In-Home Video and IMU Kinematics of Self Guided Tasks Correlate with Clinical Bradykinesia Scores

Abstract

Deep brain stimulation (DBS) delivers electrical stimulation directly to brain tissue to treat neurological movement disorders such as Parkinson's Disease (PD). Adaptive DBS (aDBS) is an advancement on DBS that uses symptom-related biomarkers to adjust therapeutic stimulation parameters in real time to improve clinical outcomes and reduce side-effects. A significant challenge for the field of aDBS is developing automated methods to optimize stimulation parameters using remote assessments of symptom severity. To address this challenge, we designed a prototype at-home data collection platform that can remotely update aDBS algorithms and explore objective assessments of motor symptom severity. Our platform collects neural, inertial, and video data, and supports clinician validation of automated symptom assessments. We deployed the system to the home of an individual with PD and collected pilot data across six days. We evaluated motor symptom severity by recording data with stimulation amplitudes set to varying levels during self-guided clinical tasks and free behavior. We assessed movement features including frequency, speed, and peak angular velocity from video-derived pose estimates and inertial data during three clinical tasks. All features showed a reduction during periods of under-stimulation and were significantly correlated with video-based clinical scores of symptom severity (Spearman rank test, $p < 0.006)$. These results demonstrate that our prototype is capable of remote multimodal data collection and that these data can enhance aDBS research outside the clinic.