Build Environment Monitoring with Raspberry Pi RP2040 using Machine Learning
Table of Contents
- Introduction
- Raspberry Pi Pico: An Overview
- Features and Specifications
- Raspberry Pi Foundation's Documentation and Support
- Raspberry Pi Pico and Edge Impulse
- Integration with Edge Impulse Studio
- Pre-built Firmware and Firmware Customization
- Using Sensors with Raspberry Pi Pico
- Pre-configured Sensors
- Sampling Analog Signals with RP2040 ADC
- Variety of Sensors for Different Applications
- Sensor Fusion for Kitchen Fire Detection
- Understanding the Risks of Unattended Cooking
- Using VOC Sensors for Smoke Detection
- Detecting Stove Conditions with Temperature and Humidity Sensors
- Training a Machine Learning Model
- Training and Deploying the Model with Edge Impulse Studio
- Distinction between Normal Cooking, Burning, and Idle Stove
- Deploying the Model to Raspberry Pi Pico
- Flashing the Pre-built Firmware
- Downloading and Using the C++ Library
- Improving the Project
- Exploring Other Sensor Combinations
- Considering Different DSP Blocks for Event Detection
- Exploring Other Boards Compatible with Edge Impulse
Article
Raspberry Pi Pico and Edge Impulse: Revolutionizing Embedded Systems
Introduction
Welcome to this guide on using Raspberry Pi Pico with Edge Impulse. In this article, we will explore how the Raspberry Pi Pico board, a low-cost microcontroller, has become one of the most popular choices for embedded systems enthusiasts. We will also Delve into the integration of Raspberry Pi Pico with Edge Impulse, a powerful platform for training and deploying machine learning models. By the end of this guide, You will have a clear understanding of how to utilize the features of Raspberry Pi Pico and Edge Impulse for various projects.
Raspberry Pi Pico: An Overview
Features and Specifications
Raspberry Pi Pico, released in 2021, is a feature-packed microcontroller board that offers excellent performance at an affordable price. It is equipped with the RP2040 microcontroller, which has a dual-Core Arm Cortex-M0+ processor running at up to 133MHz. The board also includes 2MB of flash memory, 26 programmable GPIO pins, and support for a wide range of peripherals.
Raspberry Pi Foundation's Documentation and Support
One of the key reasons behind the popularity of Raspberry Pi Pico is the extensive documentation and software support provided by the Raspberry Pi Foundation. The official documentation provides detailed information on getting started, using the GPIO pins, and programming the board using various programming languages. The Raspberry Pi community is also highly active and provides valuable assistance and resources for developers.
Raspberry Pi Pico and Edge Impulse
Integration with Edge Impulse Studio
In February 2021, Edge Impulse announced full integration of Raspberry Pi Pico with their Edge Impulse Studio. This integration allows developers to easily train and deploy machine learning models to Raspberry Pi Pico. The studio provides a user-friendly interface for collecting and labeling data, training models, and generating firmware ready for deployment.
Pre-built Firmware and Firmware Customization
With the integration of Raspberry Pi Pico and Edge Impulse, developers can leverage the pre-built firmware available in the studio. This firmware can be quickly flashed onto the board for data collection, and then, after training the machine learning model in Edge Impulse Studio, the same firmware can be downloaded with the integrated model. Alternatively, developers can choose to download the C++ library or Arduino IDE library to deploy the model with DSP blocks as a standalone project, complete with program logic and actuators.
Using Sensors with Raspberry Pi Pico
Pre-configured Sensors
Raspberry Pi Pico comes with three pre-configured sensors in the firmware. These include the Groov Ultrasonic Ranger, which can measure distance using sound waves; the DHT11 Temperature and Humidity Sensor; and the LSM6DS3 Accelerometer and Gyroscope. Additionally, the RP2040 ADC (Analog to Digital Converter) allows for sampling analog signals, enabling the use of a wide range of sensors.
Variety of Sensors for Different Applications
In addition to the pre-configured sensors, developers can take AdVantage of multiple analog signal sensors compatible with RP2040's ADC. These sensors range from commonly used sensors like light and sound sensors to more specialized ones, such as carbon dioxide and natural gas sensors. Edge Impulse provides a CLI data forwarder that simplifies data collection from any sensor, opening up endless possibilities for various applications.
Sensor Fusion for Kitchen Fire Detection
Understanding the Risks of Unattended Cooking
Unattended cooking is a significant cause of kitchen fires, accounting for nearly 31% of home fires and 53% of cooking fire-related deaths in the United States between 2014 and 2018. To address this issue, integrating a device into kitchen hoods or appliances that can monitor the environment and detect smoke or burning is crucial for fire prevention and safety.
Using VOC Sensors for Smoke Detection
To detect smoke and volatile organic compounds (VOCs) in the kitchen environment, VOC sensors prove to be effective. VOCs are carbon compounds present in smoke, and their levels can indicate the need for better ventilation due to factors such as new furniture, consumer products, or redecorations. The Groov VOC module, Based on the WSP2110 sensor, can be used to Sense VOCs emitted during cooking activities and alert the user for potential risks.
