Development of an upper limb tele-rehabilitation home robotic device for post-stroke patients

Abstract

Stroke is currently the second most frequent cause of death after coronary artery disease and its prevalence is increasing at an alarming rate. Hemiparesis is the most common outcome of stroke leading to movement deficiency. Fortunately, rehabilitation can help hemiparetic patients to learn new ways of using and moving their weak arms and legs. It is also possible with immediate therapy that people who suffer from hemiparesis may eventually regain movement. Although there are several approaches, extensive task-specific repetitive movement is one of the safest and most effective methods to regain lost mobility of the affected limbs. This therapy requires incessant medical care and intensive rehabilitation often requiring one-on-one manual interaction with the physical therapist. Robotic rehabilitation for post-stroke therapies is an emerging new domain of application for robotics with proven success stories and clinical studies. New robotic devices and software applications are hitting the market intending to assist specialists carrying out physical therapies and even allowing patients exercising at home. Rehabilitation robots are designed to assist patients performing repetitive movements for a long time irrespective of skills and fatigue compared to manual therapy. A successful robotic device for rehabilitation demands high workspace and force feedback capabilities similar to a human physiotherapist. Currently, there are several devices in the market that give a robotic solution to these repetitive movements, and have been installed in many hospitals around the world. However, features mentioned are usually achieved at the expense of other important requirements such as transparency and backdrivability, degrading the overall human-machine interaction experience. Mechanically, this implies developing robots with high workspace and force feedback features. Such systems have in turn the drawback of being bulky and heavy degrading final interaction experience with the patient. Due to these facts, a new home robotic tele-rehabilitation device is presented in this thesis. The proposed solution is a mechatronic device capable of rehabilitating patients at their homes while they interact with personal computer games connected to the robot. The proposed system also allows monitoring the user performance for optimal therapy design. The second part of this thesis proposes a novel active gravity compensation technique based on machine learning that can highly improve the performance of mechatronic systems used for rehabilitation and many other domains of robotic applications. Traditional algorithms to obtain active gravity compensation usually require the static equilibrium equations of the system. However, for complex mechatronic configurations, solving these equations is not straightforward. The use of machine learning methods can achieve gravity compensation without the need to solve the equilibrium equations. The proposed novel technique is validated in the developed tele-rehabilitation device. The third part of this thesis focuses on improving the safety of this type of haptic devices through further evaluating the stability of haptic rendering. The theoretical and experimental implications of rendering virtual stiffness, damping and inertia is thoroughly evaluated.