Lesson to all the student tragic in this course, we have already learned how to use Edge Impulse and
TensorFlow Lite for microcontrollers to train and deploy highly optimized models for inference with
real terminology, right?
You also should have understanding of how to combine model inference with integration to cloud services
and graphical user interface.
But of course, learning and making doesn't stop here for the last lesson of the course.
We encourage you to think outside of the box and create a project of your own number of projects with
tiny email, a growing day by day and below are some of the most interesting ones our team found on
the internet.
Here we have community projects, dynamical water sensor based on edge impulse and Arduino Sense, though
that's amazing right now.
You can find this one on this link.
Now this is actually an implementation to identify running water, tap sound and once heard a buzzer.
Plus a LED timer is triggering.
The device is listening continuously for any sound, and once a running tape is identified, a timer
is triggered to produce 20 seconds buzzing sound and a flashing LED light.
The project is based on a two components of Arduino, Nanotech, three Belizeans and edge impulse to
as a developing platform.
Now that sounds amazing, right?
Another is another community projects is a tiny amount keyword detection for controlling IGB lights.
Now that's interesting.
Do you somehow train a TensorFlow model to recognize certain key routes and control an RGV light strip
using and Arduino Nano 33 a license?
Here is an easy G Analyzer powered by edge impulse.
Now that's interesting for the medical field.
Now this works a tiny based medical device powered by edge impulse to predict atrial fibrillation,
atrial ventricular block one and a normal EKG with more than 90 percent accuracy.
And also, it is an application powered by Edge Impos to develop a mini diagnosis EKG analyzer device,
which can fit in a pocket.
Now that's convenient, and it can diagnose heart diseases independently without a cloud connectivity.
Now that is very, very convenient for the medical people.
Another medical device that was invented is a fall detection and heart rate monitoring using an AVR
aortic.
Now this is a wearable low power device that detects falls using machine learning at the edge.
It also monitors the heart rate full scan result in physical and physiological trauma, especially for
elderly patients.
Now, in order to improve the quality of life of these patients, this project presents the development
of a fall detection and heart rate monitoring system, so this detects any fall in the heart rates of
the patients.
The wearable device obtains information from accelerometer and heart rate sensor and sends them to the
A.W. security guard.
The fall detection is done offline on the chip, using a machine learning algorithm.
How convenient this device is because it can run even though offline.
Here is a build a smart dumbbell with some detail.
21.
Machine learning every key now distance your dumbbell into a smart belt with the microchip some 21 MLF
and an embedded classifier built in the edge imposter.
VIDEO.
Here are some ideas from the seed Edu team.
One is a better sound translator.
It actually uses real terminal internal microphone to record the data sets of sounds of your pet being
hungry, angry, lonely, happy, excited or anything, and then the train model that would help you
to understand it better.
Here we have a very small project, which is a robot control with EMG detector that that's useful.
It utilized Grove EMG detector module to analyze arm muscle activity and control your own robot with
arm gestures.
Now that's fantastic, isn't it?
Another idea from the seed Edu team is an intelligent plant care.
Now this employed to rate the temperature and humidity and pressure, we determine the internal light
sensor.
And possibly grove moisture north to monitor and control plant environment for optimal growth.
So basically what this does is it monitors the growth of a plant.
Here we have is an advanced technology, a real terminal on beetle.
So as we all know that this is a robot.
It installs real terminal on Bionic Robotic Dog from Pito LLC.
That and seed add to both beetle and real terminal have Raspberry Pi pin connectors, which makes interfacing
them easy.
That's it for this presentation.
I hope you've learned a lot, and I'm looking forward for your future projects.
If you have any questions, feel free to contact me on Skype, on my phone number and on my email once
again.
Thank you for listening.