Night R
Published © MIT

Smart Health Assistant via Sparkfun Artemis

A smart cardio-health assistant based on Sparkfun Artemis ATP

IntermediateFull instructions providedOver 4 days997

Things used in this project

Story

Read more

Custom parts and enclosures

vhat1_jE44DW8Px0.jpg

vhat2_tKWZd2XLVX.jpg

vha4_GlgkDGtAYa.JPG

vha5-removebg-preview_1RchALgiXg.png

CAD Enclosure for Smart Health Assistant v2.0

A 3 part design; consisting of top sensor mounting shell, bottom controller boards mounting, and the acrylic round diffuser for ws2812b LED ring, this design can be easily 3D printed, and covered with black poly fabric, to give unique look and feel of the product.

Schematics

System Circuit Layout

Connection Diagram in ppt_vector

Code

Main Script#1

Arduino
speech_initiate
#include <Wire.h>
#include "MAX30105.h"
#include "heartRate.h"
MAX30105 particleSensor;
#include <SFE_MicroOLED.h>  // Include the SFE_MicroOLED library
#define PIN_RESET 9  
#define DC_JUMPER 1 
MicroOLED oled(PIN_RESET, DC_JUMPER);
#define blinkPin LED_BUILTIN
#include "SparkFun_VCNL4040_Arduino_Library.h"
VCNL4040 proximitySensor;
const byte RATE_SIZE = 4; //Increase this for more averaging. 4 is good.
byte rates[RATE_SIZE]; //Array of heart rates
byte rateSpot = 0;
long lastBeat = 0; //Time at which the last beat occurred
float beatsPerMinute;
int beatAvg;

// Undefine predefined Arduino max/min defs so they don't conflict with std methods
#if defined(min)
#undef min
#endif
#if defined(max)
#undef max
#endif
#include <TensorFlowLite.h>
#include "main_functions.h"
#include "audio_provider.h"
#include "command_responder.h"
#include "feature_provider.h"
#include "micro_features_micro_model_settings.h"
#include "micro_features_tiny_conv_micro_features_model_data.h"
#include "recognize_commands.h"
#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
#include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"


// Globals, used for compatibility with Arduino-style sketches.
namespace {
tflite::ErrorReporter* error_reporter = nullptr;
const tflite::Model* model = nullptr;
tflite::MicroInterpreter* interpreter = nullptr;
TfLiteTensor* model_input = nullptr;
FeatureProvider* feature_provider = nullptr;
RecognizeCommands* recognizer = nullptr;
int32_t previous_time = 0;

// Create an area of memory to use for input, output, and intermediate arrays.
// The size of this will depend on the model you're using, and may need to be
// determined by experimentation.
constexpr int kTensorArenaSize = 10 * 1024;
uint8_t tensor_arena[kTensorArenaSize];
}  // namespace

// The name of this function is important for Arduino compatibility.
void setup() {
  
  // Set up logging. Google style is to avoid globals or statics because of
  // lifetime uncertainty, but since this has a trivial destructor it's okay.
  // NOLINTNEXTLINE(runtime-global-variables)
  static tflite::MicroErrorReporter micro_error_reporter;
  error_reporter = &micro_error_reporter;

  // Map the model into a usable data structure. This doesn't involve any
  // copying or parsing, it's a very lightweight operation.
  model = tflite::GetModel(g_tiny_conv_micro_features_model_data);
  if (model->version() != TFLITE_SCHEMA_VERSION) {
    error_reporter->Report(
        "Model provided is schema version %d not equal "
        "to supported version %d.",
        model->version(), TFLITE_SCHEMA_VERSION);
    return;
  }

  // Pull in only the operation implementations we need.
  // This relies on a complete list of all the ops needed by this graph.
  // An easier approach is to just use the AllOpsResolver, but this will
  // incur some penalty in code space for op implementations that are not
  // needed by this graph.
  //
  // tflite::ops::micro::AllOpsResolver resolver;
  // NOLINTNEXTLINE(runtime-global-variables)
  static tflite::MicroMutableOpResolver micro_mutable_op_resolver;
  micro_mutable_op_resolver.AddBuiltin(
      tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
      tflite::ops::micro::Register_DEPTHWISE_CONV_2D());
  micro_mutable_op_resolver.AddBuiltin(
      tflite::BuiltinOperator_FULLY_CONNECTED,
      tflite::ops::micro::Register_FULLY_CONNECTED());
  micro_mutable_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
                                       tflite::ops::micro::Register_SOFTMAX());

  // Build an interpreter to run the model with.
  static tflite::MicroInterpreter static_interpreter(
      model, micro_mutable_op_resolver, tensor_arena, kTensorArenaSize,
      error_reporter);
  interpreter = &static_interpreter;

  // Allocate memory from the tensor_arena for the model's tensors.
  TfLiteStatus allocate_status = interpreter->AllocateTensors();
  if (allocate_status != kTfLiteOk) {
    error_reporter->Report("AllocateTensors() failed");
    return;
  }

  // Get information about the memory area to use for the model's input.
  model_input = interpreter->input(0);
  if ((model_input->dims->size != 4) || (model_input->dims->data[0] != 1) ||
      (model_input->dims->data[1] != kFeatureSliceCount) ||
      (model_input->dims->data[2] != kFeatureSliceSize) ||
      (model_input->type != kTfLiteUInt8)) {
    error_reporter->Report("Bad input tensor parameters in model");
    return;
  }

  // Prepare to access the audio spectrograms from a microphone or other source
  // that will provide the inputs to the neural network.
  // NOLINTNEXTLINE(runtime-global-variables)
  static FeatureProvider static_feature_provider(kFeatureElementCount,
                                                 model_input->data.uint8);
  feature_provider = &static_feature_provider;
  static RecognizeCommands static_recognizer(error_reporter);
  recognizer = &static_recognizer;
  previous_time = 0;

  Serial.begin(9600);
  if (!particleSensor.begin(Wire, I2C_SPEED_FAST)) //Use default I2C port, 400kHz speed
  {
    Serial.println("MAX30105 was not found. Please check wiring/power. ");
    while (1);
  }
  particleSensor.setup(); //Configure sensor with default settings
  particleSensor.setPulseAmplitudeRed(0x0A); //Turn Red LED to low to indicate sensor is running
  particleSensor.setPulseAmplitudeGreen(0); //Turn off Green LED

  if (proximitySensor.begin() == false)
  {
    Serial.println("Device not found. Please check wiring.");
    while (1); //Freeze!
  }
  pinMode(blinkPin, OUTPUT);
  Wire.begin();
  oled.begin();    // Initialize the OLED
  oled.clear(ALL); // Clear the display's internal memory
  oled.display();  // Display what's in the buffer (splashscreen)
  delay(1000);     // Delay 1000 ms
  oled.clear(PAGE); // Clear the buffer.

