The objective is to design, build, and clinically assess Kinesia-HS, an integrated solution to facilitate pharmaceutical development of neuroprotective interventions targeted to Parkinson's disease (PD). The system will include both compact patient-worn instrumentation and web-based infrastructure for home monitoring to provide significantly increased motor symptom resolution in both amplitude and time. There has been tremendous growth and active research into neuroprotective treatments designed to slow the progression PD. Treatment efficacy is judged by the rate at which patient symptoms deteriorate over time. The current standard in evaluating PD motor symptoms is the Unified Parkinson's Disease Rating Scale (UPDRS), a ranking system in which clinicians must be present to provide a subjective integer score to document symptom severity. The discrete nature of the UPDRS renders it profoundly inadequate for measuring the rate of deterioration of motor symptoms in a neuroprotective drug study. These drugs target patients with early PD, when symptoms are barely noticeable. It often takes years or even decades before the discrete UPDRS can detect a significant change in the rate of decline of motor symptom severity. The primary innovations of the proposed system include utilizing DBS in a clinical study to simulate disease progression, using high speed video as the gold standard linked directly back to the UPDRS for sensitivity validation, and a standardized, web-based infrastructure to improve the efficiency of clinical drug trials. We will leverage CleveMed's previously developed Kinesia system, a compact wireless system to quantify PD motor symptoms, which includes user worn motion sensors and interactive software to automate a patient exam. In two large clinical studies, the motion sensing technology successfully demonstrated objective quantification of PD motor symptoms with high correlations to clinical UPDRS motor scores. While previously existing technology will be leveraged to speed development and increase likelihood of project success, significant novel software development, system integration, and evaluation is required for the pharmaceutical application. In order to validate the quantification of very slight changes in symptom severity, the existing algorithms for quantifying tremor and bradykinesia severities will be tested against high-speed, calibrated videos that give precise measures of hand movements. In addition to highly sensitive instrumentation for monitoring PD symptoms in the home, a primary innovation of the proposed system is the infrastructure backbone to enable the straightforward integration of Kinesia-HS outputs into standardized electronic data capture software currently used in clinical trials. This standardized platform for objective home assessments could lead to clinically significant results faster and with improved resolution compared to traditional methods, which could enable breakthrough therapies to get to market faster and lower developmental costs.

Public Health Relevance

Pharmaceutical companies are placing great emphasis on neuroprotective agents designed to slow the progression of Parkinson's disease. The current standard for evaluating motor symptoms in response to therapy is a subjective, integer rating scale that does not provide the resolution necessary to measure the rate at which motor symptoms change during disease progression. The proposed system will include both compact patient-worn instrumentation and web-based infrastructure for home monitoring to provide significantly increased motor symptom resolution in both amplitude and time and easy integration into clinical drug trials to speed the development of PD interventions.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-ETTN-K (10))
Program Officer
Fertig, Stephanie
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Great Lakes Neurotechnologies
Valley View
United States
Zip Code
Heldman, Dustin A; Espay, Alberto J; LeWitt, Peter A et al. (2014) Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson's disease. Parkinsonism Relat Disord 20:590-5