AI-Powered Pelvic Floor Fitness App With Real-Time Pose Correction for Women's Health
Built for M2 Method, a California pelvic health programme that needed its AI proof of concept turned into a production mobile platform.
Feedback Latency
300 ms max
Platforms
iOS + Android
POC to Production
Full Build
From Validated AI Proof of Concept to a Production Pelvic Health Platform for Women
Pelvic floor dysfunction affects nearly one in four women and is significantly underserved by digital health tools. Most fitness apps offer generic video-based exercise libraries with no ability to assess whether the user is performing movements correctly. For therapeutic exercises targeting pelvic stability and abdominal control, that gap is not just a UX limitation. Incorrect form during rehabilitation can aggravate existing conditions, and without real-time correction, home-based programmes carry real clinical risk.
M2 Method is a 12-week online pelvic floor health programme created by Meenal Mujumdar, a pelvic floor specialist and physical therapist with 17 years of practice in the Bay Area, California. The programme addresses urinary leakage, pelvic pain, postpartum recovery, and core dysfunction in women through structured exercise protocols. Meenal had already validated an AI pose detection proof of concept and needed Akoode to productise it into a fully deployable mobile application with subscription management, group access, and an admin panel for programme management.
Akoode built the complete M2 Method platform: a Flutter mobile application for iOS and Android with on-device AI pose detection and real-time corrective feedback, a Node.js backend on AWS with full subscription and group management, and a web-based admin panel giving Meenal's team complete control over programmes, users, exercises, and content.
300ms
Max Feedback Latency
AI pose correction fires within 300 milliseconds of detecting a movement deviation, keeping guidance in step with the user's exercise.
2
Platforms Live
A single Flutter codebase delivers the full application experience on both iOS and Android from one build pipeline.
6
Training Programmes
Structured pelvic floor programmes created, managed, and published through the admin panel for subscribed users to access.
38
Active Groups
Group-based programme access and in-app messaging running across 38 user groups managed through the admin dashboard.
Project Info
Client
M2 Method, USA
Industry
Healthcare
Use Case
AI Fitness App With Real-Time Pose Correction
Solution
Artificial Intelligence and Mobile App Development
Engagement
Fixed Cost
What Challenges Do Women's Health Platforms Face in Delivering AI-Guided Therapeutic Exercise?
Pelvic floor rehabilitation requires a level of movement precision that standard fitness apps are not built to verify. Users performing exercises at home have no mechanism for knowing whether their form meets the biomechanical criteria that make the exercise therapeutic rather than harmful. Building a platform that bridges that gap requires solving problems in real-time pose validation, data privacy, cross-platform delivery, and subscription-based programme access that most fitness app frameworks do not address out of the box.
No Real-Time Form Validation in Home Exercise Programmes
Women following pelvic floor rehabilitation programmes at home had no way to verify whether their joint angles, hip positions, and stability thresholds met the biomechanical criteria required for safe, effective exercise.
AI Proof of Concept Not Deployable as a Consumer Product
The validated pose detection logic existed as a Python prototype. Converting it into a production mobile application that ran on standard smartphones required a complete architecture rebuild around on-device inference.
No Subscription or Group Management Infrastructure
Delivering structured 12-week programmes with tiered access, Apple and Google billing integration, and group-based programme assignment required a backend and admin system that did not yet exist.
Programme Content Locked Behind Developer Dependency
Without an admin panel, adding new exercises, publishing programmes, managing users, and updating content required technical intervention for every change, making the platform unsustainable to operate at scale.
A physical therapist with 17 years of clinical expertise and a validated AI model should not be limited to one-to-one sessions. The platform needed to scale that expertise to every woman running the programme at home, with the same correction precision available in a clinical setting.
What We Set Out to Build
The brief required productising a validated AI proof of concept into a fully deployable, subscription-based mobile fitness platform. The finished system needed to deliver real-time pose correction on standard smartphones, support structured 12-week pelvic health programmes with group and subscription access controls, and give M2 Method's team complete independence to manage content, users, and programmes without developer involvement.
Productise AI Pose Detection for Mobile
Convert the validated Python pose detection proof of concept into an on-device AI system that runs within a Flutter mobile application on iOS and Android, meeting a maximum feedback latency of 300 milliseconds.
