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People Counting System built with Wio Terminal and Ultrasonic Sensor Custom PCB nebulizer pcba computer pcba customize

People Counting System built with Wio Terminal and Ultrasonic Sensor Custom PCB nebulizer pcba computer pcba customize

Regular price $9.00 USD
Regular price $49.00 USD Sale price $9.00 USD
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With TinyML running right on the ATSAMD51 powered Wio Terminal, we can train a machine learning model on distance patterns to recognize when people are moving in or out of a room.

With some additional programming to keep count, and using the Wio Terminal's onboard WiFi , we can easily build a cloud-connected application to monitor room occupancy remotely with IoT platforms like Azure IoT Central.

 

Feature

  • Powered by TinyML, train machine learning model on distance patterns

  • Build system and learn TinyML easily with step by step tutorial

  • Plug and Play Grove Sensors

  • Gather the data through Edge Impulse 

  • Use continuous inference example to make sure not missing any important data.

  • Store the room occupancy data in the cloud and visualize it on PC

  • Connect to Azure IoT Central, watch the detailed progress feedback on the Serial Terminal




Description 
 

An ordinary ultrasonic ranger can easily measure changes in distance to obstacles, but what about complex real-world tasks like people counting?

 

Well, with TinyML running right on the ATSAMD51 powered Wio Terminal, we can train a machine learning model on distance patterns to recognize when people are moving in or out of a room!

 

With some additional programming to keep count, and using the Wio Terminal's onboard WiFi &  we can easily build a cloud-connected application to monitor room occupancy remotely with IoT platforms like Azure IoT Central!

 

Ultrasonic Distance Sensor is an ultrasonic transducer that utilizes ultrasonic waves to measures distance. It can measure from 3cm to 350cm with an accuracy of up to 2mm. We can utilize the ultrasonic sensor to determine the direction of objects. What if you want to train a model to detect walk-in and walk out of the room? Let’s create a new project on Edge Impulse Dashboard and prepare to get the data. For gathering the data, since we don’t need very high sampling frequency, we can use a data forwarder tool from edge-impulse-cli. Upload the ei_people_counter_data_collection.ino script (Please follow up this guide and upload the script in the article) to Wio Terminal – to learn more about how to set up edge-impulse-cli and data forwarder protocol, watch the first video of TinyML series.

 

For your application, you might need to set this value lower or higher, depending on the setup. Then start walking.

 

 

 

 

 
 
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