An Underactuated Wearable Robotic Glove Driven by Myoelectric Control Input / Uma luva robótica vestível subatuada acionada por entrada de controle mioelétrico

People who have hand impairments caused by the most common neurological and degenerative musculoskeletal diseases face trouble to achieve their everyday tasks. To try to help these people, in this works is presented a prototype of a glove-like orthosis for upper limbs. It is an underactuated robotic glove controlled by myoeletric input signals collected by the Myo armband. The development and its details are all described so that it can be reproduced with improvements and used as an assistive device. To the authors' knowledge, in Brazil, there is no one similar orthosis such this one developed thus far

Fully embedded myoelectric control for a wearable robotic hand orthosis

To prevent learned non-use of the affected hand in chronic stroke survivors, rehabilitative training should be continued after discharge from the hospital. Robotic hand orthoses are a promising approach for home rehabilitation. When combined with intuitive control based on electromyography, the therapy outcome can be improved. However, such systems often require extensive cabling, experience in electrode placement and connection to external computers. This paper presents the framework for a stand-alone, fully wearable and real-time myoelectric intention detection system based on the Myo armband. The hard and software for real-time gesture classification were developed and combined with a routine to train and customize the classifier, leading to a unique ease of use. The system including training of the classifier can be set up within less than one minute. Results demonstrated that: (1) the proposed algorithm can classify five gestures with an accuracy of 98%, (2) the final system can online classify three gestures with an accuracy of 94.3% and, in a preliminary test, (3) classify three gestures from data acquired from mildly to severely impaired stroke survivors with an accuracy of over 78.8%. These results highlight the potential of the presented system for electromyography-based intention detection for stroke survivors and, with the integration of the system into a robotic hand orthosis, the potential for a wearable platform for all day robot-assisted home rehabilitation