MovementModeler for Education

Turn Everyday Video into
Stick-Figures and Motion Data

MovementModeler is a small, practical iOS app for getting motion data out of real-world video. Any short clip — staged or in the wild — can become a stick-figure visualization and BODY-25 style pose data, straight from an iPhone.

View on the App Store Python Tools Repo

What MovementModeler Does

MovementModeler is a way to collect and inspect motion data without setting up a lab or wrestling with a full pipeline first. It’s just: phone, video, data.

Motion Data, Anywhere

Record or import video on the phone students already carry. MovementModeler works indoors and outdoors, including in bright sunlight. As with any vision system, stable framing and reasonable video quality help the tracking.

Pocket-sized data collection Works outdoors Real-world footage

Stick-Figure Visualizations

Generate stick-figure videos that make movement easier to see and discuss. Students can scrub and replay clips for quick visual QC before doing any analysis.

In-class demos Visual QC MP4 export

Pose Data for Analysis

Export BODY-25 style pose data per frame and per person. From there, it’s your world — Python, MATLAB, R, or whatever tools you prefer for features, metrics, and models.

BODY-25 style JSON Per-frame data Multi-person

Where It Might Fit

These are just a few places MovementModeler can be useful.

Computer Vision & Machine Learning

Pose Estimation & Beyond

  • Intro labs on keypoints, occlusion, and tracking.
  • Small projects on action recognition or gesture classification.
  • Capstones that start from students’ own recordings instead of static datasets.
Biomechanics & PT / OT

Movement & Gait

  • Comparing joint trajectories between conditions or techniques.
  • Teaching kinematics with simple stick-figures and time series.
  • Linking qualitative observation to basic quantitative measures.
Sports Science & Performance

Technique & Consistency

  • Looking at form in jumps, cuts, pivots, or landings.
  • Exploring left–right differences or stability over time.
  • Letting students try simple performance metrics of their own.

From Video to Data, Briefly

The workflow is straightforward. The goal is to get out of the way so students can focus on the ideas you’re teaching.

  • 1. Capture or import. Film on an iPhone or use an existing clip.
  • 2. Check the stick-figure. Quick visual QC and light cleanup for obvious spikes/noise.
  • 3. Export for analysis. Use the JSON in notebooks and the MP4 in reports/presentations.

Python Tools (Optional)

For classes that already use OpenPose-style tooling, we maintain two small scripts that help bridge MovementModeler exports into common workflows. 

# 1) MovementModeler clip export -> per-frame OpenPose-style JSON
python scripts/mm_to_openpose.py movementmodeler_clip.json frames_json/

# 2) Per-frame JSON -> stick-figure MP4
python scripts/mm_json_dir_to_stickmp4.py frames_json/

Repo: movementmodeler-tools

If you’d like to see it in action

A short screen capture showing the basic flow: import a clip, inspect the stick-figure, clean up obvious spikes, and export MP4 + JSON.

Quick notes for best results

  • Stable framing helps.
  • Higher resolution and less motion blur generally track better.
  • Outdoor clips in bright sunlight work well.
1-minute demo

MovementModeler Demo Thumbnail
Left: in-app workflow · Right: exported stick-figure MP4
Tap to play fullscreen.

Instructor FAQ

Where can students use MovementModeler?

Anywhere they can film. The app works indoors and outdoors, including in direct sunlight. Steady framing and clear subjects help the results.

Do students need special hardware?

Just a reasonably recent iPhone. Analysis can happen in whatever environment you already use — Python, MATLAB, etc.

What does the export look like?

Exports include stick-figure MP4s and BODY-25 style pose data with coordinates and confidence values per joint, per frame, per person.

Can we build our own tools on top?

Yes. The intent is that you and your students treat the exported data as raw material. The optional tools repo exists to make it easier to slot into existing workflows.