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
TFT
Display
Camera
B
App
Trainer
A
2
RasPi
D
WiFi
Module
3
Player
OpenCV
Kernel
C
Diagnosis
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
(Existing)
Accelerometer Sensor / Observer
1
3
Use Cases
Startup
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
*Test cycle includes:
1. Dilation Test
2. Depth Test
3. Tracking Test
Shutdown
Application
Waiting for
Trainer
Eyeset
Begin Test
Cycle*
Begin Test
Procedure
Tests in
Progress...
Record
Responses
Display
Visual Test
*
Present Test
Results
Gather/Process
Test Results
Send Test
Results
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