Eleventh Annual Grant Winners 2010-2011
Title: Functional Assessment Using Telemetry Accelerometers and Mobile Device
Faculty and Students:
Rick Davis PA-C, Ed.D. (HPD-ALL)
Jennifer Canbek, PhD, PT (HPD-ALL)
Mary Blackinton, EdD, PT (HPD-ALL)
Ming-Shun Samuel Cheng, ScD, PT, MS (HPD-ALL)
Cheryl Hill, PhD, PT (HPD-ALL)
Kristi Brevick, BS (HPD-ALL)
Purpose: The purpose of this study is to explore the feasibility of using a telemetry accelerometers (ACCs) system integrated with a mobile device to measure balance function and to examine the accuracy of such a system compared to direct measurement. Background and Significance: Maintaining balance in the upright position is a complex motor control task for the central nervous system. Many diseases, injuries, and even the normal aging process can impair the body's ability to maintain balance. Unfortunately, current balance assessment techniques and tools could be cumbersome and time consuming to use for the busy clinician. Developing a telemetry sensor system that can capture the physical condition of a patient and transmit the information to a mobile device for processing and documentation will greatly enhance the efficiency of clinicians when assessing balance. Ultimately patients will benefit from such a system from receiving better and adequate training to regain their balance function. Methods: This is a developmental and exploratory study. The first phase of the project is to develop the telemetry system with a triaxial accelerometer sensor. Once the system is fabricated and the prototype software has been written and tested, the data collection will start. Thirty subjects, male and female, age between 20 and 65 will be recruited. Each subject will perform 3 trials of 7 different balance and functional tests. The data from the accelerometer will be captured on a mobile device, downloaded to a computer, and correlated with clinical measurements in order to develop and validate algorithms for calculating outcome measures that are commonly used to reflect an individual's balance function.
Data analysis: The algorithms will be developed using the inherent features of the ACCs and mathematical models. Accuracy of the algorithm for each of the 7 tests will be assessed using Intraclass Correlation Coefficient (ICC) analysis.