DevHeads IoT Integration Server•5mo ago

Can i use Edge Impulse to collect data, train a model and deploy it on an Arduino Nano 33 BLE Sense

@Middleware & OS @everyone Hello guys, can I use Edge Impulse to collect data, train a model, and deploy it on an Arduino Nano 33 BLE Sense for anomaly detection? I'm having issues with deployment. Here's my code 👇

Solution:

i recommend reduce your model size. also use the Arduino IDE's memory usage reporting feature to get a detailed of how memory is being used in your sketch.

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13 Replies

Enthernet Code•5mo ago

#include <EdgeImpulse.h>
#include <Arduino_LSM9DS1.h>  // Include the library for the accelerometer

#define THRESHOLD 0.5  // Define the threshold for anomaly detection

// Function to initialize the Edge Impulse model
void initializeModel() {
  // Initialize the Edge Impulse model
  if (ei_impulse_init() != EI_IMPULSE_OK) {
    Serial.println("Failed to initialize Edge Impulse model");
    while (1);  // Stop execution if initialization fails
  }
}

void setup() {
  // Start serial communication
  Serial.begin(9600);
  
  // Initialize Edge Impulse model
  initializeModel();
  
  // Initialize the accelerometer
  if (!IMU.begin()) {
    Serial.println("Failed to initialize IMU!");
    while (1);
  }

  // Set built-in LED pin as output
  pinMode(LED_BUILTIN, OUTPUT);
}

void collectData(ei::signal_t* signal) {

#include <EdgeImpulse.h>
#include <Arduino_LSM9DS1.h>  // Include the library for the accelerometer

#define THRESHOLD 0.5  // Define the threshold for anomaly detection

// Function to initialize the Edge Impulse model
void initializeModel() {
  // Initialize the Edge Impulse model
  if (ei_impulse_init() != EI_IMPULSE_OK) {
    Serial.println("Failed to initialize Edge Impulse model");
    while (1);  // Stop execution if initialization fails
  }
}

void setup() {
  // Start serial communication
  Serial.begin(9600);
  
  // Initialize Edge Impulse model
  initializeModel();
  
  // Initialize the accelerometer
  if (!IMU.begin()) {
    Serial.println("Failed to initialize IMU!");
    while (1);
  }

  // Set built-in LED pin as output
  pinMode(LED_BUILTIN, OUTPUT);
}

void collectData(ei::signal_t* signal) {

Enthernet Code•5mo ago

float x, y, z;
  static float features[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE] = { 0 };
  static int feature_index = 0;

  // Read data from the accelerometer
  if (IMU.accelerationAvailable()) {
    IMU.readAcceleration(x, y, z);

    // Fill the feature array with the collected data
    features[feature_index++] = x;
    features[feature_index++] = y;
    features[feature_index++] = z;

    // Reset the index if it exceeds the frame size
    if (feature_index >= EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE) {
      feature_index = 0;
    }
  }

  // Assign the features array to the signal data
  signal->total_length = EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE;
  signal->get_data = [](size_t offset, size_t length, float *out_ptr) -> int {
    memcpy(out_ptr, features + offset, length * sizeof(float));
    return 0;
  };
}

float runInference() {
  ei_impulse_result_t result = { 0 };
  
  // Run the Edge Impulse model and get the result
  if (ei_run_impulse(&result) != EI_IMPULSE_OK) {
    Serial.println("Failed to run Edge Impulse model");
    return 0.0;
  }

  // Return the anomaly score from the result
  return result.anomaly;
}

void loop() {
  ei::signal_t signal;

  // Collect data
  collectData(&signal);
  
  // Run anomaly detection
  float anomalyScore = runInference();
  
  // Check if the score is above a threshold indicating an anomaly
  if (anomalyScore > THRESHOLD) {
    // Handle anomaly (e.g., turn on an LED, send a notification)
    Serial.println("Anomaly detected!");
    digitalWrite(LED_BUILTIN, HIGH);  // Turn on the built-in LED
  } else {
    digitalWrite(LED_BUILTIN, LOW);  // Turn off the built-in LED
  }
  
  // Small delay to prevent excessive looping
  delay(1000);
}

float x, y, z;
  static float features[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE] = { 0 };
  static int feature_index = 0;

