Although there is increasing interest in using robotic devices to deliver rehabilitation therapy following neurologic injuries, to date, the functional gains associated to robotic rehabilitation are limited. We aim to improve neurorehabilitation by developing new robotic devices and adaptive robotic training strategies that augment or reduce movement errors based on patients’ special needs, age, and characteristics of the trained motor task.
Current Research Projects
Research on neurorehabilitation has emphasized that patients' effort and somatosensory information (i.e., the information about the interaction with the environment) during physical training are crucial to enhance brain plasticity. This project aims to develop a novel clinical-driven and cost-effective upper limb rehabilitation robot to promote simultaneously sensor and motor recovery in neurological patients with a large range of disability levels and different stages of recovery.
In this project, the impact of somatic feedback, such as the weight, geometry and texture of manipulated objects, on the therapy success is investigated. This is done by providing an upper-limb exoskeleton (ARMin) with the necessary haptic abilities, for example simulating carrying a cup of coffee. Finally, the difference between conventional robotic therapy and robotic therapy with haptic rendering is investigated in clinical studies involving therapists and people after stroke.
In this project, we investigate different ways to use virtual reality (VR) in combination with haptic feedback (forces applied by the robot) to improve robotic gait rehabilitation systems. The goal is to make patients more motivated and get them to be more engaged, as well as to design and build on novel rehabilitation paradigms to make these robotic gait rehabilitation systems more effective.
In this project, we aim to develop sensory tasks that train and assess the users' sensory capabilities (i.e., touch discrimination). We employed technics as haptic rendering combined with sensory discrimination methods to recreate a virtual environment that we can use to enhance and evaluate patients' touch discrimination based on psychometrics.