Innovative Robotic Technology Enhances Quantitative Assessment of Upper Limb Spasticity

A revolutionary advancement in diagnosing upper limb (UL) spasticity is emerging, shifting away from traditional manual evaluations by clinicians. Researchers from UNIST have developed a sophisticated robotic system that accurately measures spasticity levels, offering a promising tool for improving diagnosis, optimizing personalized rehabilitation, and establishing standardized assessment procedures.

Led by Professor Sang Hoon Kang, the team has introduced an innovative method to objectively quantify spasticity by applying subtle forces to a patient's arm and analyzing the resulting movement responses. This approach allows for quick, precise evaluations that can be performed even by non-expert practitioners, potentially transforming clinical practices and aiding tailored treatment plans.

The researchers tested their method using a 2-degree-of-freedom (2-DOF) direct-drive robotic system. Their experiments revealed that residual joint frictions within the robotic system, even in highly advanced devices like MIT-MANUS, could influence measurement accuracy. These frictional forces contribute to the nonlinear responses observed during assessments, which previously were attributed solely to the human limb's nonlinear properties.

To mitigate this issue, the team implemented an Internal Model Based Impedance Control (IMBIC) strategy that effectively compensated for the robot's internal nonlinear friction. This adjustment resulted in more linear robotic movements, significantly enhancing the reliability of spasticity measurements.

Seongil Hwang, the study's lead author, explained, "Our findings show that correcting for robotic friction dramatically improves measurement accuracy and consistency. This enables more precise evaluation of spasticity, which is vital for effective rehabilitation planning."

Historically, clinicians have relied on subjective tactile assessments to gauge spasticity, which vary according to the examiner's experience and are difficult to quantify across different joints and movements. The new robotic approach offers an objective, reproducible alternative that can standardize assessments across clinical settings.

Professor Kang emphasized the potential for this technology to aid in the development of more effective rehabilitation strategies and the establishment of consistent evaluation metrics. The team plans to collaborate with UNIST’s upcoming public hospital facilities to apply this technology in real-world clinical environments.

Published in IEEE Transactions on Neural Systems and Rehabilitation Engineering, this study underscores the significance of addressing robotic system nonlinearities to improve the accuracy of neuromotor assessments, promising a new era of precision in managing upper limb spasticity.