Full-Stack IoT Activity Recognition System (STM32 + Android)

This project is a complete Full-Stack Internet of Things (IoT) system designed for real-time human activity recognition (Walking vs. Running). By combining embedded C programming, cloud infrastructure, Machine Learning, and Android mobile development, this system demonstrates a true end-to-end IoT pipeline.

How It Works

The architecture is divided into three main components:

Device Firmware: The STM32F407 (RT-Spark) generates movement data and formats it into a JSON payload.

• Device Firmware: The STM32F407 (RT-Spark) generates movement data and formats it into a JSON payload.

Cloud Backend: A Python MQTT listener receives the data, passes it to a trained Random Forest Machine Learning model, stores the prediction in an SQLite database, and serves it via a Flask Web API.

• Cloud Backend: A Python MQTT listener receives the data, passes it to a trained Random Forest Machine Learning model, stores the prediction in an SQLite database, and serves it via a Flask Web API.

Mobile App: A Kotlin Android app queries the Flask API using the Volley HTTP library and displays the live prediction and historical data.

• Mobile App: A Kotlin Android app queries the Flask API using the Volley HTTP library and displays the live prediction and historical data.

The Hardware Challenge & Engineering Workarounds

During development, we encountered two major hardware limitations with the provided RT-Spark board that required creative engineering solutions:

Challenge 1: Sensor Unresponsiveness. The onboard LSM6DSL accelerometer/gyroscope returned persistent HAL_ERROR (NACK) responses across all I2C buses.

Solution: We verified the hardware fault using a custom bare-metal I2C diagnostic script. To ensure the pipeline could still be tested, we implemented a synthetic data generator in FreeRTOS that mimics realistic walking/running waveforms.

• Solution: We verified the hardware fault using a custom bare-metal I2C diagnostic script. To ensure the pipeline could still be tested, we implemented a synthetic data generator in FreeRTOS that mimics realistic walking/running waveforms.
• Challenge 1: Sensor Unresponsiveness. The onboard LSM6DSL accelerometer/gyroscope returned persistent HAL_ERROR (NACK) responses across all I2C buses.Solution: We verified the hardware fault using a custom bare-metal I2C diagnostic script. To ensure the pipeline could still be tested, we implemented a synthetic data generator in FreeRTOS that mimics realistic walking/running waveforms.

Challenge 2: Wi-Fi Incompatibility. The board utilizes an RW007 Wi-Fi module, which lacks native support for the ISM43362 driver required by the lab.

Solution: We developed a custom UART-to-MQTT bridge. The STM32 sends JSON data via USB, and a Python script (serial_to_mqtt.py) acts as a gateway, publishing the data to the local Mosquitto broker.

• Solution: We developed a custom UART-to-MQTT bridge. The STM32 sends JSON data via USB, and a Python script (serial_to_mqtt.py) acts as a gateway, publishing the data to the local Mosquitto broker.
• Challenge 2: Wi-Fi Incompatibility. The board utilizes an RW007 Wi-Fi module, which lacks native support for the ISM43362 driver required by the lab.Solution: We developed a custom UART-to-MQTT bridge. The STM32 sends JSON data via USB, and a Python script (serial_to_mqtt.py) acts as a gateway, publishing the data to the local Mosquitto broker.

Setup & Code

The full source code, including the firmware, Python backend, and Android app, is available on GitHub.

(You can include the flowchart image we made here)

Backend: Run app.py (Flask API) and mqtt_listener.py (ML Classifier).

• Backend: Run app.py (Flask API) and mqtt_listener.py (ML Classifier).

Bridge: Flash the STM32 and run serial_to_mqtt.py to forward data.

• Bridge: Flash the STM32 and run serial_to_mqtt.py to forward data.

App: Build and run the Android app in an emulator to see the live predictions.

• App: Build and run the Android app in an emulator to see the live predictions.