  // Read data from the accelerometer
  if (IMU.accelerationAvailable()) {
    IMU.readAcceleration(x, y, z);

    // Fill the feature array with the collected data
    features[feature_index++] = x;
    features[feature_index++] = y;
    features[feature_index++] = z;

    // Reset the index if it exceeds the frame size
    if (feature_index >= EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE) {
      feature_index = 0;
    }
  }

  // Assign the features array to the signal data
  signal->total_length = EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE;
  signal->get_data = [](size_t offset, size_t length, float *out_ptr) -> int {
    memcpy(out_ptr, features + offset, length * sizeof(float));
    return 0;
  };
}

float runInference() {
  ei_impulse_result_t result = { 0 };
  
  // Run the Edge Impulse model and get the result
  if (ei_run_impulse(&result) != EI_IMPULSE_OK) {
    Serial.println("Failed to run Edge Impulse model");
    return 0.0;
  }

  // Return the anomaly score from the result
  return result.anomaly;
}

void loop() {
  ei::signal_t signal;

  // Collect data
  collectData(&signal);
  
  // Run anomaly detection
  float anomalyScore = runInference();
  
  // Check if the score is above a threshold indicating an anomaly
  if (anomalyScore > THRESHOLD) {
    // Handle anomaly (e.g., turn on an LED, send a notification)
    Serial.println("Anomaly detected!");
    digitalWrite(LED_BUILTIN, HIGH);  // Turn on the built-in LED
  } else {
    digitalWrite(LED_BUILTIN, LOW);  // Turn off the built-in LED
  }
  
  // Small delay to prevent excessive looping
  delay(1000);
}

Dtynin•5mo ago

@Enthernet Code Edge Impulse is perfect for your Nano's anomaly detection! While your code looks good, you need to ensure a smooth deployment by verifying you have the correct Edge Impulse library (edge-impulse-arduino-sdk) for your Nano, double-check how the library defines ei_impulse_init() and ei_run_impulse() (import correct headers), and make sure collectData properly formats the data during collection.

Boss lady•5mo ago

Yea, @Enthernet Code after applying this changes and modifications your code should look like

#include <Arduino_LSM9DS1.h> // Include the library for the accelerometer
#include <edge-impulse-sdk/classifier/ei_run_classifier.h> // Include Edge Impulse SDK

#define THRESHOLD 0.5 // Define the threshold for anomaly detection

// Function to initialize the Edge Impulse model
void initializeModel() {
  // Initialize the Edge Impulse model
  if (ei_impulse_init() != EI_IMPULSE_OK) {
    Serial.println("Failed to initialize Edge Impulse model");
    while (1); // Stop execution if initialization fails
  }
}

void setup() {
  // Start serial communication
  Serial.begin(9600);
  
  // Initialize Edge Impulse model
  initializeModel();
  
  // Initialize the accelerometer
  if (!IMU.begin()) {
    Serial.println("Failed to initialize IMU!");
    while (1);
  }

  // Set built-in LED pin as output
  pinMode(LED_BUILTIN, OUTPUT);
}

void collectData(ei::signal_t* signal) {
  float x, y, z;
  static float features[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE] = { 0 };
  static int feature_index = 0;

  // Read data from the accelerometer
  if (IMU.accelerationAvailable()) {
    IMU.readAcceleration(x, y, z);

    // Fill the feature array with the collected data
    features[feature_index++] = x;
    features[feature_index++] = y;
    features[feature_index++] = z;

    // Reset the index if it exceeds the frame size
    if (feature_index >= EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE) {
      feature_index = 0;
    }

#include <Arduino_LSM9DS1.h> // Include the library for the accelerometer
#include <edge-impulse-sdk/classifier/ei_run_classifier.h> // Include Edge Impulse SDK

#define THRESHOLD 0.5 // Define the threshold for anomaly detection

// Function to initialize the Edge Impulse model
void initializeModel() {
  // Initialize the Edge Impulse model
  if (ei_impulse_init() != EI_IMPULSE_OK) {
    Serial.println("Failed to initialize Edge Impulse model");
    while (1); // Stop execution if initialization fails
  }
}

void setup() {
  // Start serial communication
  Serial.begin(9600);
  
  // Initialize Edge Impulse model
  initializeModel();
  
