AI sports data logger

Make an AI sports data logger with micro:bit CreateAI that logs how much time you and others spend running, walking and being still. 

Paso 1: entiéndelo

How does it work?

In this project, you’ll train a machine learning (ML) model to recognise when you’re running, walking and being still.

You’ll combine that model with a MakeCode program that uses the micro:bit’s data logging function to record what action you are doing every second.

This project could be useful in sports such as football or netball where you need to analyse how active certain players are. You could also use it to monitor how much time you have spent running or walking during school breaks or a during a workout.

¿Qué es el aprendizaje automático?

El aprendizaje automático (AM) es un tipo de inteligencia artificial (IA) en la que los ordenadores pueden aprender de los datos y tomar decisiones basadas en ellos.

Los modelos de ML son entrenados por humanos para ayudarles a tomar esas decisiones, por ejemplo, para reconocer diferentes «acciones» cuando mueves tu micro:bit de diferentes maneras.

¿Qué tengo que hacer?

Los sistemas de IA necesitan humanos que los diseñen, programen, prueben y utilicen. Recopilarás datos para entrenar un modelo de aprendizaje automático, lo probarás, lo mejorarás y lo combinarás con código informático para crear un dispositivo inteligente que utilice IA. Para ello utilizarás un micro:bit y el sitio web de micro:bit CreateAI.

Paso 2: créalo

Lo que necesitas

• A micro:bit V2, USB cable, and a battery pack with 2 x AAA batteries
• A computer (e.g. desktop, laptop, or Chromebook) with access to the micro:bit CreateAI website, using a Chrome or Edge web browser
• Si tu ordenador no tiene Bluetooth activado, necesitarás un micro:bit V2 adicional
• A strap and holder, or another way to attach the micro:bit to your wrist (e.g. flexible craft stems or elastic bands)
• You may also find our micro:bit CreateAI teaching tips useful

Recoger muestras de datos

When you open the project in micro:bit CreateAI, you’ll see we’ve given you some data samples for ‘running’, 'walking’ and ‘still’ actions:

You can add your own movement samples using the micro:bit's movement sensor or accelerometer.

En micro:bit CreateAI, haz clic en el botón «Conectar» para conectar tu micro:bit de recogida de datos y sigue las instrucciones.

Attach the data collection micro:bit to your left wrist like a watch, with the logo at the top. Click on the first action, ‘walking’, and click 'Record' to record your own data samples. Si te equivocas, puedes eliminar las muestras que no quieras. También puedes pulsar el botón B del micro:bit para iniciar la grabación.

Si quieres grabar de forma continua durante 10 segundos para obtener 10 muestras, pulsa los tres puntos junto al botón de grabación y selecciona esa opción.

Now record your own data samples for the ‘running’ action, then the ‘still’ action, making sure for ‘still’ that you collect samples in different positions, such as facing up and down.

Entrenar y probar el modelo

Pulsa el botón «Entrenar modelo» para entrenar el modelo y, a continuación, pruébalo. Try walking and see if ‘walking’ is the estimated action, then running to see if ‘running’ is the estimated action. Keep still and see if ‘still’ is estimated. Give your micro:bit to someone else to wear (making sure they put it on the same wrist and in the same orientation) and see if it works as well for them.

Mejorar tu modelo

La mayoría de los modelos pueden mejorarse con más datos. Si es necesario mejorar el modelo, pulsa en «← Editar ejemplos de datos».

You can delete any data samples which you think don’t fit or add more samples from yourself and other people.

Vuelve a entrenar el modelo y vuelve a probarlo.

Pon el modelo y el código en tu micro:bit

En micro:bit CreateAI, haz clic en «Editar en MakeCode» para ver el código del proyecto en el editor MakeCode.

Puedes modificar el código o simplemente probarlo tal cual. Attach your micro:bit using a USB cable, click on the ‘Download’ button in the MakeCode screen, and follow the instructions to transfer your AI model and the code blocks to it.

Attach a battery pack to the micro:bit and put it on, ready to test out.

Collect your data

First press buttons A and B together to delete any old data logs from your micro:bit. Reset the timer by pressing the reset button on the back of the micro:bit. Press button A to start logging and button B to stop logging. 

Your data will stay on your micro:bit even if you disconnect the battery or press the reset button.

Analiza tus datos

Desconecta la batería y vuelve a conectar el micro:bit a un ordenador. El micro:bit aparece como una unidad USB llamada MICROBIT. Busca en la unidad MICROBIT y abre el archivo MY_DATA para ver una tabla de tus datos en un navegador web:

The time stamps in the log represent the amount of time that has passed since your micro:bit was powered on or reset.

Click on Visual preview to see a graph of your data:

You can also click on the Copy button and then paste your data into a spreadsheet.

Cómo funcionan los bloques de código

This program uses a variable called ‘logging’. A variable in a computer program is a container for storing data which can be accessed and updated while the program is running. In this program, the variable ‘logging’ controls if the micro:bit is logging or not and can be set to ‘true’ or ‘false’. Variables that can be set to these two values are called ‘Boolean’ variables.

When the program starts, the variable ‘logging’ is set to false. A show icon block is used to display a ‘no’ icon on the LED display to indicate the micro:bit is not logging. The set columns and set timestamp blocks create labels for the data logging table your micro:bit will produce.

The on button A pressed block is used to set logging to ‘true’ and show a ‘yes’ icon on the LED display. The on button B pressed block is used to set logging to ‘false’ and show a ‘no’ icon on the LED display. And the on buttons A + B pressed block sets logging to ‘false’, displays a skull icon, and deletes any log.

Finally, an ‘every’ block is used to check every 1,000 milliseconds or second if the micro:bit is logging. If it is, an ‘if then else’ block is used with ‘is ML detected’ and ‘log data’ blocks to record a 0 if you are still, a 1 if you are walking and a 2 if you are running in your data logging table. If the micro:bit cannot detect what you are doing, it records a -1 in the table. Bigger numbers are used for more active actions, so the resulting data logging graph gives you a clear visual record of how active you have been.

Evaluación

How accurate is the AI sports logger in tracking your movements? How could you improve its accuracy? Who would find this device particularly useful? How does it compare to the Step counter or the Movement data logger projects?

Paso 3: extiéndelo

• Add a fourth action such as ‘throwing’ for sports like netball or tennis.

• Add up how much time you spent on each activity. You could do this saving your data as a CSV file, opening it in a spreadsheet and using a formula such as =COUNTIF(B2:B70,1) Where B2:B70 is the range of the activity cells, and 1 is the activity number meaning ‘walking’.