have been merged to create assistive technology. This systematic review is focused on presenting the state-ofthe- art regarding sensor fusion used in applications for hand rehabilitation. Articles were searched in four databases: IEEE Xplore, Web of science, ACM digital library and PubMed. Moreover, PRISMA and QualSyst protocols were employed to filter the articles and assess their qualities. As a result, of the 102 articles initially retrieved, only 29 articles performed sensor fusion for hand rehabilitation. Specifically, three application areas were found: sensor fusion for detecting hand movements, sensor fusion for exoskeleton control applications, and sensor fusion on serious games for hand rehabilitation. The studies included in this review measured two key aspects for rehabilitation purposes: user’s limb strength and user’s limb position. Regarding the sensors employed in the fusion, the most widely used sensor was electromyography sensor followed by inertial sensors. Furthermore, the studies included in this review have focused on the following hand movements used for rehabilitation: flexion, extension, pronation, supination, power grasp, radial/urnal, open hand, single-finger contractions, multi-finger contractions, pinch grip, hand at rest and hand grip.