Rapidly Detect Changes in Your Environment with Edge Impulse and Nordic Thingy:91

Earlier this year, the Edge Impulse team created a crowdsourced project that enables the Nordic Semiconductor Thingy:91 to detect the device’s surrounding environmental conditions using its onboard sensors, including a temperature, pressure, humidity, and a gas result reading. The project enabled the Thingy:91 to detect whether the device was physically indoors or outdoors (or inside/outside).

By collecting data from a large number of varying environments, from multiple locations around the world from our various team members, the Nordic Thingy:91 is a powerful device for machine learning inference results for physical world sensor data. The ML predictions generated by the Edge Impulse SDK can also be wirelessly sent over Bluetooth connection to a results dashboard to rapidly view changes in the device’s environmental state, whether inside a building, outside or more!

The Nordic Thingy:91 is an easy-to-use battery-operated prototyping platform for cellular IoT using LTE-M, NB-IoT, and GPS. It is ideal for creating proof of concepts, demos, and initial prototypes in your cIoT development phase. The Thingy:91 is built around the nRF9160 SiP and is certified for a broad range of LTE bands globally, meaning the Nordic Thingy:91 can be used just about anywhere in the world. There is an nRF52840 multiprotocol SoC on the Thingy:91. This offers the option of adding Bluetooth Low Energy connectivity to your project.

How can I get started?

  1. Sign up for an Edge Impulse account and create a new project.
  2. Follow along with the getting started guide to set up your Nordic Thingy:91 with Edge Impulse.
  3. If you’re collecting data with your team or colleagues, check out our blog post on utilizing the power of the crowd and your peer network with their own Nordic Thingy:91’s.
  4. Or, clone the project the Edge Impulse team has already created into your own Edge Impulse account.
  5. Once you have collected your data and trained your machine learning model (or Impulse), following along with the Nordic Thingy:91 deployment guide to retrieve model inferencing results in real-time!

We’d love to see what you create with the Edge Impulse Studio for the Nordic Thingy:91 (or Thingy:53), share your project with us on social media @NordicTweets and @EdgeImpulse!



Are you interested in bringing machine learning intelligence to your devices? We're happy to help.

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