sEMG-based control strategy for a Hand Exoskeleton System

Data from the latest World Health Organization estimates paint a picture where one-seventh of the world population needs at least one assistive device. At the same time, clinical facilities are more and more overcrowded. On one side, due to the aging of the population, on the other side, thanks to the advances in medicine for which pathologies once certainly deadly, nowadays present a low mortality rate. However, only a small percentage of those who need an assistive or rehabilitative aid can get it properly because the healthcare system is globally under heavy pressure. The early 2000s are also characterized by a marked technological drive which, starting in the middle of the twentieth century, took the name of the Fourth Industrial Revolution. Increasingly smaller processors deliver ever higher computing power, production processes are optimized, data transmission reaches impressive speeds, and technology becomes more and more accessible. In this terrain, robotics is making its way through more and more aspects of everyday life, and robotics-based assistance or rehabilitation to physically impaired people are considered two of the most promising applications of this widely investigated technology. Providing high-intensity rehabilitative sessions or home assistance through low-cost robotic devices can be an active solution to democratize some services otherwise not accessible. Simultaneously, it will also contribute to lower the burden over the healthcare system. The work presented in this thesis has aimed to tackle the topic mentioned above by developing an innovative control strategy to be implemented on a low-cost hand exoskeleton system to support people suffering from hand disabilities during the activities of daily living. Most of the independence in everyday life is due in fact to the activities carried out using the hands; this is why restoring their dexterity when pathologically lost, is vitally important. This work has been conducted starting from the solutions available within state of the art and following the main trends, heading to the development of an intuitive and easy to manage control strategy based on the intention recognition from surface electromyographic signals. Exploiting epidermal measurements of the myoelectric activity lends itself well to research activities as it is a noninvasive procedure and therefore is widely studied in the literature, including in the eld of control of robotic devices. The application of these techniques to the real control of robotic devices is rarely addressed, however, and only a few cases are reported in the literature. The main contribution of this activity is hence not only to propose a novel control strategy but also to provide a detailed explanation of its implementation into a real device. The performance of the resulting systems has been tested enrolling a patient suffering from spinal muscular atrophy in a pilot study.