Pet Respiration Monitor App

@AwaisChaudhry

Hi there, I will build a robust cross‑platform Pet Respiration Monitor app that lets owners record a 30‑second clip, runs on‑device TensorFlow Lite to count breaths per minute, and flags readings over 40 BPM or rising trends. The app will store readings locally, plot trends, provide shareable reports, and support subscription billing on Android and iOS as you described. I’ll implement clean Kotlin for Android and Swift for iOS (or a tight cross‑platform layer if you prefer), ensure on‑device inference is fast, and save history in a local database with clear visuals. You’ll get well‑commented code, a short README explaining how to update the model, pricing changes, and future builds, plus internal test builds on both stores for your review. I’ll also include a simple, easy update path for the model and pricing, and a ready‑to‑review workflow in GitHub (or a zipped hand‑off). What is the single most critical decision for you: native Kotlin/Swift vs a cross‑platform layer while preserving TFLite and store billing? Best regards,

@irsolutions

With over a decade of experience in full-stack architecture and high-scale systems, I understand your goal of developing a cross-platform Pet Respiration Monitor App. My background in building and scaling high-complexity systems, such as serving over 1 million users, directly applies to the challenges of ensuring the accuracy and reliability of respiratory rate monitoring for pets. To ensure the success of your project, a strategic insight would be to implement a robust data storage solution for storing and analyzing the pet's respiratory rate trends over time. Drawing from my experience in developing AI/ML applications, I can confidently handle the integration of TensorFlow Lite for accurate inference and provide actionable insights for pet owners. I am confident that my expertise in native and cross-platform mobile development, along with my experience in AI/ML and high-security systems, makes me well-equipped to deliver a scalable and secure Pet Respiration Monitor App. I encourage you to reach out to discuss the roadmap further and address any questions or concerns you may have. Thank you for considering my proposal.

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you need a pet health mobile app that records/uploads a 30-second sleeping video, runs on-device TFLite inference to count breaths per minute, flags readings over 40 BPM, stores history, shows trends, and manages subscriptions after one free reading. I’ve worked on mobile apps with ML model integration, in-app purchases, local storage, charts, and user authentication. My approach would be to build native Kotlin/Swift or Flutter depending on model compatibility, integrate TFLite on-device, add secure login, local reading history, trend charts, shareable reports, and App Store/Google Play subscription validation. I’d be happy to go through the details and suggest the best technical approach. I have a few questions to get a better understanding: Q1 – Do you already have the trained TFLite model ready, or should model preparation be included? Q2 – Should readings sync to a backend, or remain local-only on the device? Q3 – Which email service do you prefer for reminders: Firebase, SendGrid, Mailgun, or another provider? Looking forward to hearing from you.

@planDapps

READY TO SYNC: TECHNICAL ALIGNMENT & STRATEGIC ROADMAP A call is the best way to address the nuances of your on device inference and the subscription lifecycle logic. At Plan D Studios, we bring 12+ years of experience in Mobile App Development and Custom Software, focusing on high impact iOS Development and Android builds that require surgical precision. What we will bring to the call: Technical Clarity: We'll dive into the Video Processing requirements for TFLite and how to ensure consistent BPM detection across varied hardware. Architecture Strategy: Discussing the Database Design for Room/CoreData to ensure smooth plotting of your respiratory trends. Monetization Logic: Reviewing the safest way to implement the free to paid bridge within Google and Apple’s IAP frameworks. Milestone Thinking: Mapping out a 6–12 week execution plan that aligns with your market entry goals. Everything we discuss is handled by our in house team no outsourcing, no communication gaps. Would you like to schedule a session for tomorrow to walk through the technical README and the model integration process? Regards, Haider

@kamran2012

Hi there, I will deliver your pet respiration monitor as a Flutter app — video capture, on-device TFLite inference, subscription billing, and trend charting — with test builds on both store tracks throughout development. For the breath-counting pipeline, I will run frame differencing on the chest region after the user places a bounding box over the sleeping pet. This approach keeps inference lightweight and avoids needing a full pose-estimation model, while still producing reliable BPM readings even in low-light conditions typical of sleeping environments. Questions: 1) Do you already have a trained TFLite model, or will I need to build the respiratory-rate pipeline from video frames? 2) Which email service do you prefer for subscription reminders — SendGrid, Mailgun, or something already in place? Looking forward to talking through the details. Kamran

