Utilizing kinematic analysis of postural instability as an objective measure to aid in distinguishing between normal pressure hydrocephalus and Parkinson's disease.

Objective: Patients with normal pressure hydrocephalus (NPH) and Parkinson's Disease (PD) can clinically appear quite similar at baseline evaluation. We sought to investigate the use of kinematic assessment of postural instability (PI) using inertial measurement units (IMUs) as a mechanism of differentiation between the two disease processes. Methods: 20 patients with NPH, 55 patients with PD, and 56 age-matched, healthy controls underwent quantitative pull test examinations while wearing IMUs at baseline. Center of mass and foot position data were used to compare velocity and acceleration profiles, pull test step length, and reaction times between groups and as a function of Unified Parkinson's disease Rating Scale Pull Test (UPDRSPT) score. Results: Overall, the reactive postural response of NPH patients was characterized by slower reaction times and smaller steps compared to both PD patients and healthy controls. However, when patients were grouped by UPDRSPT scores, no reliable objective difference between groups was detected. Conclusion: At their initial evaluation, very few NPH patients demonstrate "normal" or "mild" PI as they appear to be older upon presentation compared to PD patients. As a result, kinematic assessment utilizing IMUs may not be helpful for differentiating between NPH and PD as a function of UPDRSPT score, but rather as a more fine-tuned method to define disease progression. We emphasize the need for further evaluation of incorporating objective kinematic data collection as a way to evaluate PI and improve patient outcomes.

Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients.

The use of wearable sensors in movement disorder patients such as Parkinson's disease (PD) and normal pressure hydrocephalus (NPH) is becoming more widespread, but most studies are limited to characterizing general aspects of mobility using smartphones. There is a need to accurately identify specific activities at home in order to properly evaluate gait and balance at home, where most falls occur. We developed an activity recognition algorithm to classify multiple daily living activities including high fall risk activities such as sit to stand transfers, turns and near-falls using data from 5 inertial sensors placed on the chest, upper-legs and lower-legs of the subjects. The algorithm is then verified with ground truth by collecting video footage of our patients wearing the sensors at home. Our activity recognition algorithm showed >95% sensitivity in detection of activities. Extracted features from our home monitoring system showed significantly better correlation (~69%) with prospectively measured fall frequency of our subjects compared to the standard clinical tests (~30%) or other quantitative gait metrics used in past studies when attempting to predict future falls over 1 year of prospective follow-up. Although detecting near-falls at home is difficult, our proposed model suggests that near-fall frequency is the most predictive criterion in fall detection through correlation analysis and fitting regression models.