}

// The name of this function is important for Arduino compatibility.
void loop() {
  // Fetch the spectrogram for the current time.
  const int32_t current_time = LatestAudioTimestamp();
  int how_many_new_slices = 0;
  TfLiteStatus feature_status = feature_provider->PopulateFeatureData(
      error_reporter, previous_time, current_time, &how_many_new_slices);
  if (feature_status != kTfLiteOk) {
    error_reporter->Report("Feature generation failed");
    return;
  }
  previous_time = current_time;
  // If no new audio samples have been received since last time, don't bother
  // running the network model.
  if (how_many_new_slices == 0) {
    return;
  }

  // Run the model on the spectrogram input and make sure it succeeds.
  TfLiteStatus invoke_status = interpreter->Invoke();
  if (invoke_status != kTfLiteOk) {
    error_reporter->Report("Invoke failed");
    return;
  }

  // Obtain a pointer to the output tensor
  TfLiteTensor* output = interpreter->output(0);
  // Determine whether a command was recognized based on the output of inference
  const char* found_command = nullptr;
  uint8_t score = 0;
  bool is_new_command = false;
  TfLiteStatus process_status = recognizer->ProcessLatestResults(
      output, current_time, &found_command, &score, &is_new_command);
  if (process_status != kTfLiteOk) {
    error_reporter->Report("RecognizeCommands::ProcessLatestResults() failed");
    return;
  }
  // Do something based on the recognized command. The default implementation
  // just prints to the error console, but you should replace this with your
  // own function for a real application.
  RespondToCommand(error_reporter, current_time, found_command, score,
                   is_new_command);
{                   
  long irValue = particleSensor.getIR();
  if (checkForBeat(irValue) == true)
  {
    //We sensed a beat!
    long delta = millis() - lastBeat;
    lastBeat = millis();
    beatsPerMinute = 60 / (delta / 1000.0);
    if (beatsPerMinute < 255 && beatsPerMinute > 20)
    {
      rates[rateSpot++] = (byte)beatsPerMinute; //Store this reading in the array
      rateSpot %= RATE_SIZE; //Wrap variable
      //Take average of readings
      beatAvg = 0;
      for (byte x = 0 ; x < RATE_SIZE ; x++)
        beatAvg += rates[x];
      beatAvg /= RATE_SIZE;
    }
  }

if (irValue > 60000)
{
  //Serial.print("IR=");
  //Serial.print(irValue);
  Serial.print(" BPM=");
  Serial.print(beatsPerMinute);
  Serial.print(", Avg BPM=");
  Serial.print(beatAvg);
  Serial.println();
}

  unsigned int proxValue = proximitySensor.getProximity(); 
if (proxValue>14000){
  Serial.println("");
  Serial.println("Tap Activated");
  Serial.print("...Sending the accumulated Sensor data to Local-Hub");
  //Serial.print(proxValue);
  Serial.println();
  delay(200);
  digitalWrite(blinkPin, HIGH);
  delay(150);
  digitalWrite(blinkPin, LOW);
  delay(400);
  digitalWrite(blinkPin, HIGH);
  delay(150);
  digitalWrite(blinkPin, LOW);
  delay(400);
  digitalWrite(blinkPin, HIGH);
  delay(80);
  digitalWrite(blinkPin, LOW);
  delay(240);
  digitalWrite(blinkPin, HIGH);
  delay(80);
  digitalWrite(blinkPin, LOW);
  delay(240);
  digitalWrite(blinkPin, HIGH);
  delay(80);
  digitalWrite(blinkPin, LOW);
  delay(240);
  
}

int middleZ = (oled.getLCDWidth() / 2)-14;
 int middleX = oled.getLCDWidth() / 2;
  int middleY = oled.getLCDHeight() / 2;
  oled.clear(PAGE);
   oled.setCursor(middleZ - (oled.getFontWidth() * (2)+3),
                 middleY - (oled.getFontHeight() / 2)-17);
oled.print("HEART-RATE");
//   oled.setCursor(middleZ - (oled.getFontWidth() * (2)-6),
  //               middleY - (oled.getFontHeight() / 2)-8);
//oled.print("Be Heart SMART!");
  
  oled.setCursor(middleX - (oled.getFontWidth() * (2)+1),
                 middleY - (oled.getFontHeight() / 2)+17);
  oled.print(beatAvg);
  oled.print("bpm");
  //delay(7000);
  oled.display();
  oled.clear(PAGE);
}                   
}

Main Script#2

Arduino
arduino_audio_provider.cpp
// AM_BSP_NUM_LEDS : LED initialization and management per EVB target (# of
// LEDs defined in EVB BSP) 

#include "audio_provider.h"

#include <limits>

// These are headers from Ambiq's Apollo3 SDK.
#include "am_bsp.h"         // NOLINT
#include "am_mcu_apollo.h"  // NOLINT
#include "am_util.h"        // NOLINT

#include "micro_features_micro_model_settings.h"

namespace {

// These are the raw buffers that are filled by the PDM during DMA
constexpr int kPdmNumSlots = 1;
constexpr int kPdmSamplesPerSlot = 256;
constexpr int kPdmSampleBufferSize = (kPdmNumSlots * kPdmSamplesPerSlot);
uint32_t g_ui32PDMSampleBuffer0[kPdmSampleBufferSize];
uint32_t g_ui32PDMSampleBuffer1[kPdmSampleBufferSize];
uint32_t g_PowerOff = 0;

// Controls the double buffering between the two DMA buffers.
int g_dma_destination_index = 0;
// PDM Device Handle.
static void* g_pdm_handle;
// PDM DMA error flag.
volatile bool g_pdm_dma_error;
// So the interrupt can use the passed-in error handler to report issues.
tflite::ErrorReporter* g_pdm_dma_error_reporter = nullptr;

// Holds a longer history of audio samples in a ring buffer.
constexpr int kAudioCaptureBufferSize = 16000;
int16_t g_audio_capture_buffer[kAudioCaptureBufferSize] = {};
int g_audio_capture_buffer_start = 0;
int64_t g_total_samples_captured = 0;
int32_t g_latest_audio_timestamp = 0;