@pinkeegupta

Hi, I’ve reviewed your pet respiratory monitoring app idea—this is a strong and practical MVP, and I can deliver it efficiently with clean architecture and store-ready builds. I recommend a cross-platform approach (Flutter) to keep development fast while still supporting on-device TensorFlow Lite and full subscription integration via Google Play Billing and StoreKit. What I’ll deliver: Secure login (email/password) Video recording/upload + TFLite breathing detection BPM result with alert (>40 BPM) History + trend graphs Subscription system ($4.99/month, 1 free reading) Test builds on Android & iOS Clean GitHub code + documentation Timeline: 6–10 weeks working days Cost: $3,000 – $5,000 (final depends on model complexity) Quick questions: Do you already have a trained TFLite model? Should data stay local or sync to cloud? Any preferred email service for reminders? I can start immediately and share test builds during development. Regards Anju

@reedsystems

Hi, I'm excited to propose my services for building this cross-platform mobile app. With over a decade of experience in developing apps across multiple platforms, I am confident that I can deliver a high-quality solution within the scope and timeline you've outlined. Here's what will be done: - Email-and-password sign-up/log-in: Secure user authentication. - Recording/uploading 30-second clips: Users can either record or upload videos of their sleeping pets. - On-device inference with TensorFlow Lite: Real-time breath count using the provided model. - Result screen: Display respiratory rate and provide color-coded warnings if >40 BPM. - History tab: Local storage of readings with time-based plotting, helping users identify trends over time. Shareable reports for easy sharing with veterinarians or family members. - Automatic email reminders: Regular notifications to subscribe. The app will be developed natively on both Android (Kotlin) and iOS (Swift), ensuring a seamless user experience across platforms. A clean cross-platform approach will also be considered if required. I will maintain commented source code in GitHub, providing detailed documentation for updates and future builds. Development timeline: 6–12 weeks Portfolio: You can review my previous work at [https://www.freelancer.com/u/reedsystems] Looking forward to your feedback on the model accuracy, UI design preferences, and email service requirements. This will help us finalize all details before starting the project. Best regards, Reed

@melikatfz

Hi there, Ensuring accurate respiratory tracking and seamless user experience in your app is crucial. A common challenge could be achieving precise on-device inference with TensorFlow Lite, especially while managing efficient cross-platform operations. With my expertise in mobile development and TensorFlow integrations, I can ensure a robust solution that maintains high accuracy and efficient performance across both Android and iOS platforms. Here are my questions: 1. Could you specify the version of TensorFlow Lite you plan to use? 2. Are there any specific UI/UX preferences or existing designs you want to implement? Let’s discuss your project now!

@DeveloperStation

Hi there, We’ve developed similar apps that use AI to analyze pet behavior, including a dog training app that uses video to assess dog reactions to various stimuli. We also built a web app for veterinarians to manage pet profiles and share training videos with owners, allowing them to log in and view these videos. For your project, we can use a combination of native and hybrid development to deliver a fully optimized solution. With 15 years of experience, I’ve worked extensively with both native and hybrid frameworks, and I’ve led teams that built fully hybrid apps with native-level performance. Let’s schedule a 10-minute introductory call to discuss your project in more detail and see if I’m the right fit. I usually respond within 10 minutes. Best regards, Adil

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I have built several cross-platform apps using TensorFlow Lite for on-device ML inference that worked seamlessly for real-time video processing. From my experience, the most important part is achieving smooth integration of TFLite with native or cross-platform code while ensuring accurate and fast breath rate detection. Approach: ⭕ Implement user authentication with email and password. ⭕ Develop native or cross-platform video recording and upload feature. ⭕ Integrate TFLite model for breath counting on-device. ⭕ Implement result display with clear warnings and history tracking using Room or iOS equivalents. ⭕ Add subscription handling with Google Play and Apple in-app purchases. ⭕ Set up automatic email reminders and internal test tracks for ongoing review. ❓ Could you specify the preferred cross-platform framework if any? ❓ Which email service do you plan to use for reminders? ❓ Is the TensorFlow Lite model already trained and ready or do you need help updating it? I am confident in delivering a fully functional, well-documented, and tested app within your timeline and budget. Looking forward to working with you. Best regards, Nam

