The Arduino Nano 33 BLE Sense Rev2 incorporates a variety of technical specifications designed to support a wide range of applications, especially in embedded machine learning and Bluetooth® Low Energy connectivity. Here are the detailed specifications:

Specifications:

• Microcontroller: The heart of the Nano 33 BLE Sense Rev2 is the nRF52840 microcontroller, running on Arm® Mbed™ OS, which supports the development of complex applications, including AI projects using TinyML and TensorFlow™ Lite.
• Bluetooth® Module: It uses the powerful 2.4 GHz Bluetooth® 5 Low Energy module from u-blox (NINA B306), equipped with an internal antenna, facilitating robust data transmission between devices using the ArduinoBLE library.
• Operating Voltage : 3.3V
• Input Voltage (limit) : 21V
• DC Current per I/O Pin : 15 mA
• Clock Speed : 64MHz
• CPU Flash Memory : 1MB (nRF52840)
• SRAM : 256KB (nRF52840)
• Digital Input / Output Pins : 14
• PWM Pins : all digital pins
• Communication : UART , I2C , SPI
• Analog Input Pins : 8 (ADC 12 bit 200 k samples)
• Analog Output Pins : Only through PWM (no DAC)
• External Interrupts : all digital pins
• LED_BUILTIN : 13
• USB : Native in the nRF52840 Processor

Sensors: 

• Inertial Measurement Unit (IMU): The board features a 9-axis IMU for motion detection, orientation, and vibration analysis, comprising a 6-axis BMI270 and a 3-axis BMM150.
• Environmental Sensing: For environmental data, it includes the LPS22HB sensor for reading barometric pressure and the HS3003 sensor for measuring relative humidity and temperature.
• Proximity, Gesture, and Light: An APDS9960 sensor is integrated for digital proximity, ambient light, RGB, and gesture detection.
• Microphone: The board contains an MP34DT05 digital microphone for capturing and analyzing sound, enabling the development of voice interface projects.

Learning Resources:

Nano 33 BLE Sense Rev2

Getting started with the Arduino Nano 33 BLE Sense

Applications and Projects:

The Arduino Nano 33 BLE Sense Rev2 is versatile and can be used for various innovative projects. Here are some inspiring examples and applications:

• Voice Control: Utilize voice commands to control the board’s parameters such as speed, iterations, and color through offline voice AI capabilities.
• Magic Wand: Transform the board into an AI-powered magic wand to explore projects that feel straight out of the Harry Potter universe. This project demonstrates how to incorporate gesture control and machine learning for creative applications.
• Phyphox App Monitoring: Access data from the Nano BLE Sense via the Phyphox app, enabling remote sensor monitoring with a smartphone. This application highlights the board’s potential in remote data logging and monitoring.
• Color Detection: Engage in projects that require the Nano BLE Sense to identify different colors, showcasing the board’s use in applications that need environmental interaction and response.
• Gesture Recognition for Gaming: Train a TensorFlow Lite Micro Model to recognize gestures, enabling you to play games like Street Fighter with body movements. This project captures motion data from the board, imports it into TensorFlow to train a model, and deploys the classifier onto the board, exemplifying the board’s capability in embedded machine learning and gesture-based interaction.
• Controlling RGB LED Through Bluetooth®: Modify code examples to control the RGB LED on the board in different colors based on information sent through a smartphone. This demonstrates how to use the ArduinoBLE library for Bluetooth communication, offering a practical example of wireless device interaction.