  // Initialize the accelerometer
  if (!IMU.begin()) {
    Serial.println("Failed to initialize IMU!");
    while (1);
  }

  // Set built-in LED pin as output
  pinMode(LED_BUILTIN, OUTPUT);
}

void collectData(ei::signal_t* signal) {
  float x, y, z;
  static float features[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE] = { 0 };
  static int feature_index = 0;

  // Read data from the accelerometer
  if (IMU.accelerationAvailable()) {
    IMU.readAcceleration(x, y, z);

    // Fill the feature array with the collected data
    features[feature_index++] = x;
    features[feature_index++] = y;
    features[feature_index++] = z;

    // Reset the index if it exceeds the frame size
    if (feature_index >= EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE) {
      feature_index = 0;
    }

Boss lady•5mo ago

}

  // Assign the features array to the signal data
  signal->total_length = EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE;
  signal->get_data = [](size_t offset, size_t length, float *out_ptr) -> int {
    memcpy(out_ptr, features + offset, length * sizeof(float));
    return 0;
  };
}

float runInference() {
  ei_impulse_result_t result = { 0 };
  
  // Run the Edge Impulse model and get the result
  if (ei_run_impulse(&result) != EI_IMPULSE_OK) {
    Serial.println("Failed to run Edge Impulse model");
    return 0.0;
  }

  // Return the anomaly score from the result
  return result.anomaly;
}

void loop() {
  ei::signal_t signal;

  // Collect data
  collectData(&signal);
  
  // Run anomaly detection
  float anomalyScore = runInference();
  
  // Check if the score is above a threshold indicating an anomaly
  if (anomalyScore > THRESHOLD) {
    // Handle anomaly (e.g., turn on an LED, send a notification)
    Serial.println("Anomaly detected!");
    digitalWrite(LED_BUILTIN, HIGH); // Turn on the built-in LED
  } else {
    digitalWrite(LED_BUILTIN, LOW); // Turn off the built-in LED
  }
  
  // Small delay to prevent excessive looping
  delay(1000);
}

}

  // Assign the features array to the signal data
  signal->total_length = EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE;
  signal->get_data = [](size_t offset, size_t length, float *out_ptr) -> int {
    memcpy(out_ptr, features + offset, length * sizeof(float));
    return 0;
  };
}

float runInference() {
  ei_impulse_result_t result = { 0 };
  
  // Run the Edge Impulse model and get the result
  if (ei_run_impulse(&result) != EI_IMPULSE_OK) {
    Serial.println("Failed to run Edge Impulse model");
    return 0.0;
  }

  // Return the anomaly score from the result
  return result.anomaly;
}

void loop() {
  ei::signal_t signal;

  // Collect data
  collectData(&signal);
  
  // Run anomaly detection
  float anomalyScore = runInference();
  
  // Check if the score is above a threshold indicating an anomaly
  if (anomalyScore > THRESHOLD) {
    // Handle anomaly (e.g., turn on an LED, send a notification)
    Serial.println("Anomaly detected!");
    digitalWrite(LED_BUILTIN, HIGH); // Turn on the built-in LED
  } else {
    digitalWrite(LED_BUILTIN, LOW); // Turn off the built-in LED
  }
  
  // Small delay to prevent excessive looping
  delay(1000);
}

wafa_ath•5mo ago

Are you seeing any specific error messages ?

Enthernet Code•5mo ago

Failed to run Edge Impulse model

wafa_ath•5mo ago

maybe there is not enough memory available for the model to run. Can you provide the available memory before and after initializing the model?

Enthernet Code•5mo ago

the available memory before running the code is 20200 bytes, and after initializing the model, it drops to 1500 bytes.

wafa_ath•5mo ago

it is likely that the model is consuming too much memory, and dosen't let enough space for the rest of the program

Enthernet Code•5mo ago

Okay, do u suggest switching model or find a device with a much higher memory space

Solution

wafa_ath•5mo ago

i recommend reduce your model size. also use the Arduino IDE's memory usage reporting feature to get a detailed of how memory is being used in your sketch.

Enthernet Code•5mo ago

Okay I would do this and give you my feedback, thanks 👍

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Can i use Edge Impulse to collect data, train a model and deploy it on an Arduino Nano 33 BLE Sense