[Project] Ultra TinyML: Machine Learning for 8-bit Microcontroller - How to create a gesture recognition system with Arduino and Neuton TinyML

#define NUM_SAMPLES 50

Adafruit_MPU6050 mpu;

void setup() {
  // init serial port
  Serial.begin(115200);
  while (!Serial) {
    delay(10);
  }
  // init IMU sensor
  if (!mpu.begin()) {
    while (1) {
      delay(10);
    }
  }
  
  // configure IMU sensor
  // [...]

  // print the CSV header (ax0,ay0,az0,...,gx49,gy49,gz49,target)
  for (int i=0; i<NUM_SAMPLES; i++) {
    Serial.print("aX");
    Serial.print(i);
    Serial.print(",aY");
    Serial.print(i);
    Serial.print(",aZ");
    Serial.print(i);
    Serial.print(",gX");
    Serial.print(i);
    Serial.print(",gY");
    Serial.print(i);
    Serial.print(",gZ");
    Serial.print(i);
    Serial.print(",");
  }
  Serial.println("target");
}

/*
 * Function: model_init
 * ----------------------------
 *
 *   returns: result of initialization (bool)
 */
uint8_t model_init() {
   uint8_t res;

   res = CalculatorInit(&neuralNet, NULL);

   return (ERR_NO_ERROR == res);
}

/*
 * Function: model_run_inference
 * ----------------------------
 *
 *   sample: input array to make prediction
 *   size_in: size of input array
 *   size_out: size of result array
 *
 *   returns: result of prediction
 */
float* model_run_inference(float* sample, 
                           uint32_t size_in, 
                           uint32_t *size_out) {
   if (!sample || !size_out)
      return NULL;
   if (size_in != neuralNet.inputsDim)
      return NULL;

   *size_out = neuralNet.outputsDim;

   return CalculatorRunInference(&neuralNet, sample);
}

#include "src/Gesture Recognition_v1/user_app.h"

void setup() {
   // init serial port and IMU sensor
   // [...]
   
   // init Neuton neural network model
   if (!model_init()) {
      Serial.print("Failed to initialize Neuton model!");
      while (1) {
        delay(10);
      }
   }
}

#define GESTURE_ARRAY_SIZE  (6*NUM_SAMPLES+1)

void loop() {
   sensors_event_t a, g, temp;
   float gestureArray[GESTURE_ARRAY_SIZE]  = {0};
   
   // wait for significant motion
   // [...]
   
   // read samples of the detected motion
   while (samplesRead < NUM_SAMPLES) {
      // read the acceleration and gyroscope data
      mpu.getEvent(&a, &g, &temp);
      
      // fill gesture array (model input)
      gestureArray[samplesRead*6 + 0] = a.acceleration.x;
      gestureArray[samplesRead*6 + 1] = a.acceleration.y;
      gestureArray[samplesRead*6 + 2] = a.acceleration.z;
      gestureArray[samplesRead*6 + 3] = g.gyro.x;
      gestureArray[samplesRead*6 + 4] = g.gyro.y;
      gestureArray[samplesRead*6 + 5] = g.gyro.z;
    
      samplesRead++;
    
      delay(10);
   
      // check the end of gesture acquisition
      if (samplesRead == NUM_SAMPLES) {
         uint32_t size_out = 0;
      
         // run model inference
         float* result = model_run_inference(gestureArray,  
                                             GESTURE_ARRAY_SIZE, 
                                             &size_out);
         // check if model inference result is valid
         if (result && size_out) {
            // check if problem is binary classification
            if (size_out >= 2) { 
               // check if one of the result has >50% of accuracy
               if (result[0] > 0.5) {
                  Serial.print("Detected gesture: 0"); 
                  // [...]
               } else if (result[1] > 0.5) {
                  Serial.print("Detected gesture: 1"); 
                  // [...]
               } else { 
                  // solution is not reliable
                  Serial.println("Detected gesture: NONE");
               } 
            }
         }
     }
   }
}