Description of Project
A customized VR training module with smart sensors provides a Concussion Avoidance Training system for a DoD-funded study of mTBI.
Dr. Chiming Huang, University of Missouri Kansas City
VR: Unity, Oculus Quest 2
Sensors: Arduino, Java, Python
Video Explaining the Project
Dr. Chi-ming Huang’s primary aim was to devise and design Concussion Avoidance Training (CAT) protocols for a research project funded by the Department of Defense. Dr. Huang specifically examined the role of neck stiffness in mTBI and mTBI mitigation and theorized that by undergoing training to tense neck muscles before a moment of impact, individuals could sustain fewer TBIs from impact events. To validate this hypothesis, Dr. Huang needed a non-injurious, immersive way of training participants to stiffen their necks prior to impact, a streamlined and accurate method of collecting biometric data on neck stiffness and impact in the field, and a means of gathering research insights on the training and field data.
Research that depends on human participants requires review from the IRB, the administrative body responsible for reviewing and approving all research activities within an organization. The initial challenge was to design an experimental model that had fewest barriers to IRB acceptance. Overall, the key needs of the research project that needed solutions were:
- a non-injurious, cost-effective training solution
- a centralized repository of training data
- sensors small enough to wear during game time and large enough to contain a full game time of PRY data
illumisoft developers built out three components to meet these three technical needs:
1. A Virtual Reality Training Module
The illumisoft team responded to the need for a non-injurious training solution by leveraging Oculus Quest 2 VR technology. Developers designed a training module where athletes could practice increasing neck stiffness prior to a simulated moment of impact.
2. A User-Friendly Data Dashboard
Developers recorded metrics from VR training sessions and built an API to report these metrics to a custom administrative panel. Data from the sessions was able to be filtered, viewed in graphs, and downloaded as a CSV for further analysis.
3. A Wearable Smart Sensor
For the hardware component, illumisoft partnered with Liberate Electronics to design a wearable smart sensor. illumisoft developers programmed the sensors to generate pitch, roll, and yaw data from raw accelerometry, gyroscope, and magnetometer data. These PRY values used to analyze neck stiffness were made available for USB upload after gameplay.
- A non-injurious, intuitive, and cost-effective training system for mTBI research that meets all IRB requirements
- A user-friendly data aggregation panel to view, analyze, and download training data for analysis that will lead to novel research insights
- Development and production of smart sensors that incorporate PRY calculations to present a new type of field data to mTBI researchers
- Collection and analysis of data could expand our understanding of mTBI on athletes, the elderly, and soldiers in combat which could in turn help reduce their risk of these injuries.
- Ultimately will increase the ability of physicians to deliver telemedicine and precision medicine related to mTBI.