JuanYi
Published

Air-writing TinyML Alphabet Recognition

A proof-of-concept project to recognize air-written alphabet, using Arduino Nano RP2040 Connect run with Edge Impulse tinyML model.

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Things used in this project

Hardware components

Arduino Nano RP2040 Connect
Arduino Nano RP2040 Connect
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Software apps and online services

Arduino IDE
Arduino IDE
Edge Impulse Studio
Edge Impulse Studio

Story

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Code

Arduino Sketch

Arduino
RP2040_220801_v104_Acc_Gyr_Single
/* Edge Impulse Arduino examples
 * Copyright (c) 2021 EdgeImpulse Inc.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

/* Includes ---------------------------------------------------------------- */
#include <Alphabet2_inferencing.h>
#include <Arduino_LSM6DSOX.h> //#include <Arduino_LSM9DS1.h>

/* Constant defines -------------------------------------------------------- */
#define CONVERT_G_TO_MS2    9.80665f
#define MAX_ACCEPTED_RANGE  2.0f        // starting 03/2022, models are generated setting range to +-2, but this example use Arudino library which set range to +-4g. If you are using an older model, ignore this value and use 4.0f instead

/*
 ** NOTE: If you run into TFLite arena allocation issue.
 **
 ** This may be due to may dynamic memory fragmentation.
 ** Try defining "-DEI_CLASSIFIER_ALLOCATION_STATIC" in boards.local.txt (create
 ** if it doesn't exist) and copy this file to
 ** `<ARDUINO_CORE_INSTALL_PATH>/arduino/hardware/<mbed_core>/<core_version>/`.
 **
 ** See
 ** (https://support.arduino.cc/hc/en-us/articles/360012076960-Where-are-the-installed-cores-located-)
 ** to find where Arduino installs cores on your machine.
 **
 ** If the problem persists then there's not enough memory for this model and application.
 */

/* Private variables ------------------------------------------------------- */
static bool debug_nn = false; // Set this to true to see e.g. features generated from the raw signal

// Added variable to compare confidence level of each label
float conf_curr = 0.0;
float conf_prev = 0.0;
int   detected = 0;
size_t ix;
// Add varialbe for gyro coefficient to scale down
float gyro_coef = 0.1;

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

/**
 * @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      Get data and run inferencing
*
* @param[in]  debug  Get debug info if true
*/
void loop()
{
    ei_printf("\nStarting inferencing in 2 seconds...\n");
    delay(1000);
    ei_printf("Start in 1 second.\n");
    delay(1000);
    ei_printf("Start!\n");
    delay(750); // Slight delay after sampling started to get myself ready
    // Allocate a buffer here for the values we'll read from the IMU
    float buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE] = { 0 };
    
    // Change ix+= from 3 to 6
    for (size_t ix = 0; ix < EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE; ix += 6) {
        // Determine the next tick (and then sleep later)
        uint64_t next_tick = micros() + (EI_CLASSIFIER_INTERVAL_MS * 1000);

        IMU.readAcceleration(buffer[ix], buffer[ix + 1], buffer[ix + 2]);

        for (int i = 0; i < 3; i++) {
            if (fabs(buffer[ix + i]) > MAX_ACCEPTED_RANGE) {
                buffer[ix + i] = ei_get_sign(buffer[ix + i]) * MAX_ACCEPTED_RANGE;
            }
        }

        buffer[ix + 0] *= CONVERT_G_TO_MS2;
        buffer[ix + 1] *= CONVERT_G_TO_MS2;
        buffer[ix + 2] *= CONVERT_G_TO_MS2;

        // Read gyro & multiply with coefficient to scale down
        IMU.readGyroscope(buffer[ix+3], buffer[ix + 4], buffer[ix + 5]);

        for(int i = 3; i < 6; i++){
		      buffer[ix + i] = buffer[ix + i] * gyro_coef;
	      }
        //ei_printf("%f\t%f\t%f\t%f\t%f\t%f\n", buffer[ix], buffer[ix+1], buffer[ix+2], buffer[ix+3], buffer[ix+4], buffer[ix+5]);

        delayMicroseconds(next_tick - micros());
    }
    ei_printf("Sampling end.\n");
    // Turn the raw buffer in a signal which we can the classify
    signal_t signal;
    int err = numpy::signal_from_buffer(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.)",
        result.timing.dsp, result.timing.classification);
    ei_printf(": \n");
    for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
        //Disable this line because now i can see the highest confidence label result
        //ei_printf("    %s: %.5f\n", result.classification[ix].label, result.classification[ix].value);

        // Register the highest confidence flag
        conf_curr = result.classification[ix].value;
        if (conf_curr > conf_prev) {
          detected = ix;
          conf_prev = conf_curr;
        }
    }
    // Use the highest confidence flag to return the label
    ei_printf("Final:%d\t    %s: %.5f\n", detected, result.classification[detected].label, result.classification[detected].value);
    conf_curr = 0.0;
    conf_prev = 0.0;
    detected = 0;

#if EI_CLASSIFIER_HAS_ANOMALY == 1
    ei_printf("    anomaly score: %.3f\n", result.anomaly);
#endif
}

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

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JuanYi

JuanYi

7 projects • 8 followers

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Related channels and tags
  • Arduino
  • Edge Impulse