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Monika BogatajAleksander hrastičJaka JemecTimon Kalagasidis
Published

Epilet - bracelet for epilepsy attacks

Make sure you get the help you need, when you need it.

IntermediateWork in progress4,841

Things used in this project

Hardware components

Nano 33 BLE Sense
Arduino Nano 33 BLE Sense
×1
USB-A to Micro-USB Cable
USB-A to Micro-USB Cable
×1

Software apps and online services

Arduino IDE
Arduino IDE
Edge Impulse Studio
Edge Impulse Studio
MIT App Inventor
MIT App Inventor

Story

Read more

Schematics

Scheme

Code

INO file

C/C++
Use this ino file in arduino.
#include <modulg-project-1_inference.h>
#include <Arduino_LSM9DS1.h>
#include <ArduinoBLE.h>

// BLE Battery Service
BLEService predictionService("382d6dfe-a1b3-11eb-bcbc-0242ac130002");

// BLE Battery Level Characteristic
BLEUnsignedCharCharacteristic predictionChar("382d6dff-a1b3-11eb-bcbc-0242ac130002",  // standard 16-bit characteristic UUID
    BLERead | BLENotify); // remote clients will be able to get notifications if this characteristic changes

/* Constant defines -------------------------------------------------------- */
#define CONVERT_G_TO_MS2    9.80665f

/* Private variables ------------------------------------------------------- */
static bool debug_nn = false; // Set this to true to see e.g. features generated from the raw signal
static uint32_t run_inference_every_ms = 200;
static rtos::Thread inference_thread(osPriorityLow);
static float buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE] = { 0 };
static float inference_buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE];
int counter = 0;
int indicator = 0;
float oldPrediction = 0;
long previousMillis = 0;
long previousUpdateMillis = 0;

void updatePrediction();
void run_inference_background();

void setup()
{
  // put your setup code here, to run once:
  Serial.begin(115200);
  Serial.println("Edge Impulse Inferencing Demo");

  //while (!Serial);

  pinMode(LED_BUILTIN, OUTPUT); // initialize the built-in LED pin to indicate when a central is connected

  // begin initialization
  if (!BLE.begin()) {
    Serial.println("starting BLE failed!");

    while (1);
  }

  if (!IMU.begin()) {
    ei_printf("Failed to initialize IMU!\r\n");
  }
  else {
    ei_printf("IMU initialized\r\n");
  }

  if (EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME != 3) {
    ei_printf("ERR: EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME should be equal to 3 (the 3 sensor axes)\n");
    return;
  }

  inference_thread.start(mbed::callback(&run_inference_background));

  BLE.setLocalName("Epilet");
  BLE.setAdvertisedService(predictionService); // add the service UUID
  predictionService.addCharacteristic(predictionChar); // add the battery level characteristic
  BLE.addService(predictionService); // Add the battery service
  predictionChar.writeValue(oldPrediction); // set initial value for this characteristic

  BLE.advertise();

  Serial.println("Bluetooth device active, waiting for connections...");
}

void ei_printf(const char *format, ...) {
  static char print_buf[1024] = { 0 };

  va_list args;
  va_start(args, format);
  int r = vsnprintf(print_buf, sizeof(print_buf), format, args);
  va_end(args);

  if (r > 0) {
    Serial.write(print_buf);
  }
}

void run_inference_background()
{
  // wait until we have a full buffer
  delay((EI_CLASSIFIER_INTERVAL_MS * EI_CLASSIFIER_RAW_SAMPLE_COUNT) + 100);

  // This is a structure that smoothens the output result
  // With the default settings 70% of readings should be the same before classifying.
  ei_classifier_smooth_t smooth;
  ei_classifier_smooth_init(&smooth, 10 /* no. of readings */, 7 /* min. readings the same */, 0.8 /* min. confidence */, 0.3 /* max anomaly */);

  while (1) {
    // copy the buffer
    memcpy(inference_buffer, buffer, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE * sizeof(float));

    // Turn the raw buffer in a signal which we can the classify
    signal_t signal;
    int err = numpy::signal_from_buffer(inference_buffer, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, &signal);
    if (err != 0) {
      ei_printf("Failed to create signal from buffer (%d)\n", err);
      return;
    }

    // Run the classifier
    ei_impulse_result_t result = { 0 };

    err = run_classifier(&signal, &result, debug_nn);
    if (err != EI_IMPULSE_OK) {
      ei_printf("ERR: Failed to run classifier (%d)\n", err);
      return;
    }

