ML Code

#include <Arduino_LSM9DS1.h>
#include <TensorFlowLite.h>
#include <tensorflow/lite/micro/kernels/micro_ops.h>
#include <tensorflow/lite/micro/model.h>
#include <tensorflow/lite/micro/error_reporter.h>
#include <tensorflow/lite/micro/micro_interpreter.h>

// Include the TensorFlow Lite model
#include "gesture_model.h"

// TensorFlow Lite setup
tflite::MicroErrorReporter micro_error_reporter;
tflite::MicroInterpreter* interpreter;
TfLiteTensor* input;
TfLiteTensor* output;

void setup() {
  Serial.begin(115200);

  // Initialize the LSM9DS1 sensor
  if (!IMU.begin()) {
    Serial.println("Failed to initialize IMU!");
    while (1);
  }

  // Load the model
  static tflite::MicroOpResolver<6> micro_op_resolver;
  micro_op_resolver.AddFullyConnected();
  micro_op_resolver.AddSoftmax();

  static tflite::MicroInterpreter static_interpreter(
      gesture_model, micro_op_resolver, tensor_arena, kTensorArenaSize, &micro_error_reporter);
  interpreter = &static_interpreter;

  interpreter->AllocateTensors();
  input = interpreter->input(0);
  output = interpreter->output(0);
}

void loop() {
  float ax, ay, az;
  if (IMU.accelerationAvailable()) {
    IMU.readAcceleration(ax, ay, az);

    // Prepare the input data
    input->data.f[0] = ax;
    input->data.f[1] = ay;
    input->data.f[2] = az;

    // Run inference
    interpreter->Invoke();

    // Read the result
    float gesture = output->data.f[0];
    Serial.print("Gesture Probability: ");
    Serial.println(gesture);
  }

  delay(1000); // Adjust delay as needed
}