// Copy of audio samples returned to the caller.
int16_t g_audio_output_buffer[kMaxAudioSampleSize];
bool g_is_audio_initialized = false;

//*****************************************************************************
//
// Globals
//
//*****************************************************************************

// ARPIT TODO : Implement low power configuration
void custom_am_bsp_low_power_init(void) {
  // Initialize for low power in the power control block
  // am_hal_pwrctrl_low_power_init();

  // Run the RTC off the LFRC.
  // am_hal_rtc_osc_select(AM_HAL_RTC_OSC_LFRC);

  // Stop the XTAL.
  // am_hal_clkgen_control(AM_HAL_CLKGEN_CONTROL_XTAL_STOP, 0);

  // Disable the RTC.
  // am_hal_rtc_osc_disable();

}  // am_bsp_low_power_init()

// Make sure the CPU is running as fast as possible.
void enable_burst_mode(tflite::ErrorReporter* error_reporter) {
  am_hal_burst_avail_e eBurstModeAvailable;
  am_hal_burst_mode_e eBurstMode;

  // Check that the Burst Feature is available.
  if (AM_HAL_STATUS_SUCCESS ==
      am_hal_burst_mode_initialize(&eBurstModeAvailable)) {
    if (AM_HAL_BURST_AVAIL == eBurstModeAvailable) {
      error_reporter->Report("Apollo3 Burst Mode is Available\n");
    } else {
      error_reporter->Report("Apollo3 Burst Mode is Not Available\n");
    }
  } else {
    error_reporter->Report("Failed to Initialize for Burst Mode operation\n");
  }

  // Put the MCU into "Burst" mode.
  if (AM_HAL_STATUS_SUCCESS == am_hal_burst_mode_enable(&eBurstMode)) {
    if (AM_HAL_BURST_MODE == eBurstMode) {
      error_reporter->Report("Apollo3 operating in Burst Mode (96MHz)\n");
    }
  } else {
    error_reporter->Report("Failed to Enable Burst Mode operation\n");
  }
}

}  // namespace

//*****************************************************************************
// PDM configuration information.
//*****************************************************************************
am_hal_pdm_config_t g_sPdmConfig = {
    .eClkDivider = AM_HAL_PDM_MCLKDIV_1,
    .eLeftGain = AM_HAL_PDM_GAIN_P165DB,
    .eRightGain = AM_HAL_PDM_GAIN_P165DB,
    .ui32DecimationRate =
        48,  // OSR = 1500/16 = 96 = 2*SINCRATE --> SINC_RATE = 48
    .bHighPassEnable = 1,
    .ui32HighPassCutoff = 0x2,
    .ePDMClkSpeed = AM_HAL_PDM_CLK_1_5MHZ,
    .bInvertI2SBCLK = 0,
    .ePDMClkSource = AM_HAL_PDM_INTERNAL_CLK,
    .bPDMSampleDelay = 0,
    .bDataPacking = 0,
    .ePCMChannels = AM_BSP_PDM_CHANNEL,
    .ui32GainChangeDelay = 1,
    .bI2SEnable = 0,
    .bSoftMute = 0,
    .bLRSwap = 0,
};

//*****************************************************************************
// PDM initialization.
//*****************************************************************************
extern "C" void pdm_init(void) {
  //
  // Initialize, power-up, and configure the PDM.
  //
  am_hal_pdm_initialize(0, &g_pdm_handle);
  am_hal_pdm_power_control(g_pdm_handle, AM_HAL_PDM_POWER_ON, false);
  am_hal_pdm_configure(g_pdm_handle, &g_sPdmConfig);

  //
  // Configure the necessary pins.
  //
  am_hal_gpio_pinconfig(AM_BSP_PDM_CLOCK, g_AM_BSP_PDM_CLOCK);
  am_hal_gpio_pinconfig(AM_BSP_PDM_DATA, g_AM_BSP_PDM_DATA);

  //
  // Configure and enable PDM interrupts (set up to trigger on DMA
  // completion).
  //
  am_hal_pdm_interrupt_enable(g_pdm_handle, ( AM_HAL_PDM_INT_DERR   | 
                                              AM_HAL_PDM_INT_DCMP   |
                                              AM_HAL_PDM_INT_UNDFL  | 
                                              AM_HAL_PDM_INT_OVF));

  NVIC_EnableIRQ(PDM_IRQn);

  // Enable PDM
  am_hal_pdm_enable(g_pdm_handle);
}

// Start the DMA fetch of PDM samples.
void pdm_start_dma(tflite::ErrorReporter* error_reporter) {
  // Configure DMA and target address.
  am_hal_pdm_transfer_t sTransfer;

  if (g_dma_destination_index == 0) {
    sTransfer.ui32TargetAddr = (uint32_t)g_ui32PDMSampleBuffer0;
  } else {
    sTransfer.ui32TargetAddr = (uint32_t)g_ui32PDMSampleBuffer1;
  }

  sTransfer.ui32TotalCount = 4 * kPdmSampleBufferSize;
  // PDM DMA count is in Bytes

  // Start the data transfer.
  if (AM_HAL_STATUS_SUCCESS != am_hal_pdm_dma_start(g_pdm_handle, &sTransfer)) {
    error_reporter->Report("Error - configuring PDM DMA failed.");
  }

  // Reset the PDM DMA flags.
  g_pdm_dma_error = false;
}

// Interrupt handler for the PDM.
extern "C" void am_pdm_isr(void) {
  uint32_t ui32IntMask;

  // Read the interrupt status.
  if (AM_HAL_STATUS_SUCCESS !=
      am_hal_pdm_interrupt_status_get(g_pdm_handle, &ui32IntMask, false)) {
    g_pdm_dma_error_reporter->Report("Error reading PDM0 interrupt status.");
  }

  // Clear the PDM interrupt.
  if (AM_HAL_STATUS_SUCCESS !=
      am_hal_pdm_interrupt_clear(g_pdm_handle, ui32IntMask)) {
    g_pdm_dma_error_reporter->Report("Error clearing PDM interrupt status.");
  }

#if USE_DEBUG_GPIO
  // DEBUG : GPIO flag polling.
  am_hal_gpio_state_write(31, AM_HAL_GPIO_OUTPUT_SET);  // Slot1 AN pin
#endif

