Project Title: Android Human Activity Recognition with TensorFlow
I. Introduction
The Android Human Activity Recognition with TensorFlow project is aimed at building a mobile application that can recognize and classify human activities based on sensor data from an Android device. It uses machine learning models, particularly TensorFlow, to perform activity recognition in real-time.
II. Objectives
Develop an Android application for real-time human activity recognition.
Implement machine learning models using TensorFlow for accurate activity classification.
Utilize sensor data from the Android device, such as accelerometer and gyroscope readings.
Create a user-friendly interface to display recognized activities.
III. Technologies Used
TensorFlow (Machine learning framework)
Android Studio (IDE)
Java (Programming language for Android development)
Accelerometer and gyroscope sensors (Android sensor framework)
IV. Features
Activity Recognition:

Real-time recognition and classification of human activities, such as walking, running, sitting, and standing.
Machine Learning Model Integration:

Implement a TensorFlow model for activity recognition.
Train the model using a dataset of labeled sensor data.
User Interface:

Display recognized activities on the user interface.
Provide visual feedback for the current activity.
V. Project Structure
Files:
MainActivity.java: Main activity handling sensor data and model inference.
TensorFlowModel.java: Class for loading and running TensorFlow models.
SensorDataProcessor.java: Class for processing and filtering sensor data.
activity_recognition_model.tflite: Pre-trained TensorFlow Lite model for activity recognition.
VI. User Interface Design
Main Screen:
Real-time display of recognized activities.
Visual feedback for the current activity.
VII. Machine Learning Model
TensorFlow Model:
Architecture of the machine learning model.
Training process and dataset used for model training.
VIII.Conclusion
Summary of the Android Human Activity Recognition project.
Reflection on the development process, challenges faced, and lessons learned.
X. Future Enhancements
Ideas for additional features or improvements for the app.
Consideration for model optimization and real-time model updates.
XI. References
Any external libraries, frameworks, or resources used during the development.