Build Conversational Onboarding and Programme Matching
Design an onboarding flow that identifies fitness level, goals, and physical limitations through guided questions and maps each user to the appropriate programme and difficulty level automatically.
Deliver Subscription and Group Access Management Integrate
Apple in-app purchases and Google Play billing, build subscription validation logic, and create a group-based access system that allows programme assignments and group messaging for subscribed users.
Build a Full-Featured Web Admin Panel
Deliver a web-based admin dashboard that gives M2 Method's team complete control over exercise configuration, programme creation and publishing, user management, group assignments, and blog content without developer access.
Deploy a Scalable Backend on AWS
Build a Node.js REST API backend hosted on AWS with PostgreSQL, CI/CD pipeline, token-based authentication, HTTPS encryption, and cloud-based media storage for all programme video and instructional content.
Turning a Real-Time AI Proof of Concept into an Intelligent Pelvic Health Platform
Akoode built the M2 Method platform end to end: a Flutter mobile app with on-device pose correction, a Node.js AWS backend with full subscription and group management, and a web admin panel for complete programme and content control. The result is a production-ready mobile health platform where Meenal's clinical expertise is delivered at scale through real-time AI guidance available to every user on every session.
Onboarding and Assessment
New users complete a conversational onboarding flow that identifies their fitness level, goals, and physical limitations. The system maps their responses to the appropriate programme and difficulty level before they begin their first session.
Subscription and Access Control
Users select a subscription plan and complete payment through Apple or Google billing. Subscription status is validated by the backend before any programme content or group access is unlocked, with access revoked automatically on expiry.
Exercise Execution With AI Correction
The user selects an exercise, the camera activates, and on-device pose detection maps 33 body landmarks in real time. The system calculates joint angles, hip positions, and stability indicators frame by frame, comparing them against the active step's biomechanical thresholds.
Real-Time Corrective Feedback
When a deviation is detected for more than three seconds, the system delivers audio correction: "Raise your leg higher", "Straighten your knee", "Hold that position". Visual overlays on the camera feed provide simultaneous positional guidance without requiring the user to look away from their form.
Progress Tracking and Group Engagement
Completed sessions are tracked against the programme structure. Subscribed users access group content and participate in group messaging. The admin panel updates exercises, publishes new programmes, manages user groups, and monitors platform activity in real time.
What Makes This System Powerful
Highlight 01
On-Device AI Pose Detection With Real-Time Corrective Feedback
The application runs pose estimation entirely on the user's smartphone using a pre-trained model that maps 33 body landmarks per frame. A rule-based engine compares calculated joint angles and stability indicators against exercise-specific thresholds defined in a JSON configuration, triggering audio and visual correction within 300 milliseconds of any deviation.
- 33 body landmarks tracked per frame in real time
- Audio feedback fires within 300ms of detecting form deviation
- JSON rule engine allows new exercises without changing application code
Highlight 02
Conversational Onboarding With Intelligent Programme Matching
First-time users complete a guided onboarding conversation that identifies their fitness level, pelvic health goals, and any physical limitations. The system maps these responses to the appropriate programme and difficulty level, ensuring every user begins their 12-week journey on a path matched to their specific starting point.
- Goal and limitation assessment built into the first-login flow
- Programme difficulty assigned automatically based on onboarding responses
- Users begin with a personalised programme recommendation, not a generic default
Highlight 03
Subscription Management With Apple and Google Billing Integration
The platform handles the full subscription lifecycle through native Apple in-app purchase and Google Play billing integrations. Subscription status is validated server-side before any premium programme content or group access is delivered, with automatic access revocation on expiry and reinstatement on renewal.
- Native Apple and Google billing flows integrated without third-party intermediaries
- Server-side subscription validation before content access granted
- Automatic access control on expiry and renewal without manual intervention
Highlight 04
Group-Based Programme Access and In-App Messaging
Subscribed users can be assigned to groups that have specific programme access, additional content, and group-level chat. Group messaging allows M2 Method to build community within the platform, with administrators controlling group membership, programme assignment, and access permissions from the admin panel.