  // If we got a DMA complete, set the flag.
  if (ui32IntMask & AM_HAL_PDM_INT_OVF) {
    am_util_stdio_printf("\n%s\n", "\nPDM ISR OVF.");
  }
  if (ui32IntMask & AM_HAL_PDM_INT_UNDFL) {
    am_util_stdio_printf("\n%s\n", "\nPDM ISR UNDLF.");
  }
  if (ui32IntMask & AM_HAL_PDM_INT_DCMP) {
    uint32_t* source_buffer;
    if (g_dma_destination_index == 0) {
      source_buffer = g_ui32PDMSampleBuffer0;
      g_dma_destination_index = 1;
    } else {
      source_buffer = g_ui32PDMSampleBuffer1;
      g_dma_destination_index = 0;
    }
    pdm_start_dma(g_pdm_dma_error_reporter);

    uint32_t slotCount = 0;
    for (uint32_t indi = 0; indi < kPdmSampleBufferSize; indi++) {
      g_audio_capture_buffer[g_audio_capture_buffer_start] =
          source_buffer[indi];
      g_audio_capture_buffer_start =
          (g_audio_capture_buffer_start + 1) % kAudioCaptureBufferSize;
      slotCount++;
    }

    g_total_samples_captured += slotCount;
    g_latest_audio_timestamp =
        (g_total_samples_captured / (kAudioSampleFrequency / 1000));
  }

  // If we got a DMA error, set the flag.
  if (ui32IntMask & AM_HAL_PDM_INT_DERR) {
    g_pdm_dma_error = true;
  }

#if USE_DEBUG_GPIO
  // DEBUG : GPIO flag polling.
  am_hal_gpio_state_write(31, AM_HAL_GPIO_OUTPUT_CLEAR);  // Slot1 AN pin
#endif
}

TfLiteStatus InitAudioRecording(tflite::ErrorReporter* error_reporter) {
  // Set the clock frequency.
  if (AM_HAL_STATUS_SUCCESS !=
      am_hal_clkgen_control(AM_HAL_CLKGEN_CONTROL_SYSCLK_MAX, 0)) {
    error_reporter->Report("Error - configuring the system clock failed.");
    return kTfLiteError;
  }

  // Individually select elements of am_bsp_low_power_init
  custom_am_bsp_low_power_init();

  // Set the default cache configuration and enable it.
  if (AM_HAL_STATUS_SUCCESS !=
      am_hal_cachectrl_config(&am_hal_cachectrl_defaults)) {
    error_reporter->Report("Error - configuring the system cache failed.");
    return kTfLiteError;
  }
  if (AM_HAL_STATUS_SUCCESS != am_hal_cachectrl_enable()) {
    error_reporter->Report("Error - enabling the system cache failed.");
    return kTfLiteError;
  }

  // Configure Flash wait states.
  CACHECTRL->FLASHCFG_b.RD_WAIT = 1;      // Default is 3
  CACHECTRL->FLASHCFG_b.SEDELAY = 6;      // Default is 7
  CACHECTRL->FLASHCFG_b.LPM_RD_WAIT = 5;  // Default is 8

  // Enable cache sleep states.
  uint32_t ui32LPMMode = CACHECTRL_FLASHCFG_LPMMODE_STANDBY;
  if (am_hal_cachectrl_control(AM_HAL_CACHECTRL_CONTROL_LPMMODE_SET,
                               &ui32LPMMode)) {
    error_reporter->Report("Error - enabling cache sleep state failed.");
  }

  // Enable Instruction & Data pre-fetching.
  MCUCTRL->SRAMMODE_b.DPREFETCH = 1;
  MCUCTRL->SRAMMODE_b.DPREFETCH_CACHE = 1;
  MCUCTRL->SRAMMODE_b.IPREFETCH = 1;
  MCUCTRL->SRAMMODE_b.IPREFETCH_CACHE = 1;

  // Enable the floating point module, and configure the core for lazy stacking.
  am_hal_sysctrl_fpu_enable();
  am_hal_sysctrl_fpu_stacking_enable(true);
  error_reporter->Report("FPU Enabled.");

  // Ensure the CPU is running as fast as possible.
  // enable_burst_mode(error_reporter);

  // Configure, turn on PDM
  pdm_init();
  am_hal_interrupt_master_enable();
  am_hal_pdm_fifo_flush(g_pdm_handle);
  // Trigger the PDM DMA for the first time manually.
  g_pdm_dma_error_reporter = error_reporter;
  pdm_start_dma(g_pdm_dma_error_reporter);

  error_reporter->Report("\nPDM DMA Threshold = %d", PDMn(0)->FIFOTHR);

  return kTfLiteOk;
}

TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter,
                             int start_ms, int duration_ms,
                             int* audio_samples_size, int16_t** audio_samples) {
  if (!g_is_audio_initialized) {
    TfLiteStatus init_status = InitAudioRecording(error_reporter);
    if (init_status != kTfLiteOk) {
      return init_status;
    }
    g_is_audio_initialized = true;
  }

  // This should only be called when the main thread notices that the latest
  // audio sample data timestamp has changed, so that there's new data in the
  // capture ring buffer. The ring buffer will eventually wrap around and
  // overwrite the data, but the assumption is that the main thread is checking
  // often enough and the buffer is large enough that this call will be made
  // before that happens.
  const int start_offset =
      (start_ms < 0) ? 0 : start_ms * (kAudioSampleFrequency / 1000);
  const int duration_sample_count =
      duration_ms * (kAudioSampleFrequency / 1000);
  for (int i = 0; i < duration_sample_count; ++i) {
    const int capture_index = (start_offset + i) % kAudioCaptureBufferSize;
    g_audio_output_buffer[i] = g_audio_capture_buffer[capture_index];
  }
  
  *audio_samples_size = kMaxAudioSampleSize;
  *audio_samples = g_audio_output_buffer;

  return kTfLiteOk;
}

int32_t LatestAudioTimestamp() { return g_latest_audio_timestamp; }

Main Script#3

Arduino
arduino_command_responder.cpp
#include "command_responder.h"

