Arduino Nano 33 BLE Sense Rev2

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85%
88%
Performance
85%
Ease of Use
91%
BLE Connectivity
72%
TinyML Integration
86%
Sensor Accuracy
89%
Build Quality
83%
Software Support
92%
Value for Money
90%
Size and Portability
88%
Compatibility with Mobile Devices
75%
Learning Curve
80%
Customer Support
More

Overview

The Arduino Nano 33 BLE Sense Rev2 is a compact and powerful microcontroller designed for IoT and wearable projects. This development board combines an array of integrated sensors with Bluetooth Low Energy (BLE) connectivity, making it ideal for building smart devices. Whether you're creating a simple motion detector or a more advanced AI-based wearable, this microcontroller offers an easy-to-use solution with minimal setup. Its small form factor and versatile features make it a popular choice for developers looking to bring ideas to life in a variety of applications.

Features & Benefits

This microcontroller offers an impressive set of specifications, including a 64 MHz clock speed, 1MB of flash memory, and 256KB of SRAM, ensuring efficient performance for demanding projects. It comes with an integrated microphone for audio processing and gesture recognition, along with multiple built-in sensors such as an IMU, temperature, humidity, pressure, and light sensors. With BLE connectivity, this board makes it easy to interface with smartphones, tablets, and other devices. Additionally, the support for TinyML and TensorFlow Lite allows developers to run machine learning models directly on the device.

Best For

The Arduino Nano 33 BLE Sense Rev2 is perfect for developers working on AI, IoT, or wearable technology projects. Its compact size makes it especially suitable for space-constrained environments, such as smartwatches or other wearable devices. Hobbyists looking to prototype smart home devices or environmental sensors will also appreciate its capabilities. Those interested in real-time AI applications, such as gesture recognition or environmental monitoring, will find the microcontroller's edge computing features particularly valuable, as it reduces reliance on cloud services.

User Feedback

Users consistently praise this development board for its versatility and ease of use in various projects. Many appreciate the seamless BLE connectivity, which allows for quick and reliable communication with mobile apps. However, some have mentioned that integrating TinyML models can be a bit challenging for beginners, requiring a steeper learning curve. Despite this, the overall satisfaction is high, with an impressive 4.6-star rating. Users also highlight the compact size, making it easy to integrate into tight spaces without sacrificing performance.

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Pros

  • This microcontroller is highly praised for its compact design, making it ideal for wearable and space-constrained projects.
  • Users appreciate the BLE connectivity, which ensures seamless integration with mobile devices for IoT applications.
  • The built-in sensors, including temperature and motion, provide versatility for real-time data collection and edge computing.
  • With 1MB of flash memory and 256KB SRAM, this development board offers efficient processing for various applications.
  • TensorFlow Lite compatibility enables smooth deployment of AI models for edge computing without relying on cloud services.
  • Many users find the setup process easy, making it accessible for both beginners and more experienced developers.

Cons

  • Some beginners struggle with the TinyML integration, as it can be complex without prior machine learning experience.
  • The 64 MHz clock speed may limit the microcontroller's ability to handle more demanding AI or real-time processing tasks.
  • A few users mentioned that the built-in sensors, while useful, may not offer the high precision required for certain professional-grade applications.
  • The small size, while beneficial for portability, can be a challenge when connecting to larger peripheral devices in complex projects.
  • A limited number of input/output pins may not meet the needs of users with more extensive connectivity requirements.
  • The microcontroller’s low voltage (3.3V) may be incompatible with some components that require a higher operating voltage.

Ratings

After analyzing verified user reviews from multiple countries, languages, and platforms, the ratings for the Arduino Nano 33 BLE Sense Rev2 reflect a balance of its strengths and areas for improvement. Spam, bot-generated, incentivized, and duplicated reviews were filtered out, ensuring that only authentic, high-quality feedback informed the results. While users generally praise its versatility, compact design, and BLE connectivity, there are some challenges reported in the integration of TinyML, especially for beginners. The ratings offer a clear view of how this microcontroller performs in real-world applications, covering a wide range of use cases and user experiences.

