A lot of computer-vision work stalls on procurement. The pitch involves depth cameras, wearables, or a controlled lab, and suddenly a promising idea needs a hardware budget and a deployment headache. Often, it doesn’t have to.
The signal is already in the pixels
Modern pose-estimation models extract a surprising amount of structure from ordinary video. With transformer-based architectures, you can track fine-grained movement (joint trajectories, timing, symmetry, variability) from a standard webcam or phone camera. No markers, no wearables, no special room.
That changes the economics. A capability that used to require a clinic visit can run in a browser tab.
Why longitudinal tracking is the real unlock
A single measurement is a snapshot. The interesting signal is usually in how something changes over time:
- Is a movement pattern stable, improving, or drifting?
- How much does it vary day to day?
- Does a trend appear weeks before anyone would notice it by eye?
Tracking the same individual longitudinally turns noisy single readings into a trajectory you can reason about, which is exactly what matters in health and developmental contexts.
Engineering, not magic
None of this works without careful engineering: consistent capture, robust normalisation, sensible handling of missing frames, and honest evaluation against real baselines. The model is the easy part. Making it dependable in the wild is the work, and it’s the part worth paying for.