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Nicla Sense ME

SKU ABX00050 Barcode 7630049202849 Show more
Original price €0
Original price €82,82 - Original price €82,82
Original price
Current price €82,82
€82,82 - €82,82
Current price €82,82
VAT included

Bring smart sensing solutions to the edge, with the high-performance, low-power board that packs state-of-the-art Bosch Sensortec technology into our smallest form factor yet.

Overview

The Nicla Sense ME is a tiny, low-power tool that sets a new standard for intelligent sensing solutions. With the simplicity of integration and scalability of the Arduino ecosystem, the board combines four state-of-the-art sensors from Bosch Sensortec:

  • BHI260AP motion sensor system with integrated AI
  • BMM150 magnetometer
  • BMP390 pressure sensor
  • BME688 4-in-1 gas sensor with AI and integrated high-linearity, as well as high-accuracy pressure, humidity and temperature sensors. 

Designed to easily analyze motion and the surrounding environment – hence the “M” and “E” in the name – it measures rotation, acceleration, pressure, humidity, temperature, air quality and CO2 levels by introducing completely new Bosch Sensortec sensors on the market.

Its tiny size and robust design make it suitable for projects that need to combine sensor fusion and AI capabilities on the edge, thanks to a strong computational power and low-consumption combination that can even lead to standalone applications when battery operated.

Part of Arduino Pro’s new Nicla family of modular, intelligent products that are easy to use, cost effective, versatile and accessible, the Sense ME has a new, tiny form factor that is also compatible with the Arduino MKR and Portenta ranges.

Key benefits
  • Tiny size, packed with features
  • Low power consumption
  • Add sensing capabilities to existing projects
  • When battery-powered, becomes a complete standalone board
  • Powerful processor, capable of hosting intelligence on the Edge
  • Measures motion and environmental parameters
  • Robust hardware including industrial-grade sensors with embedded AI
  • BLE connectivity maximizes compatibility with professional and consumer equipment
  • 24/7 always-on sensor data processing at ultra-low power consumption
Need Help?

Check the Arduino Forum for questions about the Arduino Language, or how to make your own Projects with Arduino. If you need any help with your board, please get in touch with the official Arduino User Support as explained in our Contact Us page.

Warranty

You can find your board warranty information here.
 


Tech specs

Microcontroller

64 MHz Arm® Cortex M4 (nRF52832)

Sensors

BHI260AP - Self-learning AI smart sensor with integrated accelerometer and gyroscope, BMP390 - Digital pressure sensor, BMM150 - Geomagnetic sensor, BME688 - Digital low power gas, pressure, temperature & humidity sensor with AI

I/O

Castellated pins with the following features: 1x I2C bus (with ext. ESLOV connector), 1x serial port, 1x SPI, 2x ADC , programmable I/O voltage from 1.8-3.3V

Connectivity

Bluetooth® 4.2

Power

Micro USB (USB-B), Pin Header, 3.7V Li-po battery with Integrated battery charger

Memory

512KB Flash / 64KB RAM, 2MB SPI Flash for storage, 2MB QSPI dedicated for BHI260AP

Interface

USB interface with debug functionality

Dimensions

22,86 mm x 22,86 mm

Weight

2 g

Conformities

The following Declarations of Conformities have been granted for this board:
CE
FCC
UKCA
REACH
WEEE
For any further information about our certifications please visit docs.arduino.cc/certifications

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

Pinout Diagram

Download the full pinout diagram as PDF here.

 

Get Inspired

PROJECT HUB
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Tiny ML in interactive spaces Arduino X K-WAY Challenge Project
Project Tutorial by fullmakeralchemist

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.

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BLOG
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
These projects from CMU incorporate the Arduino Nano 33 BLE Sense in clever ways
May 22, 2023

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

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