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);
}