To create your AI activity timer, you will train a machine learning, or ML, model
to recognise when you’re doing different movements or activities.
You’ll then combine that model with some ready-made code for an activity timer,
before downloading it to your micro:bit and using it in real life.
Click ‘Open in micro:bit CreateAI’ to launch the project.
This project comes with 6 samples of movement data for walking, 6 samples of movement data
for jumping up and down, and 6 samples of movement data for staying fairly still.
You will add more samples by recording your own movement data.
micro:bit CreateAI collects movement data samples using the accelerometer, or movement sensor, on the micro:bit.
You will wear a micro:bit and battery pack on your wrist or ankle
so that you can move freely to record your own movement data samples.
To get started, connect the ankle-worn micro:bit to CreateAI.
We call this the data collection micro:bit.
If your computer has Bluetooth enabled then you will just need 1 micro:bit with a battery pack and a USB data lead.
If you don’t have a Bluetooth connection, you’ll be prompted to use 2 micro:bits.
The second micro:bit will remain connected to the USB cable and act as
a radio link to the data collection micro:bit. Follow the instructions on screen to connect.
Once your micro:bit is connected, you will see the lines on the live graph change as you move your micro:bit about.
You’re now ready to add your own movement data samples.
As this project already includes some data samples,
we suggest you just add 1 more sample for each action for now, and spend more time collecting and analysing data later.
Make sure your data collection micro:bit is attached to the inside of the ankle, with button B on top.
To add data to a specific action, select it by clicking on it.
You will get a 3 second countdown before a 1 second recording starts.
Click record and start moving right away to ensure you get a clean data sample.
A clean sample is one where you are moving for the entire sample,
you don’t start late or finish moving early.
Next try adding an extra data sample to the ‘jumping’ data set and the ‘being still’ data set.
Select them by clicking on the action, then click record and jump or stay quite still as you record the samples.
You’ll notice on the 'being still' samples that the x,y,z lines change places depending on the angle of the attached micro:bit.
We don’t have a lot of data right now, but we do have enough to train our own machine learning model using CreateAI.
So click ‘Train model’ to use the current data to build an ML model.
The tool now builds a mathematical model that should recognise different actions when you move your micro:bit.
As soon as the model has been trained, you will see the Testing model page.
Now use the data collection micro:bit to test how well the model is working.
It should still be connected to the tool, and you’ll see that as you move it, CreateAI is estimating what action you are doing.
Try out each of the actions to see both the estimated action and the certainty bar graph change.
The % on the certainty bar graph shows how confident the model is that you are doing each action.
You may notice your model is not estimating some actions accurately, or maybe it is
working well for one action but not the other, so after exploring how it is currently working,
it is a good idea to click on ‘Edit data samples’ and improve your model.
Machine learning models usually work best with MORE data, so record some extra samples for each
of the actions, or focus on collecting more data for the action that was problematic in testing.
You can record one sample at a time or you can record 10 samples in sequence.
Clean data samples also help an ML model work better so examine your data set and identify any data samples that could confuse the model.
You can delete these by pressing X.
Once you’ve added more data and checked your data set, click ‘Train model’ again to use your amended data set.
Then test the model again on the ‘Testing model’ page.
Once you’re happy with how the ML model is behaving, you can use it with the ready-made project code.
Click on ‘Edit in MakeCode’ to see the code blocks in a special version of Microsoft MakeCode.
You can always return to CreateAI using the arrow in the top left of the screen.
These code blocks use the model you have created within an exercise timer.
The code uses 3 variables to keep track of how long you've been doing each action.
When the program first runs it sets these timer variables to 0.
The 'on ML start' blocks are triggered when the ML model decides you have started a specific action.
They show different icons on the micro:bit's LED display depending on the action it has estimated you are doing.
The 'on ML stop' blocks are triggered when the ML model decides you have finished an action, in this case walking, jumping or being still.
Code inside each block clears the screen and adds the duration of the action that has just
finished to the variable storing the total times for each action.
The ML model works with the code to allow you to view the total time spent on each action.
Press button A to see the estimate of how long you were walking.
Press button B to see how long the model estimated you were jumping.
To see the estimated duration you have been still press A and B together.
The timer counts in milliseconds, thousandths of a second, so the number shown is divided by 1000 to show a time in seconds.
To make your AI activity timer run on your micro:bit, you just need to download this code to a micro:bit.
If you don’t have another micro:bit available, simply replace the code currently on the data collection micro:bit with the project code.
Now you can test the project out in real life.
Do the correct icons display when you are exercising or not?
You can test if the timer code is working well with the model in 3 easy steps:
Press the reset button. Jump for 30 seconds.
Then press button B. You should see the number 30 scroll across your display.
You’re now ready to connect to CreateAI,
collect your own data, use it to train, test and improve a machine learning model, and then you
can combine this model with the ready-made code and try it out on your own micro:bit.
If you’re looking for ways to personalise this even more try adding some different actions like running or dance steps.
Enjoy!