#include "am_bsp.h"  // NOLINT

// This implementation will light up the LEDs on the board in response to
// different commands.
void RespondToCommand(tflite::ErrorReporter* error_reporter,
                      int32_t current_time, const char* found_command,
                      uint8_t score, bool is_new_command) {
  static bool is_initialized = false;
  if (!is_initialized) {
    // Setup LED's as outputs
    // EAC am_hal_gpio_pinconfig(AM_BSP_GPIO_LED_RED, g_AM_HAL_GPIO_OUTPUT_12);
    am_hal_gpio_pinconfig(AM_BSP_GPIO_LED_BLUE, g_AM_HAL_GPIO_OUTPUT_12);
    // EAC am_hal_gpio_pinconfig(AM_BSP_GPIO_LED_GREEN, g_AM_HAL_GPIO_OUTPUT_12);
    // EAC am_hal_gpio_pinconfig(AM_BSP_GPIO_LED_YELLOW, g_AM_HAL_GPIO_OUTPUT_12);
    is_initialized = true;
  }
  static int count = 0;

  // Toggle the blue LED every time an inference is performed.
  //++count;
  //if (count & 1) {
  //  am_hal_gpio_output_set(AM_BSP_GPIO_LED_BLUE);
  //} else {
  //  am_hal_gpio_output_clear(AM_BSP_GPIO_LED_BLUE);
  //}

  // Turn on the yellow LED if 'yes' was heard.
  // EAC am_hal_gpio_output_clear(AM_BSP_GPIO_LED_RED);
  // EAC am_hal_gpio_output_clear(AM_BSP_GPIO_LED_YELLOW);
  // EAC am_hal_gpio_output_clear(AM_BSP_GPIO_LED_GREEN);
  if (is_new_command) {
    error_reporter->Report("Heard %s (%d) @%dms", found_command, score,
                           current_time);
    if (found_command[0] == 'y') {
      error_reporter->Report("\nYES");
      // EAC am_hal_gpio_output_set(AM_BSP_GPIO_LED_YELLOW);
      am_hal_gpio_output_set(AM_BSP_GPIO_LED_BLUE);
      //delay(3000);
    }
    if (found_command[0] == 'n') {
      error_reporter->Report("\nNO");
      // EAC am_hal_gpio_output_set(AM_BSP_GPIO_LED_RED);
      am_hal_gpio_output_clear(AM_BSP_GPIO_LED_BLUE);
    }
    if (found_command[0] == 'u') {
      error_reporter->Report("\nUNKNOWN");
      // EAC am_hal_gpio_output_set(AM_BSP_GPIO_LED_GREEN);
      //am_hal_gpio_output_set(AM_BSP_GPIO_LED_BLUE);
      //am_hal_gpio_output_clear(AM_BSP_GPIO_LED_BLUE);
    }
  }
}

Main Script#4

Arduino
arduino_main.cpp
#include "main_functions.h"

// Arduino automatically calls the setup() and loop() functions in a sketch, so
// where other systems need their own main routine in this file, it can be left
// empty.

Main Script#5

Arduino
audio_provider.h
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_

#include "tensorflow/lite/c/c_api_internal.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"

// This is an abstraction around an audio source like a microphone, and is
// expected to return 16-bit PCM sample data for a given point in time. The
// sample data itself should be used as quickly as possible by the caller, since
// to allow memory optimizations there are no guarantees that the samples won't
// be overwritten by new data in the future. In practice, implementations should
// ensure that there's a reasonable time allowed for clients to access the data
// before any reuse.
// The reference implementation can have no platform-specific dependencies, so
// it just returns an array filled with zeros. For real applications, you should
// ensure there's a specialized implementation that accesses hardware APIs.
TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter,
                             int start_ms, int duration_ms,
                             int* audio_samples_size, int16_t** audio_samples);

// Returns the time that audio data was last captured in milliseconds. There's
// no contract about what time zero represents, the accuracy, or the granularity
// of the result. Subsequent calls will generally not return a lower value, but
// even that's not guaranteed if there's an overflow wraparound.
// The reference implementation of this function just returns a constantly
// incrementing value for each call, since it would need a non-portable platform
// call to access time information. For real applications, you'll need to write
// your own platform-specific implementation.
int32_t LatestAudioTimestamp();

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_

Main Script#6

Arduino
command_responder.h
// Provides an interface to take an action based on an audio command.

#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_

#include "tensorflow/lite/c/c_api_internal.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"

// Called every time the results of an audio recognition run are available. The
// human-readable name of any recognized command is in the `found_command`
// argument, `score` has the numerical confidence, and `is_new_command` is set
// if the previous command was different to this one.
void RespondToCommand(tflite::ErrorReporter* error_reporter,
                      int32_t current_time, const char* found_command,
                      uint8_t score, bool is_new_command);

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_

Main Script#7

Arduino
feature_provider.cpp
#include "feature_provider.h"

#include "audio_provider.h"
#include "micro_features_micro_features_generator.h"
#include "micro_features_micro_model_settings.h"

FeatureProvider::FeatureProvider(int feature_size, uint8_t* feature_data)
    : feature_size_(feature_size),
      feature_data_(feature_data),
      is_first_run_(true) {
  // Initialize the feature data to default values.
  for (int n = 0; n < feature_size_; ++n) {
    feature_data_[n] = 0;
  }
}

FeatureProvider::~FeatureProvider() {}

TfLiteStatus FeatureProvider::PopulateFeatureData(
    tflite::ErrorReporter* error_reporter, int32_t last_time_in_ms,
    int32_t time_in_ms, int* how_many_new_slices) {
  if (feature_size_ != kFeatureElementCount) {
    error_reporter->Report("Requested feature_data_ size %d doesn't match %d",
                           feature_size_, kFeatureElementCount);
    return kTfLiteError;
  }

  // Quantize the time into steps as long as each window stride, so we can
  // figure out which audio data we need to fetch.
  const int last_step = (last_time_in_ms / kFeatureSliceStrideMs);
  const int current_step = (time_in_ms / kFeatureSliceStrideMs);

  int slices_needed = current_step - last_step;
  // If this is the first call, make sure we don't use any cached information.
  if (is_first_run_) {
    TfLiteStatus init_status = InitializeMicroFeatures(error_reporter);
    if (init_status != kTfLiteOk) {
      return init_status;
    }
    is_first_run_ = false;
    slices_needed = kFeatureSliceCount;
  }
  if (slices_needed > kFeatureSliceCount) {
    slices_needed = kFeatureSliceCount;
  }
  *how_many_new_slices = slices_needed;

