The objective is to design, implement, and clinically assess a portable, user-worn, bradykinesia feature extraction system, BradyXplore", for integration with Great Lake Neurotechnologies'(GLN) Kinesia and Kinesia HomeView technology platforms for objective Parkinson's disease (PD) monitoring. There has been tremendous research into PD treatments including pharmaceutical interventions and deep brain stimulation (DBS). While tremor is often the most visible symptom, bradykinesia can be the most impairing. The current standard in evaluating bradykinesia is the Unified Parkinson's Disease Rating Scale, a subjective, qualitative ranking system. Scoring instructions for bradykinesia integrate multiple movement features into a single score that increases variability and limits exploration into if particular bradykinesia features are influenced by specific treatments. If different bradykinesia features were better quantified, novel therapies may be developed to target specific bradykinesia manifestations. The proposed innovations include 1) compact, user-worn motion sensors that can be used with web-based software on a home computer or tablet, 2) algorithms that use kinematic data to rate speed, amplitude, and rhythm independently, and 3) web-based applications for system delivery, patient interaction, and symptom reporting, all of which will promote clinical acceptance and a self- sustaining business model. Wireless motion sensor units containing accelerometers and gyroscopes will be worn on the finger and thumb and collect synchronized motion data. BradyXplore will independently rate speed, amplitude, and rhythm for the standard repetitive motion tasks. Sensor unit batteries will be recharged by an inductive, USB charge pad to eliminate the need for patients to fidget with small connectors. When sensor units are placed on the pad, kinematic data will be transferred to the patient's computer or tablet via a wireless link and then uploaded to our HIPAA-compliant server. In Phase I, our existing motion sensor unit was successfully upgraded to reduce size and improve sensitivity. Algorithms were developed for independently rating speed, amplitude, and rhythm. The algorithms output scores highly correlated to clinical ratings and demonstrated a differential response of speed, amplitude, and rhythm to medication. Phase II will improve ergonomics, sensitivity, data transfer, and reporting. Hardware will be upgraded to include two synchronized sensor units that recharge via an inductive pad and transfer data via a low-power radio. Software will be modified to be completely web-based so patients need not install any software. BradyXplore algorithms will be further investigated in a clinical study to demonstrate its test-retest reliability compared to clinical ratings and determine if speed, amplitude, and rhythm fluctuate differentially ON DBS. We hypothesize that BradyXplore will 1) provide a standardized platform for bradykinesia assessment that objectively quantifies speed, amplitude, and rhythm, 2) receive high clinical acceptance from both clinicians and patients, and 3) aid in development, evaluation, and optimization of therapies such as DBS and pharmaceutical interventions.

Public Health Relevance

Parkinson's disease affects approximately 1.5 million people in the United States causing motor symptoms, of which one of the most debilitating is bradykinesia (slowed movements). Bradykinesia is currently evaluated using a subjective rating scale that gives a single score taking into account speed, amplitude, fatiguing, hesitations, arrests in movement, and how these variables change over time. The proposed BradyXplore bradykinesia feature extraction system will separately quantify specific features of bradykinesia (speed, amplitude, and rhythm) in the home, which should aid in the development of novel therapies to target a patient's specific bradykinesia manifestations.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
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Special Emphasis Panel (ZRG1-ETTN-K (10))
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Fertig, Stephanie
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Great Lakes Neurotechnologies
Valley View
United States
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