Skip to content
AF
AI Motion

AI Motion & Pose Estimation for Fitness Apps

Recently re-verified:Pose Estimation Models Compared: MediaPipe, MoveNet, YOLO and More (Oct 18),MediaPipe Pose Landmarker Models: Lite vs Full vs Heavy (Oct 18),Squat Detection With Pose Estimation: Landmarks, Camera, Rep Logic (Oct 18)

Camera-based fitness runs on pose estimation: a model finds body keypoints in each frame, and your app turns those into reps, form cues, and coaching. This hub is the technical layer — which pose model to choose, 2D vs 3D, on-device vs cloud inference, what drives accuracy, and how rep counting and form scoring actually work — with links to the how-to guides when you’re ready to build.

New to the idea? Start with what pose estimation is. Ready to implement? The AI workout tracking guides walk the per-platform wiring, and the AI tracking APIs cover the buy option. This cluster is the decisions in between.

Writing code against a model’s output? The pose keypoint maps list every index of 9 keypoint schemes, read from each model’s own source.

Pose estimation tech

Which model, how many dimensions, how accurate.

Model by model

What each model outputs, runs on and is licensed under, from its own source.

Performance & deployment

Where inference runs and what it needs.

Interpreting motion

Turning keypoints into reps, form, and a decision.

Exercise by exercise

Landmarks, camera placement, rep logic and measured accuracy for one exercise at a time, from published studies.

Frequently asked questions

Do I need a special camera for AI motion tracking?
No. A regular RGB smartphone camera is enough for 2D pose estimation and even monocular (single-camera) 3D landmarks — you don't need a depth sensor. GPU/NPU acceleration helps performance on-device, and lighting and framing matter more than the camera itself. The hardware-requirements page covers what actually affects it.
Should pose estimation run on-device or in the cloud?
On-device (ML Kit, MediaPipe, TensorFlow Lite, Core ML) is usually the better default for fitness: it's low-latency, works offline, has no per-frame server cost, and keeps workout video private. Cloud inference lets you run heavier models consistently across devices but adds latency, bandwidth, and the privacy weight of streaming video. The on-device-vs-cloud page walks the trade-off.
How does an app count reps or check form from a camera?
It runs pose estimation to get body keypoints each frame, then interprets them: rep counting tracks a joint angle or keypoint over time and detects the up/down phases (usually a state machine with thresholds); form feedback computes joint angles and range of motion and compares them to a target. Both are covered here — and note that camera-based form feedback is a coaching aid, not medical or physical-therapy advice.

All 121 questions in AI Motion, answered

Independent comparison, last reviewed July 24, 2026. Pricing, rate limits, and feature availability change often — confirm current details in each provider’s official documentation before you commit. Product and company names are trademarks of their respective owners; AIFitnessAPI is not affiliated with, endorsed by, or sponsored by any product listed here.