Android Developer Needed for Activity Detection App with Federated Learning
Budget: ₹1,500 – ₹12,500 INR
Job Description:
We are looking for an experienced Android developer to build an activity detection application. The app will use mobile sensors to detect various user activities (e.g., walking, running, sitting) and employ federated learning to improve detection accuracy while maintaining user privacy. This app will be designed to analyze sensor data locally on the device, allowing the model to learn and improve without centralized data collection.
Key Responsibilities:
1.) Develop an Android app that utilizes mobile sensors (e.g., accelerometer, gyroscope) to detect and classify physical activities.
2.) Implement federated learning to enable model updates directly on user devices, ensuring data privacy and efficient distributed learning.
3.) Design and implement a user-friendly interface for activity tracking and feedback.
4.) Optimize the app for performance, ensuring minimal battery and resource consumption during continuous activity monitoring.
Test and validate the app for accuracy and performance across various Android devices.
Requirements:
1.) Experience with Android Development: Strong proficiency in Android app development (Java/Kotlin) with a solid understanding of Android SDK, UI design principles, and best practices.
2.) Sensor Data Handling: Familiarity with integrating and processing data from mobile sensors, such as accelerometers and gyroscopes, for activity recognition.
3.) Machine Learning Knowledge: Experience with machine learning frameworks (e.g., TensorFlow Lite, PyTorch Mobile) on Android.
4.) Federated Learning: Knowledge of federated learning techniques is a plus, including experience with frameworks that support on-device learning.
5.) Strong Problem-Solving Skills: Ability to analyze sensor data and design accurate, efficient algorithms for activity classification.
6.) Communication Skills: Capable of documenting and communicating progress and technical challenges effectively.
Preferred Qualifications:
Prior experience with on-device machine learning and federated learning on mobile platforms.
Familiarity with Android’s battery optimization and efficient sensor management practices.
Previous projects in health, fitness, or activity recognition applications.
We are looking for an experienced Android developer to build an activity detection application. The app will use mobile sensors to detect various user activities (e.g., walking, running, sitting) and employ federated learning to improve detection accuracy while maintaining user privacy. This app will be designed to analyze sensor data locally on the device, allowing the model to learn and improve without centralized data collection.
Key Responsibilities:
1.) Develop an Android app that utilizes mobile sensors (e.g., accelerometer, gyroscope) to detect and classify physical activities.
2.) Implement federated learning to enable model updates directly on user devices, ensuring data privacy and efficient distributed learning.
3.) Design and implement a user-friendly interface for activity tracking and feedback.
4.) Optimize the app for performance, ensuring minimal battery and resource consumption during continuous activity monitoring.
Test and validate the app for accuracy and performance across various Android devices.
Requirements:
1.) Experience with Android Development: Strong proficiency in Android app development (Java/Kotlin) with a solid understanding of Android SDK, UI design principles, and best practices.
2.) Sensor Data Handling: Familiarity with integrating and processing data from mobile sensors, such as accelerometers and gyroscopes, for activity recognition.
3.) Machine Learning Knowledge: Experience with machine learning frameworks (e.g., TensorFlow Lite, PyTorch Mobile) on Android.
4.) Federated Learning: Knowledge of federated learning techniques is a plus, including experience with frameworks that support on-device learning.
5.) Strong Problem-Solving Skills: Ability to analyze sensor data and design accurate, efficient algorithms for activity classification.
6.) Communication Skills: Capable of documenting and communicating progress and technical challenges effectively.
Preferred Qualifications:
Prior experience with on-device machine learning and federated learning on mobile platforms.
Familiarity with Android’s battery optimization and efficient sensor management practices.
Previous projects in health, fitness, or activity recognition applications.