@kishanbwts

I’ve worked on a cross-platform health app that processed video input for real-time analysis, including on-device TensorFlow Lite inference. For your pet respiration monitor, I’d suggest a React Native or Flutter solution with native modules for TFLite and billing, which speeds development while keeping platform-specific needs intact. Before starting, could you share the TFLite model size and input requirements? Also, what email service or provider do you prefer for automatic subscription reminders? Knowing this will help set up smooth backend integration without surprises. I estimate a 10-week timeline to cover sign-up, video capture/upload, on-device inference, local data storage with charts, subscription flows, plus internal test track builds and documentation. The quote would include fully commented code and a README explaining model updates, price changes, and publishing. I’m ready to lock down details and get a first iteration running quickly. Let me know how you want to proceed.

@mahig47

Hello! As per your project post, you’re looking to build an Pet Respiration Monitoring Mobile Application that allows users to record videos of their pets, analyze breathing rates using on-device AI, and manage subscriptions for continued access. The goal is to create a reliable, user-friendly health monitoring tool with accurate detection, clear alerts, and a smooth subscription flow. My focus will be on delivering a complete AI-powered mobile solution, featuring: secure email and password authentication, video recording or upload with 30-second clip handling, TensorFlow Lite integration for on-device respiration analysis, real-time result display with color-coded alerts for abnormal BPM, local data storage with history tracking and trend visualization, shareable reports for users, and subscription system integration using Google Play Billing and iOS in-app purchases. I specialize in building cross-platform mobile apps using Flutter or native technologies with AI model integration and secure payment systems, focusing on performance, scalability, and clean architecture. My focus will be on creating a dependable health tracking app that delivers accurate results and a smooth subscription journey for users. Let’s connect to review the AI model integration, subscription flow, and data visualization so we can finalize the MVP scope and development plan. Best regards, Nikita Gupta.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Cross-Platform App for Pet Owners to Monitor Breathing Rates ❇️ Hi My Friend, I hope you are doing well. I reviewed your project, and it seems you are looking for a developer to create a cross-platform mobile app for pet owners. Look no further; Zohaib is here to help you! My team has completed over 50 similar projects in mobile app development. I will build a user-friendly app that records videos, counts breaths, and sends alerts when necessary. ➡️ Why Me? I can easily create your mobile app as I have 5 years of experience in mobile development, focusing on Android and iOS. My skills include Kotlin, Swift, and TensorFlow Lite integration. Additionally, I have a strong grip on app deployment and user interface design, ensuring a smooth experience for users. ➡️ Let's have a quick chat to discuss your project details and let me show you samples of my previous work. I look forward to our conversation! ➡️ Skills & Experience: ✅ Kotlin Development ✅ Swift Development ✅ TensorFlow Lite ✅ Mobile App Design ✅ User Authentication ✅ Video Processing ✅ Subscription Integration ✅ Data Storage (Room) ✅ Charting Libraries ✅ API Development ✅ Code Documentation ✅ App Testing Waiting for your response! Best Regards, Zohaib

@vladang

Ongoing pet sleep-video monitoring is a real-time on-device ML flow focused on speed and clarity. Your flow (capture → TFLite inference → risk flag + subscription gating) is a mobile architecture problem. I’d use native Kotlin and Swift with shared TFLite, running fully on-device after trimming to a 30s buffer. Results stored locally (Room/CoreData) with charts (MPAndroidChart/Charts) to show trends and rising respiratory patterns. Subscriptions via Play Billing + StoreKit; email reminders via SendGrid or similar. Iterative delivery with internal test builds on both stores to validate model accuracy early from model testing onward. Q1: Expected TFLite input specs (fps, resolution, preprocessing)? Q2: Fully on-device only, or any cloud inference fallback? Q3: Email backend preference (SendGrid, Firebase, or custom)?

