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.