2022 Community Projects Collection of Seeed Studio XIAO Series

Good News! Now we provide free shipping for XIAO RP2040, Seeed Studio XIAO, and XIAO SAMD21!

Seeed Studio XIAO is a series of thumb-sized development boards powered by powerful and popular chips, such as SAMD21, RP2040, nRF52840, and ESP32C3. Its compact design makes it suitable for small and flexible wearable devices, IoT/TinyML AI applications, and Micro keyboard projects. In addition, all SMD components are placed on the same side of the board, so dLog UV & weather data on an SD card to train an Edge Impulse model. Then, run it to get informed of sun damage over BLE via an Android app.esigners can easily integrate XIAO into their own boards for rapid mass production.

Since its release, it has been applied to many fantastic and valuable projects and inspires the community to create more creative projects. In this blog, we’d like to recap these projects in an attempt to ignite the sparkles of creation.

IoT/TinyML AI applications

TinyML Made Easy: Anomaly Detection & Motion Classification by Marcelo Rovai

Machine Learning algorithms can run on microcontrollers and sensors themselves using very little power, interpreting much more of those sensor data that we are currently ignoring. This is TinyML, a new technology that enables machine intelligence right next to the physical world. Marcelo believes that TinyML can have many exciting applications for the benefit of society at large.

The Seeed XIAO BLE Sense is a giant tiny device! It is powerful, trustworthy, not expensive, low power, and has suitable sensors to be used on the most common embedded machine learning applications.
by Marcelo

In this project, Marcelo trains a TinyML Motion Classification model and connects it with real-life applications in transport. Different motion states and directions can reflect where the palettes may be. The accelerator and gyroscope on XIAO BLE Sense can help collect the data and then Marcelo classify four states based on these data:

Maritime (palettes in boats)
Terrestrial (palettes in a Truck or Train)
Lift (Palettes being handled by Fork-Lift)
Idle (Palettes in Storage houses)

TinyML Made Easy: Sound Classification (KWS) by MJRoBot (Marcelo Rovai)

In this tutorial, Marcelo explored Embedded Machine Learning, or simply, TinyML, running on the robust and still very tiny device, the Seed XIAO BLE Sense. Besides installing and testing the device, he also explored motion classification using real data signals from its onboard accelerometer. He use the same XIAO BLE Sense to classify sound, explicitly working as “Key Word Spotting” (KWS). A KWS is a typical TinyML application and an essential part of a voice assistant.

Pet Activity Tracker using XIAO BLE Sense & Edge Impulse by Mithun Das

Why should humans have all the fitness trackers? Our pets deserve more to stay active. A tinyML model predicts our pets’ activities based on the data collected from 6-axis IMU in XIAO BLE Sense. Our pets’ activities like rest, walking, and running can be monitored via phone. The accompanying mobile app connects to the device over Bluetooth and XIAO BLE SENSE sends prediction data every minute. Data is stored on mobile local storage and plotted on graphs to provide meaningful insight.

ML Anomaly Detection in Elevators w/ Edge Impulse & Notecard by Ivan Arakistain

Commercial elevator reliability is a key factor in the flow of people and products through a building. Improperly maintained elevators impact public safety, productivity, energy consumption, and quality of life. Non-working elevators also adversely impact people with disabilities and the elderly. By using XIAO BLE SENSE and other IoT devices for predictive maintenance, businesses can ensure consistent elevator performance to reduce downtime and save money on costly repairs.

TinyML with the Seeed XIAO – Part 1 and 2 by Jim Bennett

AI is no longer stuck in the cloud. Instead of relying on powerful computers with GPUs, the past few years have brought AI to small devices thanks to TinyML – machine learning models that can run on microcontrollers. You may already have devices that use this around you, from voice-controlled smart speakers to fitness trackers.

In this 2-part show, Jim gets his hands dirty with TinyML, building out a fitness tracker that can distinguish between rowing and running using a small microcontroller from Seeed studios – the Seeed XIAO BLE. Training and deploying these models is super complicated, so Jim enlists help from Edge Impulse, an online tool for capturing training data and building TinyML models.

Once Jim has his model, he will deploy it to his device using VS Code and PlatformIO, an extension for VS Code to do microcontroller development. From there he’ll deal with one of the complexities of building low-powered fitness trackers, connectivity. A lot of fitness trackers use Bluetooth to sync with a phone app, so Jim will take this route, syncing fitness data to Azure IoT Central from a mobile app.

Edge Impulse gesture recognition demo on XIAO BLE Sense by cnxlinux

Wearable Devices

XIAO BLE Sense Powered 3D printed IcosaLEDron by Jason Coon

Jason puts an XIAO BLE Sense & battery inside the 3D printed IcosaLEDron. Running the same code I used in my Fibonacci64 Nano build, where I use the 6-axis inertial measurement unit (IMU) to measure yaw, pitch & roll.