Performance
88%
Users appreciate the smooth performance of this microcontroller, especially in real-time AI applications and IoT projects. Its 64 MHz clock speed combined with efficient memory allows it to handle multiple sensors without lag, making it ideal for both beginner and advanced use cases.
Some buyers found the performance lacking in more complex tasks or when multiple sensors are used simultaneously. While it works well for basic projects, users seeking higher computational power for more demanding AI models may feel restricted.
Ease of Use
85%
The development board's ease of use is one of its standout features, with many users highlighting how quickly they were able to integrate it into their projects. The straightforward Arduino IDE setup and compatibility with TensorFlow Lite make it accessible for both new developers and experienced users.
While most find it user-friendly, some beginners report a steep learning curve when working with TinyML. The lack of in-depth tutorials for more advanced projects can make initial setup a bit challenging for those not already familiar with embedded development.
BLE Connectivity
91%
The BLE connectivity is widely praised for being stable and easy to set up. It enables seamless integration with mobile devices, which is a key advantage for IoT projects and applications that require remote monitoring or control via smartphones or tablets.
A small group of users noted occasional instability or slower data transmission when using BLE over long distances. However, these instances seem to be rare and likely depend on environmental factors or specific devices being used.
TinyML Integration
72%
28%
The integration of TinyML is highly appreciated by developers working on real-time AI applications. TensorFlow Lite support enables on-device machine learning, which is a powerful feature for edge computing, allowing processing without needing cloud resources.
However, many users find the integration challenging, especially beginners. The complexity of setting up and training models on the device often requires a steep learning curve and prior knowledge of machine learning frameworks, which may be off-putting for newcomers.
Sensor Accuracy
86%
The sensors built into the development board, such as the IMU, temperature, and light sensors, deliver accurate readings for most standard IoT applications. Many users find the sensor data reliable for projects like environmental monitoring or motion detection.
A few users noted occasional discrepancies in sensor readings, particularly with temperature and humidity sensors. While this doesn’t affect basic use, it may be problematic for more precise applications where high accuracy is required.
Build Quality
89%
The board's compact and durable build is appreciated by many users, especially those working in mobile or wearable tech. Its small form factor and lightweight design make it easy to incorporate into tight spaces without sacrificing strength or reliability.
A handful of users found that the microcontroller could be a bit fragile under physical stress or if not handled carefully. Some even mentioned minor issues with pin headers coming loose after multiple uses, though this isn't a common complaint.
Software Support
83%
The support for popular development platforms like Arduino IDE and TensorFlow Lite ensures that developers have access to a robust set of tools. The large community around this board offers plenty of libraries, resources, and example projects to get started.
While the software support is strong overall, some users have reported occasional bugs or issues with certain libraries. These issues can cause delays or frustration when working on more complex applications, especially if troubleshooting is required.
Value for Money
92%
The board provides excellent value for money, especially considering its range of built-in sensors, BLE connectivity, and support for TinyML. It's a versatile tool for anyone working in IoT or AI, offering high-end features at an affordable price point.
Although it offers great value for the features it provides, some users feel that the price could be lower for beginners or hobbyists who are just experimenting with basic projects. However, most agree the price is justified for those requiring advanced functionality.
Size and Portability
90%
The compact size of the board is ideal for projects with space constraints. It fits easily into small enclosures, making it a great choice for wearable tech or other mobile applications where size and weight are critical factors.
While many appreciate the portability, the small size can be a bit of a challenge for users with larger hands or those working on projects that require additional components. The small footprint limits the number of peripherals you can easily attach.
Compatibility with Mobile Devices
88%
The BLE functionality allows seamless integration with mobile devices, enabling remote control and monitoring of projects. This is especially useful for creating mobile applications for IoT devices, as users can quickly connect their smartphones for testing and deployment.
While the mobile device integration is generally smooth, some users mentioned that certain mobile apps or devices may experience connectivity issues, particularly with older smartphones or operating systems. These compatibility hiccups could cause some delays in development.
Learning Curve
75%
25%
The Arduino ecosystem, combined with the intuitive design of the board, makes it easier for developers familiar with the platform to get started quickly. It’s a great tool for prototyping and testing out ideas with a relatively low barrier to entry for those already experienced in embedded systems.
Newcomers to embedded systems and TinyML may find the learning curve steep. Some users struggled to understand advanced features like machine learning integration without prior knowledge, which could lead to frustration or slower progress on initial projects.
Customer Support
80%
20%
Many users have reported positive experiences with the customer support team, especially for resolving technical issues related to software setup and connectivity. The responsiveness and professionalism of the support team is noted as a strength.
However, there are occasional complaints about slow response times, particularly when reaching out for more complex troubleshooting. Some users feel that the support documentation could be more comprehensive, especially for beginners who may need more guidance.

Suitable for:

The Arduino Nano 33 BLE Sense Rev2 is an excellent choice for developers working on AI, IoT, and wearable technology projects. Its compact size and integrated sensors, including motion, temperature, and audio, make it a versatile tool for both prototyping and real-world applications. This microcontroller is ideal for hobbyists and professionals who need an easy-to-use platform for edge computing or integrating real-time AI, especially with the support for TinyML and TensorFlow Lite. Whether you're developing smart home devices, wearable gadgets, or environmental monitoring systems, this board offers the functionality and portability needed for space-constrained projects. Developers seeking BLE connectivity for seamless smartphone integration will also find it highly beneficial, making it suitable for mobile IoT applications.

