The objective is to design, develop, and clinically validate MyoSense", a clinician worn, high-resolution sensory enhancing prosthetic to quantitatively characterize and distinguish different types of muscle hypertonicity. Development will focus on the growing clinical need to differentiate dystonia from spasticity in children affected by cerebral palsy (CP) and other mixed or secondary dystonias. Children with CP often suffer from mixed motor disorders including spasticity, muscle weakness, ataxia, athetosis and dystonia causing severe functional impairment and limiting activities of daily living. Additionally these coexisting motor manifestations can occur in different parts of the body based on brain injury topology. Spasticity and dystonia are both currently measured clinically using subjective, ordinal and non-interval rating scales, thereby limiting applications of statistical techniques in any analysis. Currently, quantitative measures are not widely used although recent studies suggest biomechanical features can distinguish different types of hypertonicity. Distinct pharmacologic and surgical interventions exist for different neurological findings, motor signs, and movements observed for children with CP;therefore, quantitative assessment could better guide clinical judgments for treatments. Consequences for selecting invasive treatments for the incorrect diagnosis can have significant, long term consequences. Additionally, a general, quantitative assessment system for examining muscle tone should have important applications in several other movement disorders including Parkinson's disease, stroke, and general rehabilitation. The MyoSense system will provide a compact, user worn prosthetic instrumented with kinetic and force sensors and integrate real-time software feedback to guide a standardized and quantitative motor examination. The specific innovation lies in four areas. First, instead of instrumenting a patient, the sensory enhancing prosthetic is worn by the clinician making the device is highly adaptable to measure hypertonia from a wide range of joint sets in a variety of patient and conditions. Second, the integration of multi-modal sensors in a wireless glove measures real-time joint position and velocity while simultaneously measuring forces required to move the body part independent of gravity to help distinguish spasticity from dystonia. Third, real-time software display feedback will guide the clinician motion assessment to standardize and quantify features. Finally, intelligent algorithms will process the data and extract features t classify spasticity and dystonia. Phase I will utilize an existing motion sensing hardware platform with the integration of additional sensor modalities to demonstrate feasibility of capturing and quantifying features of spasticity and dystonia. The hardware will be modified to minimize patient and clinician burden by optimizing sensor type and count and embedding the system into a clinician worn glove. A software interface will be developed to collect data during clinical studies and provide real-time feedback for an evaluation. Finally, the prototype system will be evaluated in clinical feasibility study.

Public Health Relevance

We will design, develop, and clinically validate MyoSense", a clinician worn, high-resolution sensory enhancing prosthetic to quantitatively characterize and distinguish different types of muscle hypertonicity, specifically differentiating dystonia from spasticity in children affected by cerebral palsy (CP) and other mixed or secondary dystonias. Spasticity and dystonia are treated differently and are both currently measured clinically using subjective, ordinal and non-interval rating scales and as a result selecting the incorrect treatment can have significant, long lasting implications for children with CP. The MyoSense system will provide a compact, clinician worn glove instrumented with kinetic and force sensors and integrate real-time software feedback to guide a standardized and quantitative motor examination so that clinicians can appropriately prescribe treatments that will help improve patient quality of life.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
1R43NS076052-01A1
Application #
8314727
Study Section
Special Emphasis Panel (ZRG1-MOSS-F (15))
Program Officer
Fertig, Stephanie
Project Start
2012-09-30
Project End
2014-08-31
Budget Start
2012-09-30
Budget End
2014-08-31
Support Year
1
Fiscal Year
2012
Total Cost
$242,968
Indirect Cost
Name
Great Lakes Neurotechnologies
Department
Type
DUNS #
965540359
City
Valley View
State
OH
Country
United States
Zip Code
44125