Robotic pilot study for analysing spasticity: clinical data versus healthy controls.

Area of Science:

• Neurology
• Robotics
• Biomedical Engineering

Background:

• Spasticity is a motor disorder causing significant disability with no universally accepted definition or definitive assessment parameters.
• Accurate spasticity evaluation is crucial for determining recovery stages and guiding treatment effectiveness.
• Current assessment methods lack objective quantification, necessitating novel approaches.

Purpose of the Study:

• To present a novel robotic system for the quantitative assessment of spasticity.
• To validate the system's ability to measure velocity-dependent resistance, a key characteristic of spasticity.
• To compare classification algorithms for distinguishing between healthy individuals and those with acquired brain injury.

Main Methods:

• A cross-sectional robotic pilot study involving 40 individuals with acquired brain injury (ABI) and 45 healthy controls.
• Collection of force and position data during movements mimicking the Modified Ashworth Scale (MAS) in the sagittal plane.
• Application of linear regression for validation, and comparison of Dynamic Time Warping with k-nearest neighbour (DTW-KNN) against linear discriminant analysis (LDA) for classification.

Main Results:

• The robotic system successfully detected velocity-dependent resistance (p<0.05), confirming its validity.
• Significant differences in measured parameters were observed between healthy individuals and those with MAS 0.
• DTW-KNN classification improved the differentiation between healthy and patient groups by approximately 20% compared to LDA.

Conclusions:

• Quantitative spasticity evaluation using this robotic system can differentiate between healthy individuals and those with even mild spasticity (MAS 0).
• The time-series analysis of position and force data provides an accurate predictor of patient health status.
• This novel approach offers objective and reliable spasticity assessment, aiding in clinical decision-making.