
Grove - Hall Sensor
Sold outThe Hall sensor uses the Hall Effect, which is the production of a voltage difference across an electrical conductor, transverse to an electric current in the conductor and a magnetic field perpendicular to the current.
Overview
There is a continuous-time switch on this Grove module. The output from the module switches from low (turns on) when a magnetic field (south polarity) is perpendicular to the Hall sensor and when it passes the operate point threshold BOP it switches to high (turns off) when the magnetic field disappears.
The twig can for example be used to measure RPM of a wheel or a motor.
Features
- Grove Compatible Interface
- 400ns transition period for rise and fall.
- Continuous-time hall effect sensor
- Reverse current protection
Tech specs
Specifications
Item |
Min |
Typical |
Max |
Unit |
Supply Voltage |
3.8 |
5.0 |
24 |
V |
Supply Current |
4.1 |
- |
24 |
mA |
Operating Temperature |
-40 |
- |
85 |
ºC |
Get Inspired

This project shows how to create a security system using the camera of an Arduino Nicla Vision board. The system automatically triggers a camera snapshot when presence is detected. Presence is detected when the system detects a sound level that exceeds a configurable threshold. The whole system is controlled by an Arduino Cloud dashboard.

Shortly after attending a recent tinyML workshop in Sao Paolo, Brazil, Joao Vitor Freitas da Costa was looking for a way to incorporate some of the technologies and techniques he learned into a useful project. Given that he lives in an area which experiences elevated levels of pickpocketing and automotive theft, he turned his attention to a smart car security system. His solution to a potential break-in or theft of keys revolves around the incorporation of an Arduino Nicla Vision board running a facial recognition model that only allows the vehicle to start if the owner is sitting in the driver’s seat. The beginning of the image detection/processing loop involves grabbing the next image from the board’s camera and sending it to a classification model where it receives one of three labels: none, unknown, or Joao, the driver. Once the driver has been detected for 10 consecutive seconds, the Nicla Vision activates a relay in order to complete the car’s 12V battery circuit, at which point the vehicle can be started normally with the ignition. Through this project, da Costa was able to explore a practical application of vision models at-the-edge to make his friend’s car safer to use. To see how it works in more detail, you can check out the video below and delve into the tinyML workshop he attended here.