Arduino Nano 33 BLE Sense Rev2
An AI enabled board in the shape of the classic Nano board, with all the sensors to start building your next project right away.
Overview
The Arduino Nano 33 BLE Sense Rev2 is Arduino’s 3.3V AI enabled board in the smallest available form factor with a set of sensors that will allow you without any external hardware to start programming your next project, right away.
With the Arduino Nano 33 BLE Sense Rev2, you can:
- Build wearable devices that using AI can recognize movements.
- Build a room temperature monitoring device that can suggest or modify changes in the thermostat.
- Build a gesture or voice recognition device using the microphone or the gesture sensor together with the AI capabilities of the board.
The main feature of this board, besides the complete selection of sensors, is the possibility of running Edge Computing applications (AI) on it using TinyML. Learn how to use the Tensor Flow Lite library following this instructions or learn how to train your board using Edge Impulse.
Tech specs
Microcontroller |
nRF52840 (datasheet) |
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) |
EEPROM |
none |
Digital Input / Output Pins |
14 |
PWM Pins |
all digital pins |
UART |
1 |
SPI |
1 |
I2C |
1 |
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 |
IMU |
|
Microphone |
MP34DT06JTR (datasheet) |
Gesture, light, proximity, color |
APDS9960 (datasheet) |
Barometric pressure |
LPS22HB (datasheet) |
Temperature, humidity |
HS3003 (datasheet) |
Conformities
Resources for Safety and Products
Manufacturer Information
The production information includes the address and related details of the product manufacturer.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/
Responsible Person in the EU
An EU-based economic operator who ensures the product's compliance with the required regulations.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc
Documentation
SCHEMATICS IN .PDFDATASHEET IN .PDF
Download the full Pinout diagram as PDF here.
Learn more
Get Inspired
An intelligent device to track moves with responses during an interactive space with mapping, backlight, music and smart sculptures. This project makes use of a machine learning algorithm capable of tracking and detecting moves to identify associated gesture recognition through a microcontroller. Smart sculptures, lighting, music and video projection to trigger with each assigned gesture, creating a powerful AV experience highlighting the incredible potential of TinyML for the performing arts. This allows the corresponding media set Tiny ML in interactive to play when the right move was made because all these elements interact to create a new experience. This allows us to create Interactive installations, these sculptures use a combination of motors, sensors, and other electronics to create an immersive and interactive experience for the viewer. They may include projections, sound, and other sensory elements to create a complete experience.
With an array of onboard sensors, Bluetooth® Low Energy connectivity, and the ability to perform edge AI tasks thanks to its nRF52840 SoC, the Arduino Nano 33 BLE Sense is a great choice for a wide variety of embedded applications. Further demonstrating this point, a group of students from the Introduction to Embedded Deep Learning course at Carnegie Mellon University have published the culmination of their studies through 10 excellent projects that each use the Tiny Machine Learning Kit and Edge Impulse ML platform. Wrist-based human activity recognition Traditional human activity tracking has relied on the use of smartwatches and phones to recognize certain exercises based on IMU data. However, few have achieved both continuous and low-power operation, which is why Omkar Savkur, Nicholas Toldalagi, and Kevin Xie explored training an embedded model on combined accelerometer and microphone data to distinguish between handwashing, brushing one’s teeth, and idling. Their project continuously runs inferencing on incoming data and then displays the action on both a screen and via two LEDs. Categorizing trash with sound In some circumstances, such as smart cities or home recycling, knowing what types of materials are being thrown away can provide a valuable datapoint for waste management systems. Students Jacky Wang and Gordonson Yan created their project, called SBTrashCat, to recognize trash types by the sounds they make when being thrown into a bin. Currently, the model can three different kinds, along with background noise and human voices to eliminate false positives. Distributed edge machine learning The abundance of Internet of Things (IoT) devices has meant an explosion of computational power and the amount of data needing to be processed before it can become useful. Because a single low-cost edge device does not possess enough power on its own for some tasks, Jong-Ik Park, Chad Taylor, and Anudeep Bolimera have designed a system where
FAQs
What is the difference between Rev1 and Rev2?
There has been some changes in the sensor between both revisions:
- Replacement of IMU from LSM9DS1 (9 axis) for a combination of two IMUs (BMI270 - 6 axis IMU and BMM150 - 3 axis IMU).
- Replacement of temperature and humidity sensor from HTS221 for HS3003.
- Replacement of microphone from MP34DT05 to MP34DT06JTR.
Additionally some components and the changes have been done in order to improve the experience of the users:
- Replacement of power supply MPM3610 for MP2322.
- Addition of VUSB soldering jumper on the top side of the board.
- New test point for USB, SWDIO and SWCLK.
Do I need to change my sketch used in the previous revision?
For sketches done using the libraries like LSM9DS1 for the IMU or HTS221 for the temperature and humidity sensor, for the new revision this libraries must be changed to Arduino_BMI270_BMM150 for the new combined IMU and Arduino_HS300x for the new temperature and humidity sensor.