Useful computer vision starts with a webcam
You don't need specialist hardware to extract meaningful signal from movement. Standard video, transformer-based pose models and longitudinal tracking go a long way.
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.