IOS app and apple watch app -- 2

@softsolution2000

I have extensive experience in Java, Mobile App Development, Android, Software Architecture, and Kotlin, making me an ideal match for the IOS app and apple watch app project. I am confident in my ability to strategize sensor sampling, implement algorithms, and utilize frameworks like Core Motion and Core ML. Budget flexibility is possible once we discuss the full scope, and my priority is delivering quality within your budget. Let's kickstart this project and achieve milestones together efficiently. Please review my 15-year-old profile for relevant experience. Your satisfaction is my top priority, and I am ready to showcase my commitment by starting work before being hired. Looking forward to discussing the details.

@rbtech1984

Hello There!!! ⚜⭐⭐⭐⭐⚜(( THE PROJECT GOAL IS TO BUILD AN IOS AND APPLE WATCH APP FOR SCRATCHING DETECTION USING ON DEVICE SENSORS ))⚜⭐⭐⭐⭐⚜ Project goal is to create a private and lightweight motion intelligence app that recognises scratching episodes from accelerometer and gyroscope data without sending personal information outside the device. I appreciate your focus on on device processing with the option for a small backend later if model tuning requires it. You are looking for a hybrid detection approach combining rule based filtering with Core ML classification, plus notifications and a simple SwiftUI dashboard for daily and weekly summaries. I have worked with Core Motion sampling, Create ML training pipelines, and energy efficient background tasks, which helps keep battery impact low while maintaining accuracy. My plan is to use windowed feature extraction with adaptive thresholds and a compact model optimised for the Neural Engine. Most important features 1. Reliable motion sampling and Core ML inference on device 2. Configurable alerts with time stamped history 3. Minimal SwiftUI interface for phone and watch I would love to share a milestone plan covering data collection, model validation targets, and battery benchmarks so we can start quickly. Warm Regards, Farhin B.

@ayesha0124

Hi there, I'm excited about the opportunity to develop your lightweight iPhone app and Apple Watch app designed to recognize scratching episodes through motion sensors. My experience with Core Motion and Core ML equips me to suggest an effective sampling strategy utilizing accelerometer and gyroscope data for precise detection. I propose a hybrid model that employs a simple rule layer to filter out noise, complemented by a Core ML model for recognizing nuanced patterns. For the user interface, I will create a minimalist SwiftUI design that includes push notifications for exceeding daily scratching thresholds, along with a single screen displaying time-stamped events and summaries. I can deliver the initial version within 20 days for a cost of $200. Key milestones will include the detection accuracy thresholds, impact on battery life, and thorough unit/UI testing. I look forward to discussing any specific questions you might have regarding framework choices or functionality.

@SBITServices

Hi Andrea, To bring your vision of a lightweight iPhone app and Apple Watch app to life, I propose utilizing a sensor sampling strategy that leverages the accelerometer and gyroscope data. I suggest implementing a hybrid approach with a rule layer to filter noise and an embedded Core ML model for pattern recognition. The interface will feature a single screen for time-stamped events and daily/weekly summaries. Let's discuss further. Regards, Sai Bhaskar

@usamae

I am experienced in motion classification and activity recognition, making me the ideal candidate for your IOS and Apple Watch app project. Utilizing Core Motion and Core ML, I will implement a sensor sampling strategy and algorithms to detect scratching episodes accurately. My expertise in health monitoring and SwiftUI interface design will ensure a seamless user experience. With a focus on privacy and efficiency, I am committed to delivering a high-quality solution that meets your requirements.

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I have developed motion-based activity recognition apps before, utilizing device sensors to capture and classify physical events accurately and efficiently. From my experience, ensuring precise sensor data sampling and seamless integration with Core ML models on-device is key to accomplishing reliable and private detection. Approach: ⭕ I will design a sensor sampling strategy optimizing accelerometer and gyroscope data capturing using Core Motion with power efficiency. ⭕ Implement a hybrid detection mechanism combining a simple rule-based filter with a Core ML embedded classifier trained via Create ML. ⭕ Build a SwiftUI minimal interface showing event logs and configurable thresholds. ⭕ Integrate push notifications for threshold alerts and implement thorough unit and UI testing on recent iPhones. ❓ Could you please specify your target accuracy for scratching detection and battery impact limits? ❓ Is there a preference regarding data export and backend integration timelines? I am confident I can deliver a robust, privacy-centric app swiftly and iterate based on your feedback for optimal results. Best regards, Nam

