The Ohio State University Spine Research Institute (SRI) is a pioneer in adapting a variety of innovative technologies into the medical market. They run the worlds largest motion capture facility of it’s kind and have created a variety of highly sensitive monitoring devices. Their goal is to find better ways to understand, categorize, and assist in treating spine and neck injuries. They worked with Switchbox to assist them with developing a best in class software application to process medical data.
Despite years of research and medical study there is an incredibly low level of objective metrics to quantify neck and back disorders. This lack of reliable metrics makes it difficult to efficiently diagnose, monitor, and treat individual patients suffering from neck pain, and creates challenges for employers aiming to design and evaluate the effectiveness of occupational interventions or training programs. The purpose of this hardware and software prototype was to develop and test the feasibility of a medical materiel solution that uses wearable inertial measurement unit sensors to dynamically assess cervical spine motions and produce functional metrics of neck health. These metrics can then be provided to health care professionals for proper diagnosis and treatment.
Switchbox assisted in the development of a digital health prototype that uses wearable motion sensors to provide more actionable neck health metrics.
The prototype was developed and tested for accuracy, reliability and usability. Features that allow users to capture and use additional pertinent meta-data through digital forms or questionnaires were also integrated. In addition, mounting platform prototypes were designed to ensure quick and reliable sensor placement, enable rapid placement on human subjects of varying heights and body shapes, and are light, comfortable, flexible, easy to clean, easy to use, and easily adaptable for scalable manufacturing in the near future.
The prototype can capture, store, analyze, and intuitively present cervical spine motion data to assist health care professionals in providing a treatment plan. Advanced reporting was built based on the metrics provided by the wearable technology created. Doctors and other medical staff were interviewed to determine the best way to capture and analyze this data.
The final solution used a custom IoT device to collect advanced motion, a Windows PC application to interface with the device and pull data, a custom cloud based API to allow the PCs to upload data, a cloud based data processing engine to analyze and sort data, and a custom web application to allow users to authenticate, manage patients, and view reports and charts.
The Ohio State University SRI and Switchbox passed all requirements of the DoD Phase 1 grant and were selected to move to the Phase II portion of the project which received 10 times the original funding. Phase II efforts will focus on evolving the developed prototype into an enterprise-quality platform and developing actionable metrics for informing customer decision-making.
The views, opinions and/or findings contained in this case study are those of the author(s) and should not be construed as an official Department of the Army position, policy or decision unless so designated by other documentation.
The views, opinions and/or findings contained in this case study are those of the author(s) and should not be construed as an official The Ohio State University position, policy or decision unless so designated by other documentation, and is not endorsed or recommended by Ohio State.
