This Small Business Innovation Research (SBIR) Phase I project is developing an integrated computer vision system to objectively measures a person's gait, range of motion and balance and other mobility static and dynamic factors, targeted at fall risk in the elderly. This compact system is cost-effective and easy to use. It tracks people's mobility, identifies problems to correct losses, and provides feedback to motivate the patient to follow their prescribed treatment. The initial focus is the rapidly growing older adult population, who are living longer through advances in medicine, and yet, there are gaps in modern healthcare technologies that prevent elderly people from living independent lives. The project will result in an autonomous intelligent system developed to assess the elderly and others with potential limitations in mobility, to provide comparisons with norms, and to archive test outcomes to allow the subject to see their progress or regress and allow for clinical intervention. The product uses state-of-the-art developments in hardware and software, including existing motion analysis, aerospace technology, mobile telephones and the computer game industry, resulting in a system equipped to follow the motion of a person at a constant scale and quantitatively determine that motion.
The broader impact/commercial potential of this project is in the analysis, rehabilitation and monitoring of mobility issues. The project succinctly responds to priority areas of robotics technology development in the following ways: (1) leveraging improvements in core technologies and algorithms to innovatively yet cost efficiently develop a highly intelligent system capable of making objective, quantitative, "real time" measurements of mobility to replace current subjective testing or time-consuming clinical gait analysis; and (2) using this technology to support and enhance independent living and improve health service delivery to elders and the disabled allowing for more effective treatment protocol. The above broad aims are proven feasible through focused Phase I objectives: (1) connecting the tracking data to articulated human skeletal movement and (2) evaluating key, high-risk components and algorithms using a test article and computer simulation. Phase I will clear barriers to development of an advanced prototype in Phase II--resulting in additional refinements, testing in clinical trials and partnering with a manufacturer in transition to commercialization. The growth plan includes home care applications with the capacity to telemonitor and report to practitioners.
The SBIR NSF Phase I Grant was beneficial in the further development of the MMAS™ (Mobility Monitor Assessment System) platform. It is a marker less integrated computer vision system designed to objectively measure of range of motion, gait, balance and other static and dynamic factors. It tracks people’s mobility, identifies problems to correct losses, and with the use of graphs that are developed to compare with norms that can be given to the person, or sent to a health care professional for intervention. The information is archived for comparison purposes. The objectives that were met were to prove feasibility by mitigating the highest risk in technical elements for successful product commercialization. These goals were met by connecting the tracking data to articulated human skeletal movement and evaluating key, high risk components and algorithms using a test article and computer simulation. Phase I has cleared barriers to allow development of an advanced brass board prototype in Phase II, resulting in additional refinements. Under the leadership of Jack M. Winters, PhD, Professor of Biomedical Engineering at Marquette University, tests will be done to validate precision, robustness of precision and reliability test and evaluation plus a Clinical Application Targeted Pilot Study. The Mobility Monitor fills critical and timely gaps in mobility assessment in the health field. The ability of the MMAS™ to cost-effectively provide quantitative, highly accurate, user friendly data to identify an individual’s abilities and provide detailed, yet understandable reports that can be used over time to comparatively evaluate progress or regress is unique in the industry. Its ability to replace the commonly used subjective process or the expensive and time-consuming clinical gait analysis systems fulfills a significant need in rehabilitative and preventative intervention that is currently unmatched in the market. Our first application will be to address Falls Risk in the older population. With a population of over 40 million people over the age of 65, and a projected Medicare/Medicaid cost projected to be over $240 billion by 2040 in fall-related fractures, the significance of early detection and intervention will have a broad impact on societal outcomes. A second application has equal relevance to broad societal benefits, and that is the early detection of Alzheimer’s with gait analysis. Future applications include concussion in sports, hospital readmission, tele-health rehabilitation, obesity in children, and diseases and injuries that affect mobility. -
