Collecting and evaluating data from multiple devices, including wearable sensors, and comparing them to standard lab-based instruments across multiple domains of daily tasks.

Development of a hierarchical vision-based behavior phenotyping method for classification of basic human actions in video recordings performed using a single RGB camera.

A primer on digital medicine aiming to provide an introduction to core concepts and terms that define the field. Specifically, contrasting “clinical research” versus routine “clinical care,” outlining the security, ethical, regulatory, and legal issues developers must consider as digital medicine products go to market.

Presenting data collected for the BlueSky Project which aims to develop novel assessments of motor and non-motor function using mobile and wearable technology. Found that multi-day studies using wearable sensors, even those requiring long laboratory data collections, are feasible in patients with PD. The current project has yielded large amounts of clinically relevant data that will enable the development of robust algorithms for the estimation of motor and non-motor symptom severity.

Evaluating the performance of wearable inertial sensor technology in quantifying Parkinson Disease (PD) signs. The Mobility Lab System 2-minute walk test generated endpoints with good reliability and detected changes in PD patients before and after levodopa.

Development of an analytical framework that leverages data from wearable sensors to predict MDS-UPDRS-III item scores. This approach may enable frequent home-based assessments of PD motor function and more precise quantification of medication effects.