  const int slices_to_keep = kFeatureSliceCount - slices_needed;
  const int slices_to_drop = kFeatureSliceCount - slices_to_keep;
  // If we can avoid recalculating some slices, just move the existing data
  // up in the spectrogram, to perform something like this:
  // last time = 80ms          current time = 120ms
  // +-----------+             +-----------+
  // | data@20ms |         --> | data@60ms |
  // +-----------+       --    +-----------+
  // | data@40ms |     --  --> | data@80ms |
  // +-----------+   --  --    +-----------+
  // | data@60ms | --  --      |  <empty>  |
  // +-----------+   --        +-----------+
  // | data@80ms | --          |  <empty>  |
  // +-----------+             +-----------+
  if (slices_to_keep > 0) {
    for (int dest_slice = 0; dest_slice < slices_to_keep; ++dest_slice) {
      uint8_t* dest_slice_data =
          feature_data_ + (dest_slice * kFeatureSliceSize);
      const int src_slice = dest_slice + slices_to_drop;
      const uint8_t* src_slice_data =
          feature_data_ + (src_slice * kFeatureSliceSize);
      for (int i = 0; i < kFeatureSliceSize; ++i) {
        dest_slice_data[i] = src_slice_data[i];
      }
    }
  }
  // Any slices that need to be filled in with feature data have their
  // appropriate audio data pulled, and features calculated for that slice.
  if (slices_needed > 0) {
    for (int new_slice = slices_to_keep; new_slice < kFeatureSliceCount;
         ++new_slice) {
      const int new_step = (current_step - kFeatureSliceCount + 1) + new_slice;
      const int32_t slice_start_ms = (new_step * kFeatureSliceStrideMs);
      int16_t* audio_samples = nullptr;
      int audio_samples_size = 0;
      // TODO(petewarden): Fix bug that leads to non-zero slice_start_ms
      GetAudioSamples(error_reporter, (slice_start_ms > 0 ? slice_start_ms : 0),
                      kFeatureSliceDurationMs, &audio_samples_size,
                      &audio_samples);
      if (audio_samples_size < kMaxAudioSampleSize) {
        error_reporter->Report("Audio data size %d too small, want %d",
                               audio_samples_size, kMaxAudioSampleSize);
        return kTfLiteError;
      }
      uint8_t* new_slice_data = feature_data_ + (new_slice * kFeatureSliceSize);
      size_t num_samples_read;
      TfLiteStatus generate_status = GenerateMicroFeatures(
          error_reporter, audio_samples, audio_samples_size, kFeatureSliceSize,
          new_slice_data, &num_samples_read);
      if (generate_status != kTfLiteOk) {
        return generate_status;
      }
    }
  }
  return kTfLiteOk;
}

Main Script#8

Arduino
feature_provider.h
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_

#include "tensorflow/lite/c/c_api_internal.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"

// Binds itself to an area of memory intended to hold the input features for an
// audio-recognition neural network model, and fills that data area with the
// features representing the current audio input, for example from a microphone.
// The audio features themselves are a two-dimensional array, made up of
// horizontal slices representing the frequencies at one point in time, stacked
// on top of each other to form a spectrogram showing how those frequencies
// changed over time.
class FeatureProvider {
 public:
  // Create the provider, and bind it to an area of memory. This memory should
  // remain accessible for the lifetime of the provider object, since subsequent
  // calls will fill it with feature data. The provider does no memory
  // management of this data.
  FeatureProvider(int feature_size, uint8_t* feature_data);
  ~FeatureProvider();

  // Fills the feature data with information from audio inputs, and returns how
  // many feature slices were updated.
  TfLiteStatus PopulateFeatureData(tflite::ErrorReporter* error_reporter,
                                   int32_t last_time_in_ms, int32_t time_in_ms,
                                   int* how_many_new_slices);

 private:
  int feature_size_;
  uint8_t* feature_data_;
  // Make sure we don't try to use cached information if this is the first call
  // into the provider.
  bool is_first_run_;
};

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_

Main Script#9

Arduino
main_functions.h
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_

// Initializes all data needed for the example. The name is important, and needs
// to be setup() for Arduino compatibility.
void setup();

// Runs one iteration of data gathering and inference. This should be called
// repeatedly from the application code. The name needs to be loop() for Arduino
// compatibility.
void loop();

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_

Main Script#10

Arduino
micro_features_micro_features_generator.cpp
#include "micro_features_micro_features_generator.h"

#include <cmath>
#include <cstring>

#include "micro_features_micro_model_settings.h"
#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h"
#include "tensorflow/lite/experimental/microfrontend/lib/frontend_util.h"

// Configure FFT to output 16 bit fixed point.
#define FIXED_POINT 16

namespace {

FrontendState g_micro_features_state;
bool g_is_first_time = true;

}  // namespace

TfLiteStatus InitializeMicroFeatures(tflite::ErrorReporter* error_reporter) {
  FrontendConfig config;
  config.window.size_ms = kFeatureSliceDurationMs;
  config.window.step_size_ms = kFeatureSliceStrideMs;
  config.noise_reduction.smoothing_bits = 10;
  config.filterbank.num_channels = kFeatureSliceSize;
  config.filterbank.lower_band_limit = 125.0;
  config.filterbank.upper_band_limit = 7500.0;
  config.noise_reduction.smoothing_bits = 10;
  config.noise_reduction.even_smoothing = 0.025;
  config.noise_reduction.odd_smoothing = 0.06;
  config.noise_reduction.min_signal_remaining = 0.05;
  config.pcan_gain_control.enable_pcan = 1;
  config.pcan_gain_control.strength = 0.95;
  config.pcan_gain_control.offset = 80.0;
  config.pcan_gain_control.gain_bits = 21;
  config.log_scale.enable_log = 1;
  config.log_scale.scale_shift = 6;
  if (!FrontendPopulateState(&config, &g_micro_features_state,
                             kAudioSampleFrequency)) {
    error_reporter->Report("FrontendPopulateState() failed");
    return kTfLiteError;
  }
  g_is_first_time = true;
  return kTfLiteOk;
}

// This is not exposed in any header, and is only used for testing, to ensure
// that the state is correctly set up before generating results.
void SetMicroFeaturesNoiseEstimates(const uint32_t* estimate_presets) {
  for (int i = 0; i < g_micro_features_state.filterbank.num_channels; ++i) {
    g_micro_features_state.noise_reduction.estimate[i] = estimate_presets[i];
  }
}

