The overall objective of this SBIR project is to develop a pre-commercial prototype system capable of continuously monitoring involuntary movement disorders from a wide spectrum of neurological conditions. The impact of this innovation will enhance the availability of advanced brain and behavior research tools [PA- 11-134] by providing a continuous means of tracking the presence and severity of movement disorders during normal daily activities. This project will transform our unique movement disorder recognition algorithms into custom software that analyzes movement disorders for specific neurological conditions. The information obtained from body worn sensors will provide an accurate and objective means for assessing the complex and changeable nature of movement disorders. This goal cannot by realized using the current method of self-report questionnaires. The research strategy for Phase I will establish the merit and feasibility of this effort by developing an Application Generation (AG) software platform using a framework of configurable signal processing modules to generate custom applications for movement disorder analysis (Aim 1). This approach reduces the effort and enhances the flexibility of designing and testing software solutions for these applications. The AG Platform will be developed using C++ software to implement signal processing and machine-learning software modules that operate within a knowledge-based framework that we have previously developed.
In Aim 2 we will utilize the AG platform to generate movement disorder analysis software to evaluate a challenging test-case application: freezing-of-gait in Parkinson's disease (PD). The goal is to attain performance metrics for freezing that are comparable to those we have achieved for tremor and dyskinesia in previous efforts. Phase II will refine the capabilities of the AG platform developed in Phase I. We will augment it with the means to automatically design and train the machine learning algorithms, improve the user-interface, and provide options for viewing and summarizing the results. The improved AG platform will be used to develop customized disorder-analysis software that encompasses the full complement of movement disorders associated with PD (e.g. dystonia, bradykinesia, Parkinsonian gait, tremor, dyskinesia), as well as for other neurological condition, such as Essential Tremor (ET). Firmware will be developed for each custom application to efficiently integrate the analytic software with our existing Trigno wireless sensor data acquisition hardware, which needs to be streamlined for this application. This combined system will be evaluated under research use-case scenarios in Neurology. Phase II will deliver an ambulatory Movement Disorder Monitoring system that not only succeeds in providing state-of-the-art monitoring solutions for PD and Essential Tremor, but has proven technology to develop monitoring solutions for a wide variety of neurological conditions. Future development will transfer this technology to a clinical version of this system.
The project is intended to improve the accuracy and reduce the burden of researchers and clinicians when assessing motor outcomes for patients with involuntary movement disorders. The proposed technology will provide a means for evaluating new treatment options, and expedite the delivery of care. The attainment of these goals should increase the effectiveness of research, the time required for these research advancements to reach the consumer, and help control the rising costs of clinical care among the estimated 45 million Americans who have neurological disorders resulting in involuntary movements. The resulting improvements in motor function will ultimately lead to greater independence and productivity for this growing segment of the population.
| Cole, Bryan T; Roy, Serge H; De Luca, Carlo J et al. (2014) Dynamical learning and tracking of tremor and dyskinesia from wearable sensors. IEEE Trans Neural Syst Rehabil Eng 22:982-91 |
| Roy, Serge H; Cole, Bryan T; Gilmore, L Don et al. (2013) High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity. Mov Disord 28:1080-7 |
