Verification of Finger Joint Stiffness Estimation Method With Soft Robotic Actuator

Area of Science:

• Biomedical Engineering
• Rehabilitation Science
• Neurology

Background:

• Stroke frequently causes upper extremity spasticity, leading to disability.
• Quantifying finger joint stiffness is crucial for assessing impairment post-stroke.
• Existing methods for stiffness measurement require validation.

Purpose of the Study:

• To compare passive metacarpophalangeal (MCP) joint stiffness measurements using a soft-elastic composite actuator (SECA) with a standalone mechatronics device.
• To establish the compatibility of SECA-facilitated stiffness estimation with standard procedures.
• To correlate stiffness measurements with clinical spasticity scores.

Main Methods:

• Developed a standalone mechatronics device to measure MCP joint torque and angle.
• Utilized a soft-elastic composite actuator (SECA) to estimate passive MCP joint stiffness.
• Compared SECA-derived stiffness with device measurements and Modified Ashworth Scale (MAS) scores.

Main Results:

• Demonstrated a correlation between SECA-based stiffness estimation and the standalone mechatronics device.
• Results from the SECA-Finger model showed compatibility with established stiffness quantification.
• Findings suggest SECA's potential for objective spasticity assessment.

Conclusions:

• Passive MCP joint stiffness can be quantified using soft robotic actuators.
• This method offers a potential tool for objective spasticity assessment in hand rehabilitation.
• SECA-based quantification may improve clinical evaluation of post-stroke impairment.