TfLiteStatus GenerateMicroFeatures(tflite::ErrorReporter* error_reporter,
                                   const int16_t* input, int input_size,
                                   int output_size, uint8_t* output,
                                   size_t* num_samples_read) {
  const int16_t* frontend_input;
  if (g_is_first_time) {
    frontend_input = input;
    g_is_first_time = false;
  } else {
    frontend_input = input + 160;
  }
  FrontendOutput frontend_output = FrontendProcessSamples(
      &g_micro_features_state, frontend_input, input_size, num_samples_read);

  for (int i = 0; i < frontend_output.size; ++i) {
    // These scaling values are derived from those used in input_data.py in the
    // training pipeline.
    constexpr int32_t value_scale = (10 * 255);
    constexpr int32_t value_div = (256 * 26);
    int32_t value =
        ((frontend_output.values[i] * value_scale) + (value_div / 2)) /
        value_div;
    if (value < 0) {
      value = 0;
    }
    if (value > 255) {
      value = 255;
    }
    output[i] = value;
  }

  return kTfLiteOk;
}

Main Script#11

Arduino
micro_features_micro_features_generator.h
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_

#include "tensorflow/lite/c/c_api_internal.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"

// Sets up any resources needed for the feature generation pipeline.
TfLiteStatus InitializeMicroFeatures(tflite::ErrorReporter* error_reporter);

// Converts audio sample data into a more compact form that's appropriate for
// feeding into a neural network.
TfLiteStatus GenerateMicroFeatures(tflite::ErrorReporter* error_reporter,
                                   const int16_t* input, int input_size,
                                   int output_size, uint8_t* output,
                                   size_t* num_samples_read);

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_

Main Script#12

Arduino
micro_features_micro_model_settings.cpp
#include "micro_features_micro_model_settings.h"

const char* kCategoryLabels[kCategoryCount] = {
    "silence",
    "unknown",
    "yes",
    "no",
};

Main Script#13

Arduino
micro_features_micro_model_settings.h
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_

// Keeping these as constant expressions allow us to allocate fixed-sized arrays
// on the stack for our working memory.

// The size of the input time series data we pass to the FFT to produce the
// frequency information. This has to be a power of two, and since we're dealing
// with 30ms of 16KHz inputs, which means 480 samples, this is the next value.
constexpr int kMaxAudioSampleSize = 512;
constexpr int kAudioSampleFrequency = 16000;

// All of these values are derived from the values used during model training,
// if you change your model you'll need to update these constants.
constexpr int kFeatureSliceSize = 40;
constexpr int kFeatureSliceCount = 49;
constexpr int kFeatureElementCount = (kFeatureSliceSize * kFeatureSliceCount);
constexpr int kFeatureSliceStrideMs = 20;
constexpr int kFeatureSliceDurationMs = 30;

constexpr int kCategoryCount = 4;
constexpr int kSilenceIndex = 0;
constexpr int kUnknownIndex = 1;
extern const char* kCategoryLabels[kCategoryCount];

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_

Main Script#14

Arduino
micro_features_no_micro_features_data.cpp
#include "micro_features_no_micro_features_data.h"

/* File automatically created by
 * tensorflow/examples/speech_commands/wav_to_features.py \
 * --sample_rate=16000 \
 * --clip_duration_ms=1000 \
 * --window_size_ms=30 \
 * --window_stride_ms=20 \
 * --feature_bin_count=40 \
 * --quantize=1 \
 * --preprocess="micro" \
 * --input_wav="speech_commands_test_set_v0.02/no/f9643d42_nohash_4.wav" \
 * --output_c_file="/tmp/no_micro_features_data.cc" \
 */

const int g_no_micro_f9643d42_nohash_4_width = 40;
const int g_no_micro_f9643d42_nohash_4_height = 49;
const unsigned char g_no_micro_f9643d42_nohash_4_data[] = {
    230, 205, 191, 203, 202, 181, 180, 194, 205, 187, 183, 197, 203, 198, 196,
    186, 202, 159, 151, 126, 110, 138, 141, 142, 137, 148, 133, 120, 110, 126,
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    133, 135, 0,   171, 139, 132, 128, 140, 51,  126, 107, 161, 0,   95,  51,
    119, 0,   114, 0,   95,  110, 116, 51,  112, 0,   90,  0,   116, 51,  68,
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    0,   148, 128, 157, 120, 146, 120, 120, 0,   95,  78,  141, 68,  68,  0,
    68,  0,   90,  0,   85,  0,   107, 0,   78,  0,   85,  51,  102, 0,   68,
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    173, 85,  116, 141, 164, 68,  150, 123, 133, 51,  114, 0,   117, 68,  150,
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    110, 196, 85,  205, 183, 169, 0,   99,  0,   0,   0,   0,   0,   0,   0,
    0,   0,   0,   0,   141, 0,   112, 0,   0,   0,   134, 0,   0,   0,   0,
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    0,   105, 0,   0,   0,   105, 0,   0,   0,   0,   0,   0,   0,   95,  0,
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    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
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    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
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    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
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    0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
};

Main Script#15

Arduino
micro_features_no_micro_features_data.h
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_

extern const int g_no_micro_f9643d42_nohash_4_width;
extern const int g_no_micro_f9643d42_nohash_4_height;
extern const unsigned char g_no_micro_f9643d42_nohash_4_data[];

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_

Main Script#16

Arduino
micro_features_tiny_conv_micro_features_model_data.cpp
// Automatically created from a TensorFlow Lite flatbuffer using the command:
// xxd -i tiny_conv.tflite > tiny_conv_simple_features_model_data.cc
// See the README for a full description of the creation process.

#include "micro_features_tiny_conv_micro_features_model_data.h"

// We need to keep the data array aligned on some architectures.
#ifdef __has_attribute
#define HAVE_ATTRIBUTE(x) __has_attribute(x)
#else
#define HAVE_ATTRIBUTE(x) 0
#endif
#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))
#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4)))
#else
#define DATA_ALIGN_ATTRIBUTE
#endif

