Add AI Pose Estimation to
Your Android App in Days
QuickPose gives Android developers a production-ready pose estimation engine — for fitness, yoga, sports biomechanics, health, and more. No ML expertise required. No cloud dependency. Ship faster.
implementation "ai.quickpose:quickpose-sdk:latest"
// Configure features in Kotlin
val features = listOf(
QuickPoseFeature.Fitness.RepCounter(.SQUAT),
QuickPoseFeature.RangeOfMotion.HipFlexion(),
QuickPoseFeature.Yoga.PoseDetection()
)
Everything you need, nothing you don't
A focused, well-documented SDK built for Android developers who want to ship AI movement features without maintaining their own ML pipeline.
Accurate Pose Estimation
33-point full-body skeleton detection in real time. High accuracy across lighting conditions, camera angles, and body types — tuned for real-world apps, not lab demos.
High Performance
Optimised for Android's Neural Processing Units. Runs efficiently across the Android device ecosystem — from mid-range to flagship — without excessive battery drain.
Pre-Built Models
Ship immediately with ready-to-use models for rep counting, range of motion, yoga pose detection, and joint angle analysis. No training data required.
Customisable Output
Full control over rendered overlays and data output. Draw skeletons, joint angles, rep counts, and feedback — or consume raw pose data and build your own UI entirely.
Open-Source Core
Built on MediaPipe, Google's open-source ML framework. Transparent, auditable, and backed by a well-maintained foundation — no black boxes.
Jetpack Compose & View-Based Ready
Native Kotlin API with Jetpack Compose components out of the box. Works with traditional View-based layouts too. Integrates naturally into any Android architecture.
One SDK, many verticals
QuickPose's pose estimation engine is designed to be domain-agnostic. These are the most common use cases — but the SDK can be shaped to fit your product.
Fitness & Exercise
Automated rep counting, real-time form feedback, and exercise recognition for gym, home workout, and personal training apps.
Yoga & Mindfulness
Detect and score yoga poses in real time, guide users into correct alignment, and track hold duration — without a human instructor.
Sports Biomechanics
Joint angle tracking, movement efficiency analysis, and technique coaching for athletes and sports performance platforms.
Health & Rehabilitation
Objective range of motion assessments for physiotherapy, post-surgery rehab, and clinical monitoring — replacing manual goniometry.
On-device AI your users can trust
Privacy isn't an afterthought — it's in the architecture. QuickPose processes everything locally on the device, making it naturally compliant with GDPR, HIPAA considerations, and Google Play privacy requirements.
No Cloud Processing
All inference runs on the device's NPU. No video frames are ever sent to a server — zero latency from network round-trips, zero data exposure.
No Video Storage
The SDK does not collect, store, or transmit any video or image data. You decide what movement metrics to persist, and where — on-device or your own infrastructure.
Google Play Ready
Straightforward data safety declaration. No third-party data collection to disclose. Passes Play Store review without surprises, including for health and fitness categories.
Two ways to integrate
Start with the GitHub repo and our docs, or work directly with our team for a faster, customised integration.
Build it yourself
Add QuickPose via Gradle and follow our step-by-step documentation. Most Android developers have a working prototype within a day.
- Gradle & Maven Central install
- Full API documentation
- Sample projects & code snippets
- Active GitHub community
Work with our team
Book a consultation and our engineers will integrate QuickPose directly into your Android codebase — configured exactly for your use case, UI, and brand.
- Dedicated integration engineer
- Custom exercise & model config
- Code review & handover
- Post-launch support included
Common developer questions
QuickPose supports Android 8.0 (API level 26) and above. It works across a broad range of Android devices — from mid-range to flagship. Performance is best on devices with a dedicated Neural Processing Unit, though the SDK is optimised to run efficiently on CPU as well.
QuickPose is distributed via Gradle. Add the dependency to your app-level build.gradle file and sync — the SDK will be pulled from Maven Central. Full setup instructions, including ProGuard rules and permissions, are covered in our documentation.
Yes. QuickPose provides native Jetpack Compose components for the fastest integration path, as well as a View-based API for existing codebases. Both are fully supported with complete documentation and sample projects.
Yes. The SDK includes a wide library of pre-built models covering fitness exercises, yoga poses, range of motion metrics, and joint angle tracking. You configure which features are active at runtime. For entirely custom exercises or new AI models, our team can work with you to build and train those specifically.
You can access the SDK and start building for free via our GitHub repo. The first 100 devices per month are free. See our pricing page for more details.
No. All processing happens entirely on the device. No video, images, or movement data are sent to QuickPose or any third-party server. You retain full control over any output data generated by the SDK.
Absolutely. Our integration team can work directly with you to embed QuickPose into your existing Android codebase, configure it for your specific use case, and review the implementation before handover. Book a free consultation to discuss your project.
Ready to Add AI Movement to Your Android App?
Get started with our GitHub repo and docs today, or talk to our team about a guided integration tailored to your use case.
View on GitHub →Want help? Book a free consultation or email info@quickpose.ai
