A NOVEL DESIGN OF A ROBOTIC GLOVE SYSTEM FOR PATIENTS WITH BRACHIAL PLEXUS INJURIES.

Area of Science:

• Biomedical Engineering
• Rehabilitation Robotics
• Assistive Technology

Background:

• Brachial plexus injuries significantly impair hand grasping functionality, impacting daily living.
• Existing assistive devices often lack the dexterity and portability required for comprehensive rehabilitation.
• Restoring independent grasping is a critical goal for improving quality of life after nerve injury.

Purpose of the Study:

• To design and present a novel exoskeleton glove system for individuals with brachial plexus injuries.
• To restore lost grasping functionality through a portable and lightweight robotic solution.
• To lay the groundwork for intelligent control algorithms for autonomous grasping.

Main Methods:

• Development of a robotic glove system featuring Linear Series Elastic Actuators (SEA) and Rotary SEA.
• Integration of optimized finger linkages for individual finger motion and coupled hand-wrist movement.
• Inclusion of adjustable features for various hand sizes and user comfort (e.g., finger abduction/adduction, thumb flexion).
• Incorporation of sensors to measure fingertip contact force and finger bending angles.
• Design emphasizes portability with forearm-integrated micro-controllers and batteries.

Main Results:

• The exoskeleton glove system is designed to be lightweight and portable for activities of daily living.
• The system incorporates Series Elastic Actuators (SEA) and optimized linkages for dexterous finger and wrist motion.
• Adjustable features and sensor integration are included for enhanced user comfort and future control development.
• The design facilitates a completely portable solution by integrating electronics on the forearm.

Conclusions:

• The designed exoskeleton glove offers a promising solution for restoring grasping function in brachial plexus injury patients.
• The system's portability, adjustability, and integrated sensing capabilities support its potential for effective rehabilitation.
• Further development incorporating intelligent control algorithms based on measured data could lead to autonomous grasping capabilities.