TY - GEN
T1 - Instrumented Upper-Body Brace for Computerized Training of Muscle Control
AU - Vataksi, Linda
AU - Sanford, Sean
AU - Liu, Mingxiao
AU - Nataraj, Raviraj
N1 - Publisher Copyright: © 2022, Avestia Publishing. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Each year in the United States, over 2,000,000 individuals suffer from neuromuscular disorders that severely impair movement abilities. Physical therapy is the predominant option for rehabilitating motor function for these patients; however, traditional therapies often focus on physical training without greater cognitive engagement or leveraging of motor learning principles. As such, computerized interfaces for rehabilitation, such as virtual reality and robotics, are more promising given their natural appr oaches to motivate and provide enhanced feedback about performance while re-training motor skills. Our laboratory has prototyped an upper-extremity brace device integrated with a virtual reality environment for isometric training of improved muscle-level control of the upper-body for persons with motor disability. This research platform includes a position-adjustable restrictive upper-extremity brace instrumented with sensors for skin-surface electromyography (EMG, measure muscle activity) to control virtual avatars and vibration motors for haptic guidance cues during training. The core objective of this research is to adapt the current brace design to better include instrumentation elements (EMG sensors, vibration motors) onboard the brace towards an embodiment of this device that is self-contained and with greater commercial potential. Specifically, this project will focus on building the next version of this brace system that allows for custom-placement of affordable (not high-end research-grade) commercial EMG (Myoware) sensors at locations personally fitted to each participant. The Myoware sensors will be embedded onto the current upper-body restrictive brace through modular attachments based on designs developed in SolidWorks as presented in this paper. The SolidWorks design utilizes sliding mechanisms, screws, springs, and clamps to make the modular attachment more user-friendly and position adaptable in three dimensions. Embedding Myoware sensors onto the brace design replaces the need to tape research-grade (Delsys) sensors onto each participant to ensure flush and consistent contact with each participant arm for robust EMG measurements and reliable transfer of haptic feedback. Overall, these improved design implementations will result in a version of this device that is more affordable, easier to use, more customizable to each user, and facilitates greater portability. The potential customers and stakeholders would include not only patients, but also clinical support staff and telehealth companies. This versatile, advanced system for computerized rehabilitation will be valuable to any communities of neuromuscular disorders affecting upper-body function that benefit from motor rehabilitation.
AB - Each year in the United States, over 2,000,000 individuals suffer from neuromuscular disorders that severely impair movement abilities. Physical therapy is the predominant option for rehabilitating motor function for these patients; however, traditional therapies often focus on physical training without greater cognitive engagement or leveraging of motor learning principles. As such, computerized interfaces for rehabilitation, such as virtual reality and robotics, are more promising given their natural appr oaches to motivate and provide enhanced feedback about performance while re-training motor skills. Our laboratory has prototyped an upper-extremity brace device integrated with a virtual reality environment for isometric training of improved muscle-level control of the upper-body for persons with motor disability. This research platform includes a position-adjustable restrictive upper-extremity brace instrumented with sensors for skin-surface electromyography (EMG, measure muscle activity) to control virtual avatars and vibration motors for haptic guidance cues during training. The core objective of this research is to adapt the current brace design to better include instrumentation elements (EMG sensors, vibration motors) onboard the brace towards an embodiment of this device that is self-contained and with greater commercial potential. Specifically, this project will focus on building the next version of this brace system that allows for custom-placement of affordable (not high-end research-grade) commercial EMG (Myoware) sensors at locations personally fitted to each participant. The Myoware sensors will be embedded onto the current upper-body restrictive brace through modular attachments based on designs developed in SolidWorks as presented in this paper. The SolidWorks design utilizes sliding mechanisms, screws, springs, and clamps to make the modular attachment more user-friendly and position adaptable in three dimensions. Embedding Myoware sensors onto the brace design replaces the need to tape research-grade (Delsys) sensors onto each participant to ensure flush and consistent contact with each participant arm for robust EMG measurements and reliable transfer of haptic feedback. Overall, these improved design implementations will result in a version of this device that is more affordable, easier to use, more customizable to each user, and facilitates greater portability. The potential customers and stakeholders would include not only patients, but also clinical support staff and telehealth companies. This versatile, advanced system for computerized rehabilitation will be valuable to any communities of neuromuscular disorders affecting upper-body function that benefit from motor rehabilitation.
KW - Myoware sensors
KW - computerized training
KW - motor rehabilitation
KW - neuromuscular disorders
KW - upper-body restrictive brace
UR - http://www.scopus.com/inward/record.url?scp=85141886845&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141886845&partnerID=8YFLogxK
U2 - 10.11159/icbes22.132
DO - 10.11159/icbes22.132
M3 - Conference contribution
SN - 9781990800092
T3 - Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science
BT - Proceedings of the 8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022
A2 - Benedicenti, Luigi
A2 - Liu, Zheng
PB - Avestia Publishing
T2 - 8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022
Y2 - 28 July 2022 through 30 July 2022
ER -