Overview / Usage

This project integrates an STM32 F401RE microcontroller, NanoEdge AI Studio, and MPU6050 sensor for a sophisticated activity recognition system. The MPU6050 captures precise motion data, processed by NanoEdge AI Studio to train a model deployed on the STM32 F401RE. The system employs a buzzer for instant fall detection, prioritizing user safety in diverse scenarios.

Methodology / Approach

The methodology for the activity-based recognition system involves the following steps:

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1. Sensor Integration: Integrate MPU6050 sensor with STM32 F401RE to capture accurate motion data.

2. Data Processing: Utilize NanoEdge AI Studio to process and analyze the sensor data, facilitating the training of a machine learning model.

3. Model Training: Train the machine learning model within NanoEdge AI Studio, incorporating various activity patterns for accurate recognition.

4. Model Deployment: Deploy the trained model onto the STM32 F401RE microcontroller to enable real-time processing of sensor data.

5. Activity Recognition: Implement algorithms on the microcontroller to interpret sensor readings, recognizing specific activities based on the trained model.

6. Fall Detection Logic: Integrate a fall detection algorithm to identify potential falls, triggering an alert mechanism, such as a buzzer.

7. Testing and Optimization: Rigorously test the system's performance, fine-tuning algorithms and parameters for optimal accuracy and responsiveness.

8. Integration: Ensure seamless integration of all components – STM32 F401RE, NanoEdge AI Studio, MPU6050, and the alert mechanism – for a cohesive and effective solution.

9. User Feedback: Collect user feedback to refine and enhance the system based on real-world usage scenarios.

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This methodology combines hardware integration, machine learning model development, and algorithmic implementation to create an intelligent activity recognition system with fall detection capabilities.

Technologies Used

The technology used in the activity-based recognition system includes:

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Microcontroller: STM32 F401RE for central processing and real-time data analysis.

Sensor: MPU6050, incorporating a gyroscope and accelerometer to capture precise motion data.

AI Development: NanoEdge AI Studio for developing, training, and deploying machine learning models onto the STM32 microcontroller.

Communication: Protocols such as I2C or SPI for communication between the microcontroller and sensor.

Alert Mechanism: Buzzer for immediate alerts in case of fall detection.

Algorithmic Logic: Custom algorithms for activity recognition and fall detection implemented on the STM32 microcontroller.

Integration Framework: Ensuring seamless interaction between hardware components and software systems.

Testing Tools: Utilizing testing tools and methodologies to assess the system's performance and reliability.

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This combination of hardware, software, and machine learning technologies creates a comprehensive solution for recognizing user activities and enhancing safety through fall detection.