- Group-level programme assignment and access control
- In-app text messaging within each group
- Admin-managed group membership and permission structure
Highlight 05
Web Admin Panel for Full Programme and Content Management
A full-featured web admin panel gives M2 Method's team complete operational control. Administrators create and configure exercises including AI correction thresholds and alignment rules, build and publish training programmes, manage user accounts and subscriptions, assign group memberships, and publish blog content without any developer involvement.
- Exercise creation with configurable AI correction thresholds per movement
- Programme build, publish, and group assignment from one interface
- User management, subscription monitoring, and blog publishing all in-house
Key Challenges in Building an AI-Powered Pelvic Health Mobile Application
Productising an AI proof of concept into a consumer mobile application for a therapeutic health context introduced challenges that extended well beyond standard mobile development. The requirement for on-device AI inference at sub-300-millisecond latency, combined with the clinical sensitivity of the exercise domain and the need for dual-platform subscription billing, meant that every layer of the stack required decisions specific to this brief rather than standard framework defaults.
Achieving Sub-300ms Pose Correction on Consumer Smartphones
The clinical value of the feedback depends entirely on its speed. A correction that fires two seconds after a deviation has occurred does not help a user mid-exercise.
Our Approach
Pose detection was implemented on-device using pre-trained MediaPipe models, eliminating server round-trip latency and keeping inference within the smartphone's local processing pipeline at all times.
Converting a Python POC Into a Production Mobile AI System
The validated proof of concept ran in Python with OpenCV on a desktop. Rebuilding the same detection and correction logic to run within a Flutter mobile application required a complete re-architecture of the inference pipeline.
Our Approach
The biomechanical rule engine was rebuilt as a JSON-configurable system within the Flutter application, with on-device pose estimation replacing the desktop OpenCV pipeline while preserving the correction logic and threshold definitions from the original POC.
Therapeutic Safety in a High-Precision Exercise
Domain Pelvic floor rehabilitation exercises require exact biomechanical validation. Incorrect correction thresholds or missed deviations in this domain carry real clinical risk, not just poor user experience.
Our Approach
Exercise correction rules were defined in close alignment with the clinical criteria established by Meenal Mujumdar, with each threshold validated against the original POC's biomechanical specifications before being deployed into the production rule engine.
Dual-Platform Subscription Billing With Server-Side Access Control
Apple and Google have separate billing systems with different API flows, validation requirements, and lifecycle event handling. Both needed to be integrated without creating inconsistency in how subscription access was granted or revoked
Our Approach
Both billing integrations were handled natively with server-side subscription validation, ensuring access control was enforced consistently regardless of which platform the user subscribed through, with automatic handling of renewals, expirations, and reinstatements.
What Changed After Implementation
Before this platform existed, M2 Method operated as a content-based online programme with no mechanism for verifying that users were performing exercises correctly at home. Meenal's clinical expertise was available in person and through video content, but the real-time correction that makes pelvic floor rehabilitation safe and effective for self-directed users was absent. After the build, every subscribed user on iOS or Android has access to the same AI-guided correction that Meenal provides in clinical sessions, delivered through their smartphone camera within 300 milliseconds of any movement deviation. M2 Method's team manages all programme content, user groups, and platform operations from the admin panel without developer support.
No Real-Time Form Correction for Home Users
Users followed video-based programme content with no way to verify whether their joint angles and stability met the therapeutic criteria for each exercise.
AI Validation Existed Only as a Desktop Prototype
The pose detection proof of concept ran on a desktop with OpenCV and was not accessible to any end user in a consumer context.
No Subscription or Group Infrastructure
Programme access, payment processing, and group-based content delivery had no backend system to manage or automate them at scale.
Programme Content Required Developer Changes
Updating exercises, publishing programmes, and managing users had no admin interface, making every operational change a development task.
On-Device AI Correction in a Flutter Mobile App
Rebuilt the pose detection and correction logic as an on-device system within a Flutter application, delivering real-time feedback on iOS and Android at sub-300ms latency.
Full Productisation From POC to App Store
Converted the validated Python proof of concept into a production-ready mobile application, approved and live on the Apple App Store and Google Play Store.