@mobiwebsolutions

Hi There!!! THE GOAL OF THE PROJECT:- Develop a lightweight iPhone and Apple Watch app that detects scratching episodes using device motion sensors and provides real-time feedback while preserving user privacy. I have carefully read your project description and understand that the app must process data on-device using Core Motion and Core ML, provide push notifications for configurable thresholds, and display time-stamped events with rolling daily and weekly summaries. I am the best fit for this project because I have extensive experience building iOS apps with motion classification, health monitoring, and activity recognition features. Implement on-device motion detection using accelerometer and gyroscope Integrate Core ML for subtle pattern recognition with a rule-based noise filter Develop SwiftUI interface with unit and UI testing on recent iPhone models I provide basic services including UI design, database management, testing, and full source code delivery at project completion, along with setup documentation. I bring 9+ years experience as a full stack developer and have completed similar projects involving sensor-driven health monitoring and real-time activity tracking. Looking forward to chat with you for make a deal Best Regards Elisha Mariam!

@maksymh31

Hello, Approach: implement an on-device pipeline that uses a lightweight rule layer to reject obvious motion noise, then a compact Core ML classifier for nuanced patterns. Sensor strategy: sample accel+gyro at 50–100 Hz in short sliding windows (1–3 s) with event-triggered bursts to save power; compute simple time/freq features and feed a 1D-CNN or small LSTM exported to Core ML (quantized). Frameworks: Core Motion, Core ML, Create ML for prototype labeling, SwiftUI, WatchConnectivity (if Watch companion required), UNUserNotificationCenter for thresholds. Plan & cost: 14 days total, breakdown — core detection & model integration 8d (140 EUR), SwiftUI screens & notifications 3d (60 EUR), unit/UI testing & polish 3d (40 EUR). Total: 240 EUR. Milestones & acceptance: (1) Prototype detection + sample collection; (2) Model tuning to reach precision ≥80% & recall ≥75% on held-out samples; (3) UI, notifications, tests; battery impact ≤3% extra/hour during monitoring; notifications on threshold breach; full on-device processing with optional raw-event export. To start I need any labeled examples or permission to run a short data-collection build, target iPhone/Watch models, and your preferred daily threshold. Do you have labeled scratching samples or are you open to an initial 1–2 day in-app data collection run, and which iPhone/Apple Watch models should I target for testing? Best regards,

@ayesha86664

Hi there! Monitoring scratching episodes manually can be intrusive and inaccurate, and users need a private, reliable solution. A lightweight iPhone app that uses only on-device sensors and processing ensures data privacy while providing accurate tracking. I have experience building motion-sensor-based health and activity monitoring apps using Core Motion and Core ML, with real-time event detection and user-friendly SwiftUI interfaces. I can design a sampling strategy using accelerometer and gyroscope data, implement a hybrid noise-filter + ML detection pipeline, and display time-stamped events along with daily/weekly summaries. Push notifications can be integrated to alert users when thresholds are exceeded, while ensuring low battery impact. Unit and UI testing will guarantee accurate detection and smooth performance on recent iPhone models. I will provide a clear milestone plan covering: sensor strategy and preprocessing, Core ML integration, UI/UX implementation, testing, and final delivery with acceptance criteria including detection accuracy and battery efficiency. check our work https://www.freelancer.com/u/ayesha86664 Would you like me to include a small backend option for exporting raw events for future model updates, or keep it strictly on-device for v1? Let me know if you’re interested & we can discuss it. Best Regards Ayesha

@emirh31

Hello, I can develop your iPhone app to detect scratching using Core Motion and Core ML, processing data fully on-device to ensure privacy. My approach: sample accelerometer & gyroscope at ~50Hz, filter obvious noise with a rule layer, and classify subtle patterns via a lightweight Core ML model. SwiftUI will provide a minimal settings and summary interface, with push notifications for daily thresholds. I’ll also include unit/UI tests on current iPhone models. I estimate $230 over 7 days, covering coding, interface, testing, and milestone reviews. Could you clarify preferred detection accuracy and battery impact tolerance? Looking forward to hearing from you. Thanks.

