sEMG-Controlled Soft Exo-Glove for Assistive Rehabilitation Therapies

• Published in: IEEE access Vol. 12; pp. 43506 - 43518
• Main Authors: Copaci, Dorin, Cerro, David Serrano Del, Guadalupe, Janeth Arias, Lorente, Luis Moreno, Rojas, Dolores Blanco
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Algorithms; Assistive rehabilitation; Assistive technologies; Classification algorithms; Classifiers; Control algorithms; Control theory; Electromyography; Exoskeletons; Feature extraction; gesture classification; Gesture recognition; Hand (anatomy); Human motion; Muscles; Patient rehabilitation; Rehabilitation; Shape memory alloys; soft exo-glove; Soft robotics; Support vector machines; Task analysis; Tendons; Thumb; User requirements
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3380469

The movement of the human hand offers various degrees of freedom, enabling efficient performance of dynamic tasks and robust interaction with the environment in a compliant and continuous manner. However, the rigid exoskeleton used in hand rehabilitation limits the user's freedom of movement, complicating their natural interaction with the environment. In this study, we present a soft exo-glove for assistive rehabilitation actuated by Shape Memory Alloys (SMA), controlled by a surface electromyography (sEMG) hand gesture classifier. Thanks to the actuator type, the soft exo-glove enables slow, smooth, and controlled movements when activated and provides complete control transparency when the device is not active. This advantage enhances the comfort and acceptance of the exo-glove by the patient. On the other hand, the classifier, in conjunction with the control algorithm and the soft exo-glove, offers the potential to use the exo-glove in assistive hand rehabilitation therapy. For user-friendly use, an interface has been developed, enabling the acquisition of new sEMG data from new users, retraining of the classifier, and connection with the soft exo-glove for rehabilitation therapy. The main objective of this study is to demonstrate that the proposed wearable soft exo-glove, along with the control algorithm and the employed classifier, constitutes an effective solution for assistive rehabilitation tasks, as demonstrated with healthy subjects. Furthermore, this solution can be easily adapted to the users' characteristics and requirements.