A webcam model can identify hand or body landmarks, but a useful interaction requires much more than drawing dots on a video frame. The application has to turn noisy observations into stable actions that feel predictable.
SyntaxLab's tracking demo runs hand, body, and face landmark tasks in the browser. It interprets finger positions as gestures, calculates body movement measures, and applies smoothing or thresholds before driving a 3D interaction or displaying observations.
Separate detection from interaction
A landmark model reports estimated points. The application interprets them. A pinch might mean “grab,” but only after the system defines a threshold, filters rapid fluctuations, and handles a hand that disappears between frames.
That separation makes the experience easier to tune. Detection errors can be measured independently from gesture interpretation and UI response. If a gesture fires repeatedly, the fix may be debounce logic or state transitions rather than a different model.
Design for latency and variation
Camera quality, lighting, hand distance, browser performance, and device thermals all affect the experience. A prototype should be evaluated on ordinary phones and laptops, across backgrounds and user movement, rather than only on a developer workstation.
MediaPipe's web Hand Landmarker documentation describes the task API and the outputs developers can use in JavaScript applications. Google AI Edge: Hand Landmarker for Web
Ask for consent and explain processing
Camera access should be explicit, easy to stop, and limited to the feature that needs it. The SyntaxLab tracking demo processes the camera for interactive feedback; its project code is evidence of a browser implementation, not a full privacy audit or a guarantee of uniform performance.
Project evidence
The browser pipeline is in syntaxlab/src/routes/demos.tracking.tsx and the components under src/components/demos/tracking/. The page uses MediaPipe Tasks Vision and updates a Three.js scene from hand/body/face state. This is an experiment/demo; no accessibility, medical, safety, or fitness outcome has been validated.