6 Popular ESP32 AI Applications Using TinyML in 2024

As we advance into 2024, the integration of TinyML with the ESP32 microcontroller is revolutionizing IoT solutions, offering cutting-edge advancements in smart technology. In this article, we delve into popular TinyML applications powered by the versatile ESP32, utilizing the Edge Impulse platform. From enhancing everyday devices with artificial intelligence to creating sustainable, energy-efficient solutions, we showcase practical implementations that demonstrate the transformative potential of these technologies. Whether you're an IoT developer or a tech enthusiast, discover how the combination of ESP32 and TinyML can unlock new possibilities in the world of smart technology.

Technical Overview and Advantages

TinyML

TinyML is a machine learning technology designed specifically for micro devices, enabling low-power devices to perform efficient data processing and analysis tasks. This technology is particularly suited for battery-powered devices and offers the following significant advantages:

• Energy Efficiency: TinyML algorithms are optimized to run on microcontrollers with only a few hundred KB of memory, significantly reducing energy consumption. This allows devices to operate for years without frequent battery replacement.
• Enhanced Autonomy: This technology is ideal for privacy-sensitive applications. For example, it processes health monitoring data directly on the user’s device without transmitting data to the cloud, thus protecting user privacy and enhancing data security.

ESP32 Microcontroller

The ESP32 is a low-cost, high-performance microcontroller that stands out in the smart device market for its versatility and cost-effectiveness. The primary reasons for choosing the ESP32 as hardware include:

• Low Power Design: The ESP32 incorporates advanced energy-saving technologies, including multiple low-power sleep modes and a power management unit, making it ideal for battery-powered applications due to its minimal energy consumption during extended operation.
• Multifunctional Interfaces: The ESP32 not only supports Wi-Fi and Bluetooth but also seamlessly connects with various sensors via low-energy Bluetooth (BLE), providing powerful data processing capabilities for applications such as smart homes and health monitoring.
• Cost-Effective: Compared to other similar products, the ESP32 offers a lower unit cost, significantly reducing the overall project budget when deployed on a large scale.

By combining these technologies, developers can create intelligent and energy-efficient applications, driving the widespread adoption of smart technology across various industries and laying the foundation for a true AIoT (Artificial Intelligence of Things) revolution. The next section will explore specific case studies of these technologies in practical applications on the Edge Impulse platform, demonstrating how theory can be translated into action to unlock new possibilities in smart technology.

Application Case Studies

1. Environmental Monitoring

Electronic Nose for Air Quality Detection

By utilizing ESP32 and TinyML technology, an intelligent electronic nose can be created to detect various gases and air pollutants, making it ideal for air quality monitoring in industrial areas or urban environments. The DFRobot community features a similar electronic nose project that demonstrates the use of MEMS gas sensors combined with ESP32 to differentiate between different types of drinks and fruits. This electronic nose device can identify different smells and volatile organic compounds by detecting unique combinations of gases, thereby facilitating air quality detection in practical applications.

In this project, the ESP32 is responsible for data acquisition, and the data captured by the MEMS sensors are processed to train machine learning models, supported by TinyML technology. The trained model is then deployed back to the ESP32, enabling continuous gas detection and classification. This integrated application is not only suitable for industrial environment monitoring but can also be extended to quality control in the food and beverage industry.

Figure: Electronic Nose System Based on ESP32

Wildfire Detection System

In environmental protection projects, ESP32 and TinyML also show great potential. The wildfire detection system uses these technologies to monitor environmental conditions and detect early signs of wildfires, potentially saving lives and reducing property loss. The system integrates temperature sensors, smoke sensors, and optical sensors to monitor the environmental conditions of forests in real-time. Once abnormalities such as a sharp rise in temperature or an increase in smoke concentration are detected, the system triggers an alarm.

With the support of TinyML, the ESP32 can process sensor data and perform real-time data analysis and model training, ensuring accurate identification of early signs of wildfires. The trained model is then deployed on the device, enhancing monitoring and response efficiency.

In a related project, developers used the ESP32-CAM for object detection. This project demonstrated how to collect and label image data with the ESP32-CAM and use the Edge Impulse platform to train a neural network model. After training, the model was exported as an Arduino library and deployed on the ESP32-CAM to achieve real-time detection functionality. Although this project mainly targets object detection, its methods and technologies can be applied to the development of a wildfire detection system.

