sEMG-based Control Strategy for a Hand Exoskeleton System

This book reports on the design and testing of an sEMG-based control strategy for a fully-wearable low-cost hand exoskeleton, implementing an innovative sEMG classifier for predicting the wearer's motor intention and driving the exoskeleton accordingly. It fills a gap in the literature by providing readers with extensive information and a source of inspiration for future design and control of medical and assistive devices.

Format: Paperback / softback
Length: 91 pages
Publication date: 24 November 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into the intricate design and rigorous testing of a groundbreaking SEMG-based control strategy for a fully wearable, low-cost hand exoskeleton. It provides a detailed account of the modifications made to the electronics of a previous prototype, encompassing a novel sEMG classifier designed to accurately predict the wearer's motor intention and subsequently drive the exoskeleton accordingly. While similar classifiers have been extensively employed for motor intention prediction in the past, their application to wearable devices has been relatively overlooked. Thus, this book fills a significant gap in the literature, offering readers a wealth of comprehensive information and serving as a valuable source of inspiration for the future development and control of medical and assistive devices.

The book begins by introducing the background and significance of the hand exoskeleton, highlighting its potential to enhance human capabilities and improve quality of life for individuals with disabilities. It then proceeds to outline the objectives and goals of the research project, which centered on developing an efficient and user-friendly control system for the hand exoskeleton.

Chapter 1 provides an overview of the hand exoskeleton and its components, including the actuators, sensors, and control system. It explains the challenges faced in designing a wearable device that is both comfortable and effective, while also considering factors such as weight, power consumption, and user interface.

Chapter 2 focuses on the modifications made to the electronics of the previous prototype. It describes in detail the implementation of the innovative sEMG classifier, which is responsible for predicting the wearer's motor intention and driving the exoskeleton accordingly. The chapter discusses the various stages of the classifier's development, including feature extraction, training, and validation. It also highlights the challenges encountered during the implementation process, such as dealing with noise and ensuring accurate classification.

Chapter 3 explores the application of the sEMG classifier to wearable device control. It discusses the advantages and disadvantages of using sEMG signals for motor intention prediction and highlights the potential benefits of incorporating them into wearable devices. The chapter also discusses the challenges associated with integrating sEMG signals with other sensory inputs and developing a seamless user interface.

Chapter 4 presents the results of the design and testing of the hand exoskeleton. It describes the experimental setup, the protocols used for data collection, and the evaluation criteria employed to assess the performance of the control system. The chapter discusses the results obtained from the user studies, which demonstrated the effectiveness of the sEMG-based control strategy in enabling the wearer to perform various tasks with increased dexterity and precision.

Chapter 5 concludes the book by summarizing the key findings and recommendations. It emphasizes the importance of interdisciplinary research and collaboration in developing innovative medical and assistive devices. It also suggests future research directions and opportunities for the development of wearable devices that leverage sEMG signals for motor intention prediction and control.

In conclusion, this book provides a valuable contribution to the field of wearable technology and rehabilitation. It showcases the potential of SEMG-based control strategies for enhancing human capabilities and improving the quality of life for individuals with disabilities. The detailed account of the modifications made to the electronics of the hand exoskeleton and the implementation of the innovative sEMG classifier provide readers with a comprehensive understanding of the technical aspects involved in developing wearable devices. The application of sEMG signals to wearable device control offers exciting opportunities for the future development of medical and assistive devices, and the book serves as a valuable resource for researchers, engineers, and practitioners in this field.

Weight: 185g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030902858
Edition number: 1st ed. 2022