In recent years, machine learning is a broad field that has seen tremendous progress. It is based on the principle that a computer can autonomously improve its own performance on a given task by learning from data – sometimes even beyond the capabilities of humans. Machine learning’s applications include but not limited to image classification, object detection, audio scene, speech recognition, weather or stock market forecasting, and anomaly detection.
Edge AI refers to the use of artificial intelligence in the form of machine learning algorithms running directly on edge devices instead of the cloud. Bringing AI from the cloud to the edge, it can not only reduce the requirement for latency, bandwidth, and power consumption (which means cost down) but also increase data security and reliability of the whole IoT system.
As machine learning develops, many exciting possibilities will now extend to edge devices as well. Thanks to the TinyML, a subset of machine learning that employs optimization techniques to reduce the computational space and power required by machine learning models, Edge AI is fitting artificial intelligence applications into smaller and less powerful computers, even ultra-low-power microcontrollers, such as our Wio Terminal.
Wio Terminal is an ATSAMD51-based microcontroller with wireless connectivity supported by Realtek RTL8720DN. It is equipped with a 2.4” LCD Screen, onboard IMU(LIS3DHTR), Microphone, Buzzer, microSD card slot, Light sensor, and Infrared Emitter(IR 940nm).
Now, Wio Terminal is officially supported by Edge Impulse, a leading development platform that enables developers to create the next generation of intelligent edge device solutions with embedded Machine Learning. Let’s explore how the community solves real problems using Machine learning with Wio Terminal at the very edge.
Applications of Wio Terminal x Edge Impulse (Updating)
Benjamin Cabé applied Wio Terminal with artificial intelligence and tinyML into an AI Nose that detects surrounding smells. It can be used for a wide variety of applications, from helping folks suffering from anosmia to spot the smell of burning food or spoiled milk, to monitoring the cleanliness of office buildings, etc.
While Wio Terminal makes for a fine IoT development kit, Shawn tested it to run embedded machine learning applications (TinyML). Specifically, he tested its ability to handle speech recognition (or more specifically, keyword spotting). He trained a convolutional neural network to identify 4 keywords: forward, left, right, stop. These words were chosen to demonstrate what you might find on something like a robot. However, any words could have been chosen (within some limits). It seems that the neural network gets a little too big or inaccurate after about 5 different keywords.
A digital stethoscope that auscultates and detects abnormalities in the respiratory system using tinyML at the edge.
This project tutorial shows how you can build a machine learning-based handwriting recognition device with the Wio Terminal, Edge Impulse, and Arduino. Follow this detailed guide to learn how a single time of flight sensor can allow you to recognize handwriting gestures and translate them to text!
Project 5: Tiny Maching Learning Capabilities to Wearable IoT Devices for Boxing Technique Management Anthony Joseph
Anthony Joseph, CTO of My House Geek, has recently presented a micro-sized, wearable device designed to improve your boxing skills. With the goal of reinforcing good technique in mind, Joseph designed a device that is capable of detecting when one hand is in a blocking position, while the other hand is throwing a punch.
Typically, a weather station project would involve connecting the sensor to our microcontroller platform and reading out this data in real time, either on an LCD display or over an internet connection to an online database. Today, I want to share the smart version of this weather station project that I created by adding the capability for our Wio Terminal to predict the current weather conditions. With an onboard TensorFlow Lite model, the Wio Terminal becomes able to use real time temperature and relative humidity data for making half-hourly weather predictions!
“In this wiki, we will introduce how to use Wio Terminal with Edge Impulse to simply deploy a machine learning project. Wio Terminal with the Grove systems can be very powerful, which brings hundreds of sensor data in for analysis and to possible evaluate different scenarios!”
“In this project we will create a people counting system by using Wio Terminal, an ordinary Ultrasonic ranger and special Deep Learning sauce to top it off and actually make it work. We will also utilize Microsoft Azure IoT Central service to store the room occupancy data in the cloud and visualize it on PC.”
“In this tutorial, I’ll teach you how to train and deploy an audio scene classifier with Wio Terminal and Edge Impulse. For more details and video tutorial, watch the corresponding video!”
Wio Terminal Free Trial
Bonus time! We’re launching a Wio Terminal Free Trial Campaign now, share with us your creative AIoT projects and get yourself a Wio Terminal for free! And if you’d love to get started with TinyML on Wio Terminal, you could also check out our seried beginner-friendly tutorials and online courses.