Figure: Object Detection Using ESP32-EYE with Edge Impulse and TinyML

2. Health and Safety

Gesture Recognition in Wearable Devices

Using TinyML technology, ESP32-based wearable devices can achieve complex gesture recognition functions, which are particularly important in health monitoring and safety applications. By training machine learning models to recognize specific gestures, such as falls or abnormal movements, the device can alert caregivers in time to provide immediate medical assistance. Additionally, gesture recognition technology can be used to control smart home devices, such as lights and TVs, enhancing user interaction and improving the convenience of life.

An example project is the implementation of gesture recognition using the ESP32 development kit and PlatformIO on the neural networks designed in Edge Impulse. This project is particularly suitable for developers who wish to run machine learning models directly on the ESP32 without other sensor drivers. Developers can access this project tutorial for detailed implementation information and code.

Gesture Classification

Building on gesture recognition, further gesture classification functionality allows the device to distinguish and respond to different gesture commands. For example, waving a hand might turn off the lights, while swiping a palm could adjust the volume. Each gesture corresponds to a specific command, and through precise model training, the ESP32 can recognize these different gestures and perform corresponding actions.

In the process of implementing gesture classification, data processing and model optimization using TinyML are key. By continuously collecting user gesture data and conducting analysis and training on the Edge Impulse platform, the final model can run efficiently on the ESP32, ensuring quick and accurate responses.

The tutorial “Gesture Classification Using ESP32 and TinyML” details how to use ESP32 and TinyML for gesture classification. In this project, developers used TensorFlow Lite to program the ESP32, achieving classification and response to different gestures. This project not only demonstrates the practical application of gesture recognition technology but also provides step-by-step guidance to help developers understand and implement gesture recognition functionality.

Figure: Gesture Recognition with ESP32 and TinyML

Predictive Maintenance in Industry

In industrial environments, using ESP32 and Edge Impulse for predictive maintenance is an efficient choice. This technology can predict potential failures by continuously monitoring data collected from various sensors (such as vibration and temperature sensors), thereby reducing downtime and maintenance costs. This involves analyzing critical indicators such as vibration patterns and temperature changes of equipment to maintain optimal operational efficiency.

Specifically, there is an example of a predictive maintenance project using Arduino Nano 33 BLE Sense for sound classification. This project uses sound sensors to detect abnormalities in running motors to prevent failures. This approach relies on continuous monitoring of equipment operating sounds and utilizes trained machine learning models to identify potential anomalies, enabling maintenance personnel to address issues before they become serious faults.

These case studies demonstrate how traditional sensor technology can be combined with modern machine learning algorithms to predict potential fault points through continuous data monitoring and analysis, providing effective technical support for predictive maintenance in industrial applications.

Figure: Using Sensor or Audio Data for Equipment Predictive Maintenance

3. Smart Home Automation

In the field of smart home automation, combining TinyML with ESP32 can achieve various innovative applications, such as voice-activated devices and automated pet care systems. These applications not only enhance the convenience of households but also provide increased support for specific groups, such as the elderly and disabled.

Voice-Activated Devices

Using ESP32, you can build a smart home voice assistant capable of responding to voice commands to control lights, appliances, and more. For example, with simple voice commands, you can operate home devices without touching any physical switches, which is especially useful for people with mobility impairments. A specific project example is using ESP32 and the ESP RainMaker platform to not only control LED lights via voice but also monitor environmental variables such as temperature and humidity, further enhancing the smart home automation experience.

Figure: ESP32-S3-BOX Voice Assistant

Conclusion

The combination of TinyML and ESP32 microcontrollers not only showcases the innovative potential of intelligent technology but also demonstrates how practical and sustainable solutions can be promoted across various industries. This article highlights multiple practical applications, from environmental monitoring to smart home automation, showing how these technologies can be translated into action and have a profound impact on our daily lives.

By continuously exploring and applying these cutting-edge technologies, developers and tech enthusiasts can create efficient and intelligent solutions and prepare for the true era of AIoT (Artificial Intelligence of Things). As technology continues to advance, the combination of ESP32 and TinyML will continue to unlock new possibilities, driving the widespread application of smart technology and making our world more intelligent and connected.