Detecting Stove Conditions with Temperature and Humidity Sensors
To complement VOC sensors, temperature and humidity sensors can provide valuable insights into stove conditions. By utilizing the data from both temperature and humidity sensors, it becomes possible to differentiate between normal cooking, burning, and idle stove scenarios. This fusion of sensor data enhances the accuracy of fire detection and prevention.
Training a Machine Learning Model
Training and Deploying the Model with Edge Impulse Studio
Edge Impulse Studio simplifies the process of training a machine learning model using data collected from various sensors. By uploading the collected data to the studio, developers can label and preprocess the data, select and train different machine learning algorithms, and evaluate the performance of the models. Once the model is trained and optimized, it can be deployed to Raspberry Pi Pico for real-time inference.
Distinction between Normal Cooking, Burning, and Idle Stove
Training the machine learning model to accurately distinguish between normal cooking, burning, and idle stove scenarios is crucial for effective fire detection. With the dataset collected from the sensors, Edge Impulse allows developers to build models that can analyze the Patterns and features in the sensor data, enabling precise classification of different kitchen stove conditions.
Deploying the Model to Raspberry Pi Pico
Flashing the Pre-built Firmware
The simplest way to deploy the trained machine learning model to Raspberry Pi Pico is by downloading the pre-built firmware from Edge Impulse Studio. The firmware can be easily flashed onto the board, enabling real-time data collection and inference. By connecting Raspberry Pi Pico to an SBC or PC, the pre-built firmware can be used directly.
Downloading and Using the C++ Library
Alternatively, developers can choose to download the C++ library or Arduino IDE library provided by Edge Impulse. This approach allows for greater flexibility and customization as developers can implement their own application logic directly on the microcontroller. By making use of the library, developers can acquire sensor data, preprocess it, and run the machine learning model on Raspberry Pi Pico independently.
Improving the Project
Exploring Other Sensor Combinations
To enhance the functionality and effectiveness of the kitchen fire detection system, it is worth exploring other sensor combinations. Consulting with experts in the field, such as chemists, can provide valuable insights into the most appropriate sensors or combinations of sensors for this specific task. Continual research and experimentation with different sensor setups can lead to better fire detection and prevention mechanisms.
Considering Different DSP Blocks for Event Detection
While the Current implementation of the project relies on the Flatten block for data processing, it may be worthwhile to explore other Digital Signal Processing (DSP) blocks for more accurate event detection. Different DSP blocks handle time-related information differently, allowing for the detection of sudden changes in data. For example, an alternative DSP block could be better suited for detecting the actual start of a kitchen fire.
Exploring Other Boards Compatible with Edge Impulse
Apart from the official Raspberry Pi Pico boards, there are several other boards available that are compatible with Edge Impulse. These boards offer additional features and variations, such as built-in Wi-Fi or Bluetooth connectivity and onboard sensors. Exploring and utilizing these boards can provide a wider range of options and functionalities for various projects, including kitchen fire detection.
Conclusion
In conclusion, the combination of Raspberry Pi Pico and Edge Impulse presents an exciting opportunity to revolutionize embedded systems and bring advanced machine learning capabilities to low-cost microcontrollers. By leveraging the features and integration options provided by both platforms, developers can build innovative projects, such as kitchen fire detection systems, that can significantly improve safety and prevent potential hazards. With the ease of use, extensive documentation, and strong community support, the possibilities for Raspberry Pi Pico and Edge Impulse are endless.
Highlights
- Raspberry Pi Pico offers a low-cost, high-performance solution for embedded systems.
- Integration with Edge Impulse allows for easy training and deployment of machine learning models.
- Pre-configured sensors and RP2040 ADC enable a wide range of sensor applications.
- Sensor fusion using VOC, temperature, and humidity sensors enhances kitchen fire detection.
- Edge Impulse Studio provides a user-friendly interface for model training and optimization.
- Pre-built firmware or C++ library options offer flexibility for deploying models to Raspberry Pi Pico.
FAQ
Q: What is Raspberry Pi Pico?
A: Raspberry Pi Pico is a microcontroller board released by the Raspberry Pi Foundation. It offers high performance at a low cost and is ideal for various embedded systems projects.
Q: Can I use sensors with Raspberry Pi Pico?
A: Yes, Raspberry Pi Pico supports a wide range of sensors. It comes with pre-configured sensors and also has an ADC for sampling analog signals from other sensors.
Q: How can I train and deploy machine learning models with Raspberry Pi Pico?
A: Edge Impulse Studio allows for easy training and deployment of machine learning models to Raspberry Pi Pico. It provides a comprehensive platform for data collection, model training, and performance evaluation.
Q: Can I customize the firmware on Raspberry Pi Pico?
A: Yes, Raspberry Pi Pico allows for firmware customization. You can download the pre-built firmware or use the C++ library provided by Edge Impulse to implement your own application logic.
Q: Can I use Raspberry Pi Pico for kitchen fire detection?
A: Yes, Raspberry Pi Pico, along with sensor fusion techniques and machine learning models, can be used to Create an effective kitchen fire detection system. By utilizing sensors like VOC, temperature, and humidity, it is possible to monitor stove conditions and detect potential hazards.
Resources
Raspberry Pi Pico Documentation
Edge Impulse