Not suitable for:

The Arduino Nano 33 BLE Sense Rev2 may not be the best fit for users looking for a more powerful microcontroller for complex, high-performance AI applications. While it excels in small to mid-range projects, its 64 MHz clock speed and the complexity of TinyML integration can be limiting for advanced developers seeking high computational power. Beginners, in particular, might find the learning curve steep, especially when dealing with machine learning models or advanced sensor integration. Additionally, those requiring extremely precise sensor readings for highly sensitive applications, like industrial-grade monitoring, might encounter some inaccuracies with certain built-in sensors. Overall, users looking for a more robust, high-performance platform might want to explore other options.

Specifications

  • Microcontroller: Powered by the nRF52840 microcontroller for efficient processing and connectivity.
  • Clock Speed: Operates at a 64 MHz clock speed, providing ample processing power for a variety of tasks.
  • Memory: Includes 1MB of flash memory and 256KB of SRAM for efficient data storage and processing.
  • Bluetooth: Supports Bluetooth Low Energy (BLE) 5.0 for wireless communication with compatible devices.
  • Sensors: Equipped with an IMU, microphone, temperature, humidity, pressure, light, and color sensors.
  • Software Compatibility: Supports TensorFlow Lite for AI applications and is compatible with the Arduino IDE for development.
  • Power Supply: Operates at a 3.3V voltage, ensuring compatibility with a wide range of low-power devices.
  • Dimensions: Compact form factor measuring 2.36 x 0.79 x 0.16 inches, ideal for space-constrained projects.
  • Weight: Lightweight at just 0.317 ounces, making it suitable for wearable and portable applications.
  • Connectivity: Provides BLE connectivity for easy integration with smartphones, tablets, and other Bluetooth-enabled devices.
  • Operating Temperature: Designed to function in a wide temperature range, suitable for various environmental monitoring tasks.
  • Power Consumption: Low power consumption, making it ideal for battery-operated projects and IoT applications.
  • Edge AI: Supports edge AI computing, enabling real-time machine learning applications directly on the device.
  • Gesture Recognition: Integrated microphone supports audio processing and gesture recognition for interactive applications.
  • Compatibility: Compatible with a range of Arduino accessories and external components for flexible project building.
  • Custom Solutions: Ideal for building custom solutions, especially for professionals working on compact and efficient designs.
  • Development Tools: Compatible with popular development tools like TensorFlow Lite, making it easy to create AI-based projects.
  • Environmental Sensing: Built-in sensors allow for precise environmental sensing, such as temperature, humidity, and light levels.
  • Mounting Options: Small form factor and flexible I/O options allow for easy integration into various mounting setups.

FAQ

The Arduino Nano 33 BLE Sense Rev2 is a compact microcontroller designed for Internet of Things (IoT) and wearable tech projects. It includes built-in sensors like IMU, microphone, temperature, and light, and supports TinyML for running AI models at the edge.

To set up this development board, you’ll need to install the Arduino IDE, select the correct board and port, and then upload a basic sketch to get started. The board is compatible with TensorFlow Lite for machine learning projects, so make sure you have the necessary libraries installed.

This device comes with a variety of built-in sensors, including an IMU (inertial measurement unit) for motion tracking, a microphone for audio processing, and sensors for temperature, humidity, pressure, light, and color. These make it ideal for environmental monitoring and AI-based applications.

Yes, this Arduino board supports Bluetooth Low Energy (BLE), allowing for easy integration with smartphones and tablets. BLE connectivity enables wireless communication with mobile devices for real-time monitoring or control of your IoT projects.

The device is equipped with 1MB of flash memory and 256KB of SRAM, providing enough capacity for storing code and handling moderate processing tasks. This is sufficient for most AI applications and IoT projects, but may be limiting for more complex or memory-intensive tasks.

The Arduino Nano 33 BLE Sense Rev2 is versatile enough for both beginners and advanced users. Its compact design and simple setup make it easy for newcomers, while its support for TensorFlow Lite and various sensors gives experienced developers the flexibility to build more advanced projects.

Yes, this microcontroller is designed to support edge computing with TensorFlow Lite, making it ideal for running machine learning models directly on the device. This allows for real-time AI processing without needing a connection to the cloud.

Absolutely! The small size of this device (only 2.36 x 0.79 x 0.16 inches) makes it an excellent choice for wearable projects, where space is limited. Its built-in sensors for motion and temperature are especially useful for fitness trackers and health monitoring applications.

While the Arduino Nano 33 BLE Sense Rev2 supports TensorFlow Lite for edge AI applications, its 64 MHz clock speed and limited memory (256KB SRAM) may be a bottleneck for more complex machine learning models or real-time processing tasks. It’s best suited for smaller models or less resource-intensive applications.

Some users have mentioned that integrating TinyML can be tricky for beginners, especially when working with more complex models. Additionally, users have reported challenges with the 3.3V operating voltage, as some components require higher voltages. Make sure to check voltage compatibility with your other components before use.