    // print the predictions
    ei_printf("Predictions ");
    ei_printf("(DSP: %d ms., Classification: %d ms., Anomaly: %d ms.)",
              result.timing.dsp, result.timing.classification, result.timing.anomaly);
    ei_printf(": ");

    // ei_classifier_smooth_update yields the predicted label
    const char *prediction = ei_classifier_smooth_update(&smooth, &result);

    if (*prediction == '1') {
      counter++;
      if (counter == 20) {
        //ime funkcije, ki bo odstevala
        //tukaj printamo da je epilepsija
        ei_printf("epilepsija\n");
        indicator = 1;
        counter = 0;
        long currentMillis = millis();
        if (currentMillis - previousMillis >= 200) {
          previousMillis = currentMillis;
          updatePrediction();
        }
      }

    }
    else {
      counter = 0;
      ei_printf("ni epilepsija\n");
      indicator = 0;
      long currentMillis = millis();
      if (currentMillis - previousMillis >= 200) {
        previousMillis = currentMillis;
        updatePrediction();
      }

    }

    ei_printf("%s ", prediction);
    // print the cumulative results
    ei_printf(" [ ");
    for (size_t ix = 0; ix < smooth.count_size; ix++) {
      ei_printf("%u", smooth.count[ix]);
      if (ix != smooth.count_size + 1) {
        ei_printf(", ");
      }
      else {
        ei_printf(" ");
      }
    }
    ei_printf("]\n");

    delay(run_inference_every_ms);
  }

  ei_classifier_smooth_free(&smooth);
}

void loop()
{
  // wait for a BLE central
  BLEDevice central = BLE.central();
  if (central) {
    Serial.print("Connected to central: ");
    // print the central's BT address:
    Serial.println(central.address());
    // turn on the LED to indicate the connection:
    digitalWrite(LED_BUILTIN, HIGH);

    // check the battery level every 200ms
    // while the central is connected:
    while (central.connected()) {
      long currentMillis = millis();
      // if 200ms have passed, check the battery level:
      if (currentMillis - previousMillis >= 200) {
        previousMillis = currentMillis;
        updatePrediction();
      }

      // Determine the next tick (and then sleep later)
      uint64_t next_tick = micros() + (EI_CLASSIFIER_INTERVAL_MS * 1000);

      // roll the buffer -3 points so we can overwrite the last one
      numpy::roll(buffer, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, -3);

      // read to the end of the buffer
      IMU.readAcceleration(
        buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE - 3],
        buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE - 2],
        buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE - 1]
      );

      buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE - 3] *= CONVERT_G_TO_MS2;
      buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE - 2] *= CONVERT_G_TO_MS2;
      buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE - 1] *= CONVERT_G_TO_MS2;

      // and wait for next tick
      uint64_t time_to_wait = next_tick - micros();
      delay((int)floor((float)time_to_wait / 1000.0f));
      delayMicroseconds(time_to_wait % 1000);
    }
    // when the central disconnects, turn off the LED:
    digitalWrite(LED_BUILTIN, LOW);
    Serial.print("Disconnected from central: ");
    Serial.println(central.address());
  }

}

void updatePrediction() {
  float prediction = indicator;
  long currentMillis = millis();

  if (prediction != oldPrediction || (currentMillis - previousUpdateMillis) >= 5000) {      // if the battery level has changed
    previousUpdateMillis = currentMillis;
    predictionChar.writeValue(prediction);  // and update the battery level characteristic
    oldPrediction = prediction;           // save the level for next comparison
  }
}


#if !defined(EI_CLASSIFIER_SENSOR) || EI_CLASSIFIER_SENSOR != EI_CLASSIFIER_SENSOR_ACCELEROMETER
#error "Invalid model for current sensor"
#endif

Github repository

All code including libraries. Add library to arduino. You can compress it as .zip and include as zip library. Sketch --> Include library --> Add .ZIP Library...

Credits

Monika Bogataj

Monika Bogataj

1 project • 6 followers
Aleksander hrastič

Aleksander hrastič

1 project • 6 followers
Jaka Jemec

Jaka Jemec

1 project • 5 followers
Timon Kalagasidis

Timon Kalagasidis

1 project • 5 followers

Comments

Related channels and tags
  • Edge Impulse
  • Internet of Things
  • Internet of Things
  • Machine Learning & AI
  • Machine Learning & AI
  • MIT App Inventor
  • Wearables
  • Wearables