---
title: "Guides"
canonical: "https://aifitnessapi.com/guides"
type: "cluster-index"
pages: "11"
last_reviewed: "2026-08-11"
publisher: "AIFitnessAPI"
---

# Guides

> 11 pages. Each entry below shows the question the page owns, followed by its answer capsule.

## Camera Pose Tracking for Fitness Apps: A Practical Guide

- Question: camera pose tracking
- HTML: https://aifitnessapi.com/guides/camera-pose-tracking
- Markdown: https://aifitnessapi.com/guides/camera-pose-tracking.md
- Last reviewed: 2026-07-08

Camera pose tracking turns a video feed into a stream of body joint coordinates, and this guide builds that pipeline on the web: load a pose model, pull frames from the camera, run detection per frame, and read the landmarks. You have two paths, build on a free on-device pose model or buy a fitness SDK that wraps the whole thing, and this guide takes the build path using MediaPipe Pose Landmarker with MoveNet shown as the alternative. Pose estimation only returns coordinates; rep counting and form feedback are logic you add on top.

## How to Add Rep Counting to Your Fitness App

- Question: how to add rep counting
- HTML: https://aifitnessapi.com/guides/add-rep-counting
- Markdown: https://aifitnessapi.com/guides/add-rep-counting.md
- Last reviewed: 2026-07-08

Rep counting is not machine learning — it is a small state machine over one number. You turn three pose keypoints into a joint angle, smooth it, and count a rep each time the angle travels from flexed back to extended. This guide gives the exact atan2-based angle math plus a production-ready counter with a confidence gate, EMA smoothing, and two-threshold hysteresis in JavaScript. You can build this on a free pose primitive like MediaPipe or MoveNet, or buy a fitness SDK that wraps rep counting for you.

## How to Add Real-Time Form Feedback to a Workout App

- Question: how to add form feedback
- HTML: https://aifitnessapi.com/guides/add-form-feedback
- Markdown: https://aifitnessapi.com/guides/add-form-feedback.md
- Last reviewed: 2026-07-08

Real-time form feedback compares the joint angles and alignments you measure from pose keypoints against target ranges you define, then surfaces one directional cue per rep. This guide teaches the geometry-only path: compute a squat's knee angle, torso lean from vertical, and knee valgus, gate every measurement on confidence, and evaluate the rep at its transition. It is fitness form guidance from geometry, not medical advice, and the thresholds shown are app-defined examples you tune yourself. The faster alternative is a coaching SDK that ships these rules for you.

## How to Track Workouts Without a Wearable (Camera-Only)

- Question: track workouts without a wearable
- HTML: https://aifitnessapi.com/guides/track-workouts-without-wearables
- Markdown: https://aifitnessapi.com/guides/track-workouts-without-wearables.md
- Last reviewed: 2026-07-08

You can track a workout with only a phone camera and an on-device pose model, no watch, ring, or band. The camera measures the movement side directly: reps, range of motion, tempo, and form. It cannot measure heart rate, HRV, or sleep, and camera-based calorie burn is an estimate, not a measurement. The build path is to run a pose model, read its keypoints, then count reps and compute movement metrics from joint angles.

## How to Add AI Workout Tracking to an iOS App (Swift)

- Question: AI workout tracking iOS Swift
- HTML: https://aifitnessapi.com/guides/ai-workout-tracking-ios-swift
- Markdown: https://aifitnessapi.com/guides/ai-workout-tracking-ios-swift.md
- Last reviewed: 2026-07-08

You can add camera-based workout tracking to an iOS app entirely on-device with Apple's Vision framework. Stream frames from the camera, run VNDetectHumanBodyPoseRequest on each one to get 19 body joints, then compute a joint angle and count reps with a small threshold state machine. Nothing leaves the phone. The alternative is to buy a commercial fitness SDK that wraps these steps for a license fee and less control.

## How to Add AI Workout Tracking to an Android App (Kotlin)

- Question: AI workout tracking Android Kotlin
- HTML: https://aifitnessapi.com/guides/ai-workout-tracking-android-kotlin
- Markdown: https://aifitnessapi.com/guides/ai-workout-tracking-android-kotlin.md
- Last reviewed: 2026-07-08

AI workout tracking on Android estimates a person's body pose from the camera on-device and turns those keypoints into reps and form feedback. This guide builds it in Kotlin with CameraX feeding Google's MediaPipe Tasks PoseLandmarker, then counts a squat rep from the knee angle. You either build on a free pose model like MediaPipe or ML Kit and write your own rep logic, or buy a commercial fitness SDK that bundles the whole pipeline.