@purealmaz

I HAVE A QUESTION: Do you have the trained TensorFlow Lite model ready for integration, or is model development part of this scope? Also, for the subscription reminder emails, do you have a preferred third-party service (e.g., SendGrid, Mailgun)? I understand your requirements for a cross-platform pet respiration monitor app featuring on-device AI, subscription billing, and health trend analysis. With extensive experience in mobile app development, integrating machine learning models, and implementing in-app purchases, I’m confident I can deliver a robust solution within your 6-12 week timeline. As requested, here is my relevant experience. One of my recent projects, available on both app stores, was a wellness app that used the phone's camera for real-time health metric analysis. The system supports: - On-device processing with a custom ML model for instant feedback. - Secure user authentication and local data persistence using Room/CoreData. - Subscription tiers managed via RevenueCat to simplify App Store and Google Play billing. This experience is directly relevant to your project, especially with regard to on-device TFLite integration, cross-platform subscription management, and local data charting. My initial timeline estimate is 8-10 weeks, and we can finalize the cost after a brief discussion on the UI/model specifics. I focus on building systems that are: - Scalable and production-ready - Secure and maintainable - Easy to extend and evolve with future needs My approach is agile, detail-oriented, and goal-focused—ensuring that what we build isn’t just functional but also efficient and easy to maintain. Let’s connect and discuss how we can bring your idea to life. Looking forward to it! Best regards, Philip Oyedoyin

@hr157

Hi, I’m a mobile app developer with 8+ years of experience building AI-powered, subscription-based apps for iOS and Android. I can deliver a clean, high-performance solution with on-device TFLite inference, smooth video handling, and secure in-app billing. Skills & Deliverables: • Native (Kotlin/Swift) or cross-platform app • TFLite model integration (on-device inference) • Video capture/upload & processing • Subscription setup (Google Play + Apple IAP) • Local storage (Room/CoreData) + charts • Shareable reports & email reminders • Test builds + GitHub source + documentation Timeline: 6–10 weeks (MVP + testing) Questions: • Is the TFLite model finalized? • Preferred email service (SendGrid, Firebase, etc.)? • Any UI references or branding ready? Let’s connect to finalize details and start.

@markupdudes

Hi, I’m excited about your Pet Respiration Monitor App project and confident in delivering a seamless, cross-platform mobile solution that accurately captures and analyzes pet breathing using TFLite. With experience in native Android and iOS development using Kotlin and Swift, including integrating machine learning models and in-app purchase systems, I can ensure a smooth flow from video capture to real-time respiratory rate analysis, user subscription, and data visualization. I’ll provide clean, well-commented code on GitHub and working builds for test tracks on both platforms within your 6-12 week timeline. Your detailed flow and expectations align perfectly with my expertise, especially in video processing, on-device inference, and subscription management. Let’s finalize the model update process, UI specifics, and email service details to start promptly. Could you share more details about the TensorFlow Lite model’s input/output format and performance requirements? Thanks,

@abdulmoeed138

Greetings, I can confidently handle this project as a Senior Mobile App Developer specializing in cross-platform applications. With proven experience in developing similar apps, I am well-equipped to create the Pet Respiration Monitor App you require. My profile on freelancer.com is attached for your review. I will deliver: - Seamless email-and-password sign-up/log-in functionality - Efficient recording and analysis of pet respiratory rates - Intuitive result screens with color-coded warnings - History tab for tracking trends over time - Shareable reports and automatic email reminders For high-quality work within budget, message me on freelancer.com to discuss further details. I look forward to collaborating with you on this innovative project. Regards, Moeed

@AlexVydrin

Hello, I am free now and can start work immediately in your timezone. Your project sounds fascinating, and I'd love to help bring it to life. With my experience in developing mobile apps, I've worked on similar projects and am familiar with integrating TensorFlow Lite models for on-device processing. I can ensure a smooth user experience for both Android and iOS users with either native or cross-platform solutions. I'll provide clean, well-documented code and keep you posted throughout the development process by uploading builds for your review. If you have any specific questions about the model or UI, feel free to ask so we can get everything aligned before starting. Looking forward to hearing from you soon!