Smartphone Pupillometry for Concussion Screening

ISEF Category: Translational Medical Science

Subcategory: Disease Detection and Diagnosis  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Your pupils react in a blink, but that blink can carry useful health data. A tiny shift in reflex timing can point to brain stress after head injury or hard exertion. Your phone may be able to measure that shift. That makes this project a real screening challenge, not just a cool app idea.

What Is It?

Pupillometry means measuring how your pupils change size when light hits your eyes. Your pupil is like a camera aperture. It opens in the dark and closes in bright light. The pupillary light reflex is the automatic response that makes that happen.

In this project, you use a smartphone camera and a light stimulus to track how fast and how far the pupil reacts. The key outputs are latency, which is the delay before the pupil starts to respond, and amplitude, which is how much the pupil changes size. If you compare your measurements to published normal ranges, you can ask whether exertion, fatigue, or possible concussion changes the pattern.

Why This Is a Good Topic

This topic works well because you can measure a real biological signal, compare groups, and test a clear hypothesis. It connects to sports safety, brain health, and quick screening tools that might help spot when someone needs more care. You can also collect quantitative data from video, which gives you room for careful analysis instead of guesswork.

Research Questions

• How does acute physical exertion change pupillary light reflex latency in healthy student athletes?
• What is the effect of prior sleep loss on pupillary light reflex amplitude measured by smartphone video?
• Does the time of day change baseline pupil size and reflex timing in the same person?
• To what extent do smartphone-derived pupil measurements match published pupillometry norms?
• Which light stimulus features produce the clearest pupil response signal with a front-facing camera?
• How does repeated exertion across a season affect within-person variation in pupil reflex metrics?

Basic Materials

• Smartphone with front-facing camera and manual video settings capability.
• Consistent white flashlight or phone light source.
• Tripod or phone stand to keep camera position fixed.
• Darkened room or light-blocking setup.
• Printable pupil reference card or calibration target.
• Consent forms and parent or guardian permission forms.
• Data log sheet or spreadsheet template.
• Measuring tape for fixed camera distance.

Advanced Materials

• Smartphone with high-frame-rate front camera or external camera attachment.
• Infrared-compatible illumination setup if available and approved.
• Pupil tracking software or image analysis package.
• Standardized light stimulus device with repeatable output.
• Head stabilization support to reduce motion blur.
• External calibration target for pixel-to-millimeter conversion.
• Heart rate monitor or exertion monitor for pairing with physiologic state.
• Secure data storage for video files and coded participant IDs.

Software & Tools

• ImageJ: Measures pupil diameter frame by frame from video clips and helps you compare response curves.
• Python: Automates frame extraction, smoothing, and basic statistics on pupil time series.
• OpenCV: Detects the pupil edge in video frames and supports custom tracking workflows.
• Google Sheets: Organizes participant data, calculates summary statistics, and makes quick charts.
• PubMed: Helps you find review articles and original studies on pupillometry, concussion, and normal pupil response.

Experiment Steps

1. Define one question you can answer with pupil timing or pupil size, and pick the comparison groups you will use.
2. Choose a video setup that keeps camera distance, lighting, and stimulus timing as constant as possible.
3. Build a calibration plan so you can turn pixels into a pupil diameter estimate and compare across sessions.
4. Select the response metric you will analyze first, such as latency, constriction speed, or peak amplitude.
5. Plan controls that separate true biological change from motion blur, room light changes, and camera auto-adjustment.
6. Design your statistics plan before collecting data, including how you will handle repeated trials and outliers.

Common Pitfalls

• Letting the phone auto-adjust exposure, which makes the pupil look larger or smaller for camera reasons instead of biology.
• Changing room brightness between trials, which confuses the light stimulus effect with background light drift.
• Moving the phone slightly during recording, which breaks pupil tracking and creates false timing changes.
• Using a stimulus that is too weak or too scattered, which gives a noisy reflex signal with unclear onset.
• Mixing participants with different recent exercise or sleep states without recording that context, which hides the real source of variation.

What Makes This Competitive

A strong version of this project does more than show that the pupil changes. It compares your smartphone measurements to published norms, checks repeatability, and tests whether your signal can separate states like rest, exertion, or suspected concussion risk. Better projects also report error bars, inter-rater agreement, and receiver-style performance metrics instead of only averages. That makes your work look like a screening study, not a demo.

Project Variations

• Test whether pupil reflex changes differ after aerobic exercise versus contact practice.
• Compare right-eye and left-eye responses to see whether one eye gives cleaner tracking data.
• Analyze whether pre-exercise hydration status or sleep duration predicts pupil response variability.

Learn More

• PubMed: Search for review articles on pupillometry, concussion, and autonomic nervous system function.
• NIH PubMed Central: Read full-text papers on pupil light reflex methods and data analysis.
• Review of Scientific Instruments: Search for papers on optical measurement methods and eye-tracking instrumentation.
• NASA Open Science resources: Look for public guides on image analysis and signal processing methods that transfer well to video tracking.
• MIT OpenCourseWare: Search biology and signal processing course materials for background on physiological measurement and data analysis.