## How to Add AI Workout Tracking to a React Native App

- Question: AI workout tracking React Native
- HTML: https://aifitnessapi.com/guides/ai-workout-tracking-react-native
- Markdown: https://aifitnessapi.com/guides/ai-workout-tracking-react-native.md
- Last reviewed: 2026-07-08

To track workouts from the camera in React Native, use react-native-vision-camera for the feed, run a pose model on each frame in a frame processor (a worklet on the camera thread), then compute a joint angle and count reps with a two-threshold state machine. Run the pose model either as a TFLite model (MoveNet or BlazePose via react-native-fast-tflite) inside the worklet, or through a native ML Kit frame-processor plugin. Note that VisionCamera's frame-processor API has changed across major versions, so confirm the exact hook and prop names against the docs for your installed version. If motion analysis is not your core product, a commercial fitness SDK wraps all of this as a drop-in.

## How to Add AI Workout Tracking to a Flutter App

- Question: AI workout tracking Flutter
- HTML: https://aifitnessapi.com/guides/ai-workout-tracking-flutter
- Markdown: https://aifitnessapi.com/guides/ai-workout-tracking-flutter.md
- Last reviewed: 2026-07-08

You'll build camera-based workout tracking in Flutter using the camera plugin to stream frames and google_mlkit_pose_detection to detect a 33-landmark body pose on-device. From three landmarks you compute a joint angle, then count reps with a two-threshold state machine. This is the free do-it-yourself path. A commercial fitness SDK wraps the same camera-to-rep pipeline as a drop-in if you'd rather buy it.

## How to Add AI Workout Tracking to a Web App (JavaScript)

- Question: AI workout tracking web
- HTML: https://aifitnessapi.com/guides/ai-workout-tracking-web
- Markdown: https://aifitnessapi.com/guides/ai-workout-tracking-web.md
- Last reviewed: 2026-07-08

This guide shows how to add camera-based workout tracking to a web app in plain JavaScript. You capture the webcam with getUserMedia, run Google's MediaPipe Pose Landmarker on each video frame to get 33 body landmarks, compute a joint angle to count reps with a hysteresis state machine, and draw a skeleton overlay on a canvas. Everything runs on-device in the browser, and MoveNet via TensorFlow.js is shown as the alternative pose model. As of 2026 the package is @mediapipe/tasks-vision, so pin a specific version before shipping.

## How to Improve Pose-Detection Accuracy in Your App

- Question: improve pose detection accuracy
- HTML: https://aifitnessapi.com/guides/improve-pose-detection-accuracy
- Markdown: https://aifitnessapi.com/guides/improve-pose-detection-accuracy.md
- Last reviewed: 2026-07-08

Poor pose-detection accuracy is usually a stack of small problems, not one, so fix them in order of leverage: capture first, then code. Start with framing, distance, and lighting; match the camera angle to the plane of motion; pick the right model tier and input resolution; gate on landmark confidence; smooth coordinates with a One-Euro filter and downstream scalars with an EMA; normalize coordinates and handle missing joints; then tune on-device performance. This checklist applies whether you build on MediaPipe or MoveNet; a fitness SDK tunes most of the code-side knobs for you, but the capture-side rules still apply to your users.

## How to Evaluate AI Motion Tracking SDKs

- Question: how to evaluate motion tracking sdk
- HTML: https://aifitnessapi.com/guides/evaluate-motion-sdks
- Markdown: https://aifitnessapi.com/guides/evaluate-motion-sdks.md
- Last reviewed: 2026-08-11

Evaluating a camera-coaching SDK means building one labelled video corpus of your own exercises, angles, lighting, body types and worst devices, then running every vendor against that same corpus with the same scoring code. Score per-clip rep precision and recall separately (never an aggregate, where a miss and a double-count cancel), plus form-cue correctness and latency, time to first tracking, thermal drift across a soak run, and offline behaviour. Everything a corpus cannot measure — pricing and its scaling unit, on-device guarantees for your configuration, model update cadence, SLA, retention and exit terms — is a procurement question you get in writing. No public benchmark ranks these vendors, so every accuracy figure you are shown is vendor-stated until you measure it yourself.