Abstract
Over the past two decades, numerous hand exoskeletons have been developed for rehabilitation scenarios, yet very few have the capability to assess spasticity. This paper introduces a quantitative assessment approach for hand spasticity developed on a cable-driven hand exoskeleton that was specifically designed with spastic hand in mind. The exoskeleton features an adaptive cable-linkage transmission mechanism equipped with bi-directional force transducers, enabling constant-velocity extension and flexion of individual finger joints while simultaneously recording joint torque and angle. Based on the exoskeleton, a multi-layered quantitative assessment approach is proposed to evaluate spasticity on individual finger joints. The basis of the approach is collecting joint resistance information automatically following Modified Tardieu Scale, serving for the computation of six middle-level parameters related to the properties of a spastic joint. The parameters include the range of motion, average resistance
torque, joint stiffness, joint viscosity, catch angle, and reflexinduced resistance torque. The first four mechanical parameters are finally combined into a biomechanical metric, whereas the rest two reflex-related parameters result in a neurological metric, to neatly describe spasticity level. The performance of
the exoskeleton to measure the parameters was tested on six healthy subjects. The assessment approach applied with the developed exoskeleton shows good reliability, repeatability, and validity to capture the features of spastic hands, providing strong evidence for further validation on real patients.
torque, joint stiffness, joint viscosity, catch angle, and reflexinduced resistance torque. The first four mechanical parameters are finally combined into a biomechanical metric, whereas the rest two reflex-related parameters result in a neurological metric, to neatly describe spasticity level. The performance of
the exoskeleton to measure the parameters was tested on six healthy subjects. The assessment approach applied with the developed exoskeleton shows good reliability, repeatability, and validity to capture the features of spastic hands, providing strong evidence for further validation on real patients.
Original language | English |
---|---|
Pages (from-to) | 2335-2342 |
Number of pages | 8 |
Journal | IEEE Robotics and Automation Letters |
Volume | 10 |
Issue number | 3 |
Early online date | 15 Jan 2025 |
DOIs | |
Publication status | E-pub ahead of print - 15 Jan 2025 |
Keywords
- Biomechanical assessment
- Hand Exoskeleton
- Rehabilitation Robotics
- Spasticity
ASJC Scopus subject areas
- Control and Systems Engineering
- Biomedical Engineering
- Human-Computer Interaction
- Mechanical Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Control and Optimization
- Artificial Intelligence