Node.js Backend With Subscription and Group Management
Built a complete AWS-hosted backend handling authentication, Apple and Google billing, subscription lifecycle, group access, and content delivery for the full platform.
Web Admin Panel for Full Operational Independence
Delivered a web admin panel giving M2 Method's team complete control over exercises, programmes, users, groups, and blog content without any developer involvement.
Real-Time AI Correction Available on Every Session
Every subscribed user receives pose correction within 300 milliseconds of a movement deviation, on their own smartphone, without needing a therapist present.
Platform Live on iOS and Android App Stores
The M2 Method application is publicly downloadable and operating with active subscribers and group members across both platforms.
Subscription and Group Access Fully Automated
Apple and Google billing, access control, group assignment, and expiry management all run without manual intervention from the M2 Method team.
Full Programme and Content Management In-House
Meenal's team creates exercises, publishes programmes, manages users, and runs group operations entirely through the admin panel from day one.
300msMax Feedback Latency
AI pose correction fires within 300 milliseconds of detecting a movement deviation during live exercise.
2App Stores Live
The full application is publicly downloadable on both the Apple App Store and Google Play Store
38Active User Groups
Group-based programme access and in-app messaging running across 38 managed user groups on the platform.
Use Cases of AI Pose Detection in Healthcare and Digital Fitness Applications
The on-device pose correction architecture, JSON-configurable rule engine, and subscription-based mobile platform built for M2 Method applies directly to any healthcare, rehabilitation, or fitness organisation that needs to deliver real-time movement validation to users at home. The same approach works across physical therapy, sports performance, occupational rehabilitation, and guided wellness programmes.
Pelvic Floor and Postpartum Rehabilitation Apps
Physical therapists and pelvic health specialists delivering structured home rehabilitation programmes that require real-time movement validation for safe, unsupervised exercise.
Post-Surgical Physiotherapy Platforms
Orthopaedic and surgical rehabilitation providers building digital programmes where joint angle precision and movement sequencing during recovery must be monitored in real time.
Sports Performance and Athletic Coaching Apps
Performance coaches delivering technique correction for strength movements, running form, or sport-specific drills where instant feedback on joint position and alignment is critical.
Occupational Therapy and Injury Prevention Tools
Workplace health programmes using AI pose detection to monitor correct movement during occupational tasks or rehabilitation exercises for injury prevention and return-to-work support.
Corporate Wellness and Remote Fitness Platforms
Employer wellness platforms delivering guided exercise programmes to remote or distributed workforces where real-time form feedback replaces the need for in-person instruction.
Subscription-Based Specialist Health Programme Apps
Any licensed healthcare practitioner or wellness specialist wanting to productise their clinical methodology into a subscription mobile application with AI-guided exercise correction at its core.
Why Businesses Choose Akoode Technologies for Artificial Intelligence Development
Akoode builds AI-powered mobile platforms for healthcare, fitness, and wellness organisations that need production-ready systems, not prototypes. The team handles the full build scope from AI model integration and mobile application development through backend infrastructure, subscription billing, and admin panel delivery. Projects range from POC productisation for clinical health platforms to full-stack AI application development for consumer wellness products.
Proven POC-to-Production AI Development
Akoode took M2 Method's validated pose detection proof of concept and rebuilt it as a production mobile system running on consumer smartphones at sub-300ms latency. That gap between a working prototype and a shippable product is where most AI projects stall. Akoode closes it.
End-to-End Delivery Across Mobile, Backend, and Admin
The M2 Method platform was built entirely within Akoode's team: Flutter mobile app, Node.js AWS backend, Apple and Google billing integration, and web admin panel. The client received one complete, production-ready system with no integration gaps between layers.
Clinical Domain Sensitivity in AI System Design Building
AI correction for therapeutic pelvic floor exercises required working from clinical criteria, not generic fitness datasets. Akoode approached the correction thresholds with the same precision a physical therapist would apply, validating every rule against the established biomechanical specifications before production deployment.
Operational Independence Delivered at Handover
M2 Method's team manages their full platform, exercises, programmes, users, groups, and content, without returning to a developer. That independence was built into the system from the start, not added as an afterthought.