@borisdeveloper71

Hello,there Thank you for posting your project, "IOS app and apple watch app -- 2." I've read the description carefully and am confident that I can successfully complete this project. I have over 7 years of experience in Mobile Development, Android, Software Architecture, Kotlin, Android App Development, Mobile App Development, Java and Bluetooth. I have done some projects as smiliar as this one. I can share my previous project experience if you'd like. I enjoy learning new technologies and taking on challenges, even those that seem impossible. I'm very interested in this project and am confident that I can deliver the best results possible without stress. I look forward to working with you. Thank you, Boris

@Ahmedh058

Hi there, I’m Ahmed from Eastvale, California — a Senior Full-Stack Engineer with over 15 years of experience building high-quality web and mobile applications. After reviewing your job posting, I’m confident that my background and skill set make me an excellent fit for your project — IOS app and apple watch app -- 2 . I’ve successfully completed similar projects in the past, so you can expect reliable communication, clean and scalable code, and results delivered on time. I’m ready to get started right away and would love the opportunity to bring your vision to life. Looking forward to working with you. Best regards, Ahmed Hassan

@QuickMentor

IOS app and apple watch app -- 2 I'm excited after reviewing your project details! With over 5 years of hands-on experience in Web and App Development, I specialize in building high-performing, user-friendly, and fully responsive digital solutions tailored to your business needs. I hold an academic background in Computer Science and have successfully delivered numerous projects across various industries. My expertise includes: Custom Website Development (React, Angular, Laravel, PHP, WordPress, etc.) Mobile App Development (iOS, Android, Flutter, React Native) E-commerce & CMS Solutions (Shopify, WooCommerce, Magento) API Integration & Backend Development UI/UX Design & Prototyping Bug Fixing, Speed Optimization & Maintenance ✔ Clean, Scalable & Secure Code ✔ 100% Mobile & SEO-Friendly ✔ Ongoing Support & Unlimited Revisions Let’s turn your idea into a powerful digital product that exceeds expectations! Check my profile: https://www.freelancer.com/u/QuickMentor Looking forward to working with you!

@jonhw5

Hello, thanks for posting this project. I’ve reviewed your vision for an on-device iPhone app to detect and track scratching episodes via motion sensors. This aligns strongly with my experience in iOS development, motion data analysis, and health/activity recognition. For sensor processing, I propose using Core Motion for efficient access to real-time accelerometer and gyroscope data, with adaptive sampling rates balancing detection accuracy and battery efficiency. Initial rule-based filtering (based on empirical thresholds for frequency/amplitude) can rapidly exclude noise, followed by an embedded Core ML classifier—ideally prototyped using Create ML—to refine detection of subtler, user-specific scratching patterns. All processing would remain on-device to maximize privacy. For the interface, I recommend building the app with SwiftUI for a polished, minimalist main dashboard displaying detected events and summaries, plus a simple settings view. Notifications will leverage iOS local notification APIs to alert users on configurable thresholds. Unit and UI tests targeting iOS 16 and 17 devices will ensure robustness and reliability. I’ve previously developed iOS solutions for activity and gesture recognition using Core Motion and custom Core ML models, including personal health tracking apps and pilot studies in sensor-based event detection, where both accuracy and minimal battery drain were critical concerns. Upon project kickoff, I’ll share a concise proposal outlining sensor strategy, frameworks, and a milestone-based plan with time/cost estimates. Acceptance criteria will include event detection accuracy (e.g., >90% for true positive scratching events in controlled tests), smooth real-time operation, and negligible ongoing battery impact. Let me know if you’d like more detail or wish to discuss specific model architectures or feature engineering ideas for your use case. Warm regards, Vitalii

