In this Arduno Machine learning project we're going to identify the letters from the Morse alphabet.
In practice, we'll translate dots (•) and dashes (‒) "typed" with a push button into meaningful characters.
In this tutorial we're going to target an Arduino Nano board (old generation), equipped with 32 kb of flash and only 2 kb of RAM.

## 1. Features definition

For our task we'll use a simple push button as input and a fixed number of samples taken at a fixed interval (100 ms), starting from the first detection of the button press. I chose to record 30 samples for each letter, but you can easily customize the value as per your needs.

With 30 samples at 100 ms frequency, we'll have 3 seconds to "type" the letter and on the Serial monitor will appear a sequence of 0s and 1s, representing if the button was pressed or not; the inference procedure will translate this sequence into a letter.
As a reference, here are a couple example of what we'll be working with.

``````// A (•‒)
0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1

// D (‒••)
0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,1,1,1,0,0,0,1,1,1,1,1,1

// E (•)
0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1``````

## 2. Record sample data

To the bare minimum, we'll need a push button and two wires: one to ground and the other to a digital pin. Since in the example we'll make the button an `INPUT_PULLUP`, we'll read 0 when the button is pressed and 1 when not.

All we need to do is detect a press and record the following 30 samples of the digital pin:

``````#define IN 4
#define NUM_SAMPLES 30
#define INTERVAL 100

double features[NUM_SAMPLES];

void setup() {
Serial.begin(115200);
pinMode(IN, INPUT_PULLUP);
}

void loop() {
if (digitalRead(IN) == 0) {
recordButtonStatus();
printFeatures();
delay(1000);
}

delay(10);
}

void recordButtonStatus() {
for (int i = 0; i < NUM_SAMPLES; i++) {
delay(INTERVAL);
}
}``````
``````
void printFeatures() {
const uint16_t numFeatures = sizeof(features) / sizeof(float);

for (int i = 0; i < numFeatures; i++) {
Serial.print(features[i]);
Serial.print(i == numFeatures - 1 ? 'n' : ',');
}
}
``````

Open the Serial monitor and type a few times each letter: try to introduce some variations each time, for example waiting some more milliseconds before releasing the dash.

If you've never typed morse code before (as me), choose letters with few keystrokes and quite differentiable, otherwise you will need to be very good with the timing.

Save the recordings for each letter in a file named after the letter, so you will get meaningful results later on.

You may end with duplicate recordings: don't worry, that's not a problem. I'll paste my recordings for a few letters, as a reference.

``````// A (•‒)
0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1
0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1
0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1

// D (‒••)
0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,1,1,1,0,0,0,1,1,1,1,1,1
0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1,1,0,0,0,1,1,1,1,1,1
0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,1,1,1,0,0,0,1,1,1,1,1,1,1
0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1,0,0,0,1,1,1,1,1,1,1
0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,1,1,1,1,0,0,0,1,1,1,1,1,1,1
0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,1,1,1,0,0,1,1,1,1,1,1,1
0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,1,1,1,1,0,0,1,1,1,1,1,1,1
0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,1,1,1,1,0,0,1,1,1,1,1,1,1
0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1

// E (•)
0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1

// S (•••)
0,0,0,1,1,1,0,0,0,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,1,1,1,1,0,0,0,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,1,1,1,1,0,0,0,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,1,1,1,1,0,0,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,1,1,1,1,0,0,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,1,1,1,1,0,0,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1

// T (‒)
0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1``````

If you do a good job, you should end with quite distinguible features, as show in the plot below.

## 3. Train and export the classifier

For a detailed guide refer to the tutorial

``````from sklearn.ensemble import RandomForestClassifier
from micromlgen import port

# put your samples in the dataset folder
# one class per file
# one feature vector per line, in CSV format
features, classmap = load_features('dataset/')
X, y = features[:, :-1], features[:, -1]
classifier = RandomForestClassifier(n_estimators=30, max_depth=10).fit(X, y)
c_code = port(classifier, classmap=classmap)
print(c_code)``````

At this point you have to copy the printed code and import it in your Arduino project, in a file called `model.h`.

## 4. Run the inference

``````#include "model.h"

void loop() {
if (digitalRead(IN) == 0) {
recordButtonStatus();
Serial.print("Detected letter: ");
Serial.println(classIdxToName(predict(features)));
delay(1000);
}

delay(10);
}``````

Type some letter using the push button and see the identified value printed on the serial monitor.

That’s it: you deployed machine learning in 2 Kb!

#### Project figures

On my machine, the sketch targeted at the Arduino Nano (old generation) requires 12546 bytes (40%) of program space and 366 bytes (17%) of RAM. This means you could actually run machine learning in even less space than what the Arduino Nano provides. So, the answer to the question Can I run machine learning on Arduino? is definetly YES.

Did you find this tutorial useful? Was is it easy to follow or did I miss something? Let me know in the comments so I can keep improving the blog.

Check the full project code on Github

Help the blow grow