Hi I am totally new to Arudino development and ML model development. I have already completed the development and would like to deploy my model on my Seed XIAO BLE nRF52840. But the issue is that the sample code in the library is only for 3-axis accelerometer. I also require to include the gyroscope data. How should I go about modifiying the code?
I know I have to add to read the gyroscope info in the while loop but I have tried the whole day today and it keeps on giving me error. Appericate anyone help for this!
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];
/* Forward declaration */
void run_inference_background();
/**
* @brief Arduino setup function
*/
void setup()
{
// put your setup code here, to run once:
Serial.begin(115200);
Serial.println("Edge Impulse Inferencing Demo");
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 != 6) {
ei_printf("ERR: EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME should be equal to 6 (the 6 sensor axes)\n");
return;
}
inference_thread.start(mbed::callback(&run_inference_background));
}
/**
* @brief Return the sign of the number
*
* @param number
* @return int 1 if positive (or 0) -1 if negative
*/
float ei_get_sign(float number) {
return (number >= 0.0) ? 1.0 : -1.0;
}
/**
* @brief Run inferencing in the background.
*/
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);
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);
}
/**
* @brief Get data and run inferencing
*
* @param[in] debug Get debug info if true
*/
void loop()
{
while (1) {
// 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]
);
for (int i = 0; i < 3; i++) {
if (fabs(buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE - 3 + i]) > MAX_ACCEPTED_RANGE) {
buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE - 3 + i] = ei_get_sign(buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE - 3 + i]) * MAX_ACCEPTED_RANGE;
}
}
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);
}
}
#if !defined(EI_CLASSIFIER_SENSOR) || EI_CLASSIFIER_SENSOR != EI_CLASSIFIER_SENSOR_ACCELEROMETER
#error "Invalid model for current sensor"
#endif
interesting…