@Nilosiqueiratj

Hi, I reviewed your project about "IOS app and apple watch app -- 2" and noticed that you're working with the handset’s own motion sensors, specifically the accelerometer and gyroscope. That tells me the main challenge here is accurately detecting scratching episodes on-device while maintaining privacy and managing sensor data efficiently to avoid battery drain. I’ve worked on similar mobile development projects where I: - designed scalable backend APIs, - implemented secure authentication and data models, - and delivered production-ready web/mobile features. For your project, I’d suggest starting with a sensor sampling strategy that balances frequency to capture motion nuances without excessive battery use, combined with a lightweight rule-based filter followed by an embedded Core ML model for subtler pattern detection. Using Core Motion for sensor data acquisition and Core ML for on-device inference would streamline your approach while preserving user privacy. Before moving forward, I have one quick question: Could you clarify the acceptable thresholds for detection accuracy and battery impact you expect for the initial version? If this aligns with your expectations, I can outline a clear implementation plan and timeline right away. Best regards, Nilo

@andriik18

Hi, I am excited about the opportunity to develop your lightweight iPhone app that recognizes scratching episodes using motion sensors. With over 7 years of experience in mobile app development, I specialize in working with Core ML and Core Motion, which are essential for processing motion data effectively. I can provide a clear sensor sampling strategy and utilize a hybrid algorithm for accurate detection while ensuring user data privacy. I suggest we plan for a minimalist SwiftUI interface, and I will outline milestone deliverables including detection accuracy and battery impact. Let's discuss the project timeline and budget of around 200 EUR, aiming to complete the initial version in 10 days. Best regards, Andrii

@trb2004

Having spent a significant portion of my career designing and developing mobile applications, I'm uniquely positioned to tackle the specifics of your project. Regarding the sensor sampling strategy, I propose maximizing data capture through a smart balance between device performance and battery consumption considerations. My preferred-frameworks include Core Motion, Core ML, Create ML, among others; however, it's your app's specific needs that will guide my framework choice. For the hybrid detection approach you seek, I'll design a filtering mechanism using a rule layer to identify and weed out noise events, couple this with an embedded Core ML model to learn deeper patterns from subtle events. This method will ensure effective roll-up of time-stamped daily and weekly events in the single screen that displays these data points. Past project experiences demonstrate my deep involvement in motion classification and activity recognition fields making me an ideal candidate for your vision. Being a versatile developer, I assure you efficient coding, minimalist SwiftUI interface, comprehensive unit/UI testing on recent iPhone models while remaining cost effective and ensuring timely delivery. With swift communication and quick response time as my driving forces, I guarantee our partnership will bring your lightweight iPhone application to life efficiently and securely on all levels.

@chiragm078

My team and I are thrilled at the opportunity to work on your groundbreaking app project. With our five years of experience and expertise in mobile app development, particularly in Android and Java, we are well-versed in the utilization of sensor data and motion tracking to develop robust, accurate and efficient solutions. In this project, we will leverage powerful frameworks like Core Motion and CoreML, ensuring a well-rounded and thoughtful approach to your app's requirements. The aim is to strike the right balance between accuracy and preserving user privacy by processing data predominantly on-device, while maintaining an option for minimal backend usage. Moreover, our solid background in health monitoring and activity recognition applications makes us distinctly suited for this task. We have successfully created various apps employing sensors similar to what you intend on using, offering valuable insights into motion classification paradigms that could benefit your project. I eagerly look forward to discussing potential milestone deliverables with you, such as detection accuracy thresholds and battery impact evaluations to align our expectations effectively for a fruitful collaboration. Let's revolutionize how scratching episodes are managed with your app together!

@sjohn117

Hi,\n\nI’m Sean, an AI & Full-Stack Developer with over 5 years of experience and strong skills in mobile app development, Core ML, and software architecture. I have previously created motion detection applications, successfully utilizing accelerometer and gyroscope data to classify activities.\n\nFor your iOS app, I recommend using Core Motion for sensor data gathering, coupled with a hybrid model that employs simple rule-based filtering initially, alongside a Core ML model for the nuanced detection of scratching patterns. My approach would ensure that all processing remains on-device for privacy, with a possibility for future backend integration.\n\nI typically deliver this scope in 30 days, inclusive of a minimalist SwiftUI interface and rigorous unit/UI testing on the latest iPhone models. I adhere to high standards in testing, secure code practices, and performance monitoring. I can do this project perfectly. What specific accuracy thresholds and battery impact criteria do you have in mind for the detection model?\n\nThanks,