A tracking device for a wearable high-DOF passive hand exoskeleton

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Wearable Sensors

Background:

• Exoskeletons offer promising solutions for motor function recovery.
• Accurate, real-time tracking of hand movements is crucial for effective rehabilitation.
• Current home-based therapy often lacks objective performance metrics.

Purpose of the Study:

• To develop and evaluate an exoskeleton-based system for tracking hand and index finger movements.
• To utilize machine learning for estimating joint angles and detecting grip attempts.
• To assess the system's feasibility in healthy individuals and stroke survivors.

Main Methods:

• Development of the HandSOME II exoskeleton with 15 hand degrees of freedom.
• Integration of a magnetometer-based activity tracking device for index finger motion.
• Application of machine learning algorithms to estimate metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joint angles.
• Testing with healthy controls and individuals post-stroke.

Main Results:

• Successful tracking of index finger movement and estimation of MCP and PIP joint angles.
• Detection of grip attempts using machine learning algorithms.
• Feasibility demonstrated in both healthy and stroke populations.

Conclusions:

• The developed exoskeleton and activity tracking system show potential for monitoring hand function during daily activities.
• This technology could enhance stroke rehabilitation by providing objective data for home-based therapy.
• Improved compliance and personalized rehabilitation programs may be facilitated by this approach.