Utilizing an Augmented Reality Headset to Accurately Quantify Lower Extremity Function in Parkinson's Disease.

Research overview

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Subjective, imprecise evaluation of lower extremity function hinders the effective treatment of gait impairments in Parkinson's disease (PD). Markerless motion capture (MMC) offers opportunities for integrating objective biomechanical outcomes into clinical practice. However, validation of MMC biomechanical outcomes is necessary for clinical adoption of MMC technologies. This project evaluated the criterion validity of a custom MMC algorithm (CART-MMC) against gold-standard 3D motion capture (Traditional-MC) and its known-groups validity in differentiating PD from healthy controls (HC). Sixty-two individuals with PD and 29 HCs completed a stepping in place paradigm. The trials were recorded by an augmented reality headset with embedded RGB and depth cameras. The CART-MMC algorithm was used to reconstruct a 3D pose model and compute biomechanical measures of lower extremity performance. CART-MMC outcomes were statistically equivalent, within 5% of Traditional-MC, for measures of step count, cadence, duration, height, height asymmetry, and normalized path length. CART-MMC captured significant between-group differences in step height, height variability, height asymmetry, duration variability, and normalized path length. In conclusion, CART-MMC provides valid biomechanical outcomes that characterize important domains of PD lower extremity function. Validated biomechanical evaluation tools present opportunities for tracking subtle changes in disease progression, informing targeted therapy, and monitoring treatment efficacy.

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