Rapid Ocular Sideline Concussion Diagnostics
The project aims to provide automated ocular-based concussion testing on the sidelines using a head-mounted eye-set. The team has made progress in gathering research, acquiring prototype parts, and submitting a proposal to the NFL-UA-GE Head Health Challenge. The architecture involves components like an Android tablet, TFT display, camera, RasPi, OpenCV, and WiFi module. Use cases include startup procedures, test cycles, and result compilations. Risks such as access to concussed patients and design challenges are identified with corresponding mitigation plans in place.
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Presentation Transcript
18-549 Design and Architecture: 2/19/2014 Rapid Ocular Sideline Concussion Diagnostics Team 8 Brandon Lee--Andrew Pfeifer--Thomas Phillips--Ryan Quinn 1
Status Update Our Project consists of a head-mounted eye-set that provides automated ocular-based concussion testing and diagnosis for sideline use. o Project Status In communication with several experts, planned meetings and phone calls to gather concussion-based research and contacts General idea endorsement by Dr. Vincent Miele, with suggested addition of integrated balance testing Proposal submitted to NFL-UA-GE Head Health Challenge II o Parts Status Prototype parts ordered on February 8th, most acquired this morning 2
Architecture Concussion-Testing Eyeset Android Tablet A TFT Display Camera B App Trainer Player RasPi 2 OpenCV Kernel D WiFi Module C 3 Diagnosis (Existing) Accelerometer Sensor / Observer 1. Severe impact observed, player brought to sideline 2. Concussion testing A. Tests administered via TFT display B. Eye movements recorded in response to tests C. Image/Video Processing on responses D. Resultant data sent over WiFi link 3. Trainer analyzes data in tablet interface 1 3
Use Cases Application Eyeset Startup Begin Test Cycle* Waiting for Trainer 1. Severe impact observed 2. Player moved to sideline 3. Eye-set equipped 4. Trainer activates testing via App interface 5. Test cycle begins A. Visual test on TFT display B. Eye (pupil) responses recorded 6. Image/video processing on responses to compile test results 7. Next test administered 8. Diagnosis given Begin Test Procedure Tests in Progress... Record Responses Display Visual Test * Present Test Results Gather/Process Test Results *Test cycle includes: 1. Dilation Test 2. Depth Test 3. Tracking Test Send Test Results Shutdown 4
Risks and Mitigation Risks Mitigation Plan Direct access to concussed patients for testing might not be available Extensive testing with un-concussed subjects; UPMC contacts may help Eye-set design may be uncomfortable, unbalanced, or clunky Design a compact housing for RasPi and sensors, possibly with counter balance Camera focus may lack sharpness and clarity to perform eye analysis on a wide variety of eye types Alternative lenses may need to be acquired; image and video processing algorithms may account for possible blurs Plan A Plan B Plan C Android App works smoothly; ergonomic eye-set performs accurate tests; individualized player diagnoses Individualized player diagnoses give way to more general tests; the eye-set and App are still well packaged and easy-to-use Less refined eye-set performs accurate, general tests that are reliably sent to an easy-to-use App interface 5
