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Automated Cognitive Assessment of Piglets through Eye Blink Conditioning

Automated Cognitive Assessment of Piglets through Eye Blink Conditioning. Group 36 Darren Chen, Neil Sarwal , James Sangmok Yun ECE 445 Senior Design May 5, 2014. Introduction.

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Automated Cognitive Assessment of Piglets through Eye Blink Conditioning

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  1. Automated Cognitive Assessment of Piglets through Eye Blink Conditioning Group 36 Darren Chen, Neil Sarwal, James Sangmok Yun ECE 445 Senior Design May 5, 2014

  2. Introduction • Present a solution to test the cognitive development of piglets through classical conditioning and the “eye blink paradigm”. • Study the effects of animal nutrition on permanent changes in neurodevelopmental patterns for piglets

  3. Piglet Harness/Mask

  4. Features • Camera/Lighting • Linux Hub/Display • Tone Delivery • Air Puff Delivery • Expandable, Modular Design

  5. ODROID + Modules Power Input Monitor Logitech C910 Buzzer Keyboard, Mouse, Camera Boost Converter + Relays LED Lighting

  6. System Overview • Hardware • Boost Converter/Electronic Valve • LED Lighting • Buzzer • Software • ODROID XU-Lite • Interface/Communication with Modules • Image Processing

  7. System Overview

  8. Hardware Overview/Requirements • Boost Converter/Electronic Valve • Takes 5Vdc from ODROID adapter and converts to stable 12Vdc for use with the electronic valve for air puff delivery • LED Lighting • Provides ample light around the camera for clear monitoring • Buzzer • Accepts a 5-12Vdc and produces a tone, signaling the start of the trial

  9. Boost Converter • RLC Circuit, output controlled by a relay • Pulse Generator powering the relay • Boosted voltage powers the Electronic Valve in air puff delivery

  10. Boost Converter – Pulse Generator – Schematic VCC Reset Discharge Trigger Threshold Control Output GND

  11. Boost Converter – Pulse Generator – Output

  12. Boost Converter – RLC Circuit – Schematic

  13. Boost Converter – RLC Circuit – Output

  14. Boost Converter – LED Lighting • Using white LED’s, to allow minimal color/gradient distortion in video feed • Hole to be cut to fit the camera • Brightness controlled manually via trimpot

  15. Boost Converter – LED Lighting – Schematic

  16. Boost Converter – LED Lighting – Output

  17. Boost Converter – Buzzer • Powered by DC voltage • Produces a ~70dB tone to signal the trial beginning

  18. Boost Converter – Buzzer – Schematic

  19. Software Overview/Requirements • ODROID XU-Lite • Central Hub for all attached modules and software • Interface with Modules • Using GPIO pins on the Odroid, send signals to relays controlling the modules • Timing of activation of modules in accordance with the Eye Blink Paradigm • Image Processing • Eye blink detection algorithm

  20. 650 ms Delay Paradigm 600 ms Start Tone Start Airpuff 100 ms End Tone/Airpuff Intertrial interval Correlation EyeBlink Detection Period Checkpoint Startle Response Period Time (ms)

  21. C++ code Overview • Turn on LED • Capture Base Image/Calibration • Session while loop (until 20 trials){ Intertrial Period, randomly generated 20-40 seconds Recapture Base Image if needed Start Tone, 100ms sleep time for startle period Start Eyeblink Detection algorithm End detection by timing 600msoutput blink confirmation, start airpuff valve Sleep 50ms End Valve, Tone, Trial }

  22. Timing average per trial using Timestamp() • Total Numbers: 50 • Mean: 0.67849 Seconds • Standard Deviation: 0.00522 seconds • Consistently about 28 ms off of what we want • Due to video processing and while loop configuration • Potential solution: Smarter timing configuration Account for processing delay within code

  23. Verification via Odroid Terminal output

  24. Verification via Oscilloscope X Axis intervals: 100.0ms Y Axis intervals: 1.00V dX = 675.000 ms dY = 1.8000V

  25. Eye Blink Algorithm Overview • Uses a base image every trial period • Appropriate histograms are displayed throughout process • Compares histograms during critical points of the Eye Blink Paradigm • Comparison between base histogram and eye blink histogram is initiated • Correlation is used to compare histograms to pre-set threshold to determine eye blink

  26. Acquiring the Histograms • Separate helper function is used • Algorithm is defined as: • Set histogram bin count and range • Create Matrix for histogram • Calculate histogram for each frame • Visualize each bin • Return the histogram matrix

  27. Acquiring the Base Image If the histogram is too different or too similar then the previous base histogram and by extension the image is kept.

  28. Determining Eye Blink OpenCV’s compareHist function will compare the base histogram to the eye blink histogram. Crossing the threshold of .8 will indicate an eye blink

  29. Challenges/Problems • Boost Converter • Prolonged use results in decreasing efficiency • High current load causes components to overheat • Divide current load with alternative circuit design

  30. Future Hardware Development • Develop better methods of powering the air puff delivery • Scale modules up to 12 test trials at once • Secure mounting on the piglet’s mask

  31. Future Software Development • Develop a more user-friendly interface • Allow free control of timing between each module • Port data and display to a separate PC • Increase accuracy of eye blink detection

  32. Credits • Prof. Paul Scott Carney • Prof. Andrew C. Singer • Prof. Ryan Neil Dilger • Mr. Austin Mudd • Mr. Kevin Chen

  33. Thank You Darren Chen, Neil Sarwal, James Sangmok Yun

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