const unsigned char
    g_tiny_conv_micro_features_model_data[] DATA_ALIGN_ATTRIBUTE = {
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        0xb2, 0x73, 0x81, 0x84, 0xc8, 0x81, 0x88, 0x72, 0xbe, 0x85, 0x86, 0x97,
        0xd3, 0x8a, 0xc7, 0x75, 0xce, 0x9c, 0x69, 0xa6, 0xb0, 0xa1, 0x8e, 0x64,
        0xb1, 0xa6, 0x67, 0xaa, 0xcd, 0x95, 0x97, 0xa2, 0xb2, 0xb2, 0x85, 0x9a,
        0x9d, 0xa3, 0x5e, 0x73, 0x6e, 0xae, 0x50, 0x83, 0x8c, 0xab, 0x92, 0x43,
        0x6b, 0x66, 0x43, 0x5c, 0x8f, 0x8a, 0x9a, 0x6c, 0x84, 0x48, 0x80, 0x6b,
        0x8d, 0x82, 0xaf, 0x89, 0x71, 0x9f, 0xa4, 0x9a, 0x7b, 0x68, 0x91, 0xaa,
        0x6b, 0xa3, 0x9c, 0x62, 0x8d, 0x6d, 0x87, 0x87, 0x81, 0x9a, 0x97, 0x6c,
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        0x7d, 0xb2, 0xa6, 0x98, 0xa1, 0x7b, 0x8e, 0x83, 0x8c, 0xa2, 0x7e, 0x73,
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        0x7d, 0xa3, 0x79, 0x80, 0x9e, 0x6b, 0xa9, 0x6c, 0x80, 0xb6, 0x81, 0xa6,
        0x92, 0x5b, 0xb7, 0x99, 0x81, 0x7e, 0x8e, 0x89, 0x97, 0x86, 0x93, 0x86,
        0x7b, 0x9a, 0x7f, 0x9a, 0x8e, 0x69, 0xa3, 0xa4, 0x9f, 0x8b, 0x96, 0x6f,
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        0x73, 0x6d, 0x86, 0x8e, 0x6b, 0x80, 0xa7, 0x9d, 0x91, 0x73, 0x95, 0x70,
        0x80, 0xc3, 0x9f, 0x8b, 0x72, 0x86, 0x6b, 0xad, 0x76, 0xbe, 0xad, 0x8e,
...

This file has been truncated, please download it to see its full contents.

Main Script#17

Arduino
micro_features_tiny_conv_micro_features_model_data.h
// This is a standard TensorFlow Lite model file that has been converted into a
// C data array, so it can be easily compiled into a binary for devices that
// don't have a file system. It was created using the command:
// xxd -i tiny_conv.tflite > tiny_conv_simple_features_model_data.cc

#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_

extern const unsigned char g_tiny_conv_micro_features_model_data[];
extern const int g_tiny_conv_micro_features_model_data_len;

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_TINY_CONV_MICRO_FEATURES_MODEL_DATA_H_

Main Script#18

Arduino
micro_features_yes_micro_features_data.cpp
#include "micro_features_yes_micro_features_data.h"

/* File automatically created by
 * tensorflow/examples/speech_commands/wav_to_features.py \
 * --sample_rate=16000 \
 * --clip_duration_ms=1000 \
 * --window_size_ms=30 \
 * --window_stride_ms=20 \
 * --feature_bin_count=40 \
 * --quantize=1 \
 * --preprocess="micro" \
 * --input_wav="speech_commands_test_set_v0.02/yes/f2e59fea_nohash_1.wav" \
 * --output_c_file="yes_micro_features_data.cc" \
 */

const int g_yes_micro_f2e59fea_nohash_1_width = 40;
const int g_yes_micro_f2e59fea_nohash_1_height = 49;
const unsigned char g_yes_micro_f2e59fea_nohash_1_data[] = {
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};

Main Script#19

Arduino
micro_features_yes_micro_features_data.h
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_

extern const int g_yes_micro_f2e59fea_nohash_1_width;
extern const int g_yes_micro_f2e59fea_nohash_1_height;
extern const unsigned char g_yes_micro_f2e59fea_nohash_1_data[];

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_

Main Script#20

Arduino
recognize_commands.cpp
#include "recognize_commands.h"

#include <limits>

RecognizeCommands::RecognizeCommands(tflite::ErrorReporter* error_reporter,
                                     int32_t average_window_duration_ms,
                                     uint8_t detection_threshold,
                                     int32_t suppression_ms,
                                     int32_t minimum_count)
    : error_reporter_(error_reporter),
      average_window_duration_ms_(average_window_duration_ms),
      detection_threshold_(detection_threshold),
      suppression_ms_(suppression_ms),
      minimum_count_(minimum_count),
      previous_results_(error_reporter) {
  previous_top_label_ = "silence";
  previous_top_label_time_ = std::numeric_limits<int32_t>::min();
}

TfLiteStatus RecognizeCommands::ProcessLatestResults(
    const TfLiteTensor* latest_results, const int32_t current_time_ms,
    const char** found_command, uint8_t* score, bool* is_new_command) {
  if ((latest_results->dims->size != 2) ||
      (latest_results->dims->data[0] != 1) ||
      (latest_results->dims->data[1] != kCategoryCount)) {
    error_reporter_->Report(
        "The results for recognition should contain %d elements, but there are "
        "%d in an %d-dimensional shape",
        kCategoryCount, latest_results->dims->data[1],
        latest_results->dims->size);
    return kTfLiteError;
  }

  if (latest_results->type != kTfLiteUInt8) {
    error_reporter_->Report(
        "The results for recognition should be uint8 elements, but are %d",
        latest_results->type);
    return kTfLiteError;
  }

  if ((!previous_results_.empty()) &&
      (current_time_ms < previous_results_.front().time_)) {
    error_reporter_->Report(
        "Results must be fed in increasing time order, but received a "
        "timestamp of %d that was earlier than the previous one of %d",
        current_time_ms, previous_results_.front().time_);
    return kTfLiteError;
  }

  // Add the latest results to the head of the queue.
  previous_results_.push_back({current_time_ms, latest_results->data.uint8});

  // Prune any earlier results that are too old for the averaging window.
  const int64_t time_limit = current_time_ms - average_window_duration_ms_;
  while ((!previous_results_.empty()) &&
         previous_results_.front().time_ < time_limit) {
    previous_results_.pop_front();
  }

  // If there are too few results, assume the result will be unreliable and
  // bail.
  const int64_t how_many_results = previous_results_.size();
  const int64_t earliest_time = previous_results_.front().time_;
  const int64_t samples_duration = current_time_ms - earliest_time;
  if ((how_many_results < minimum_count_) ||
      (samples_duration < (average_window_duration_ms_ / 4))) {
    *found_command = previous_top_label_;
    *score = 0;
    *is_new_command = false;
    return kTfLiteOk;
  }

  // Calculate the average score across all the results in the window.
  int32_t average_scores[kCategoryCount];
  for (int offset = 0; offset < previous_results_.size(); ++offset) {
    PreviousResultsQueue::Result previous_result =
        previous_results_.from_front(offset);
    const uint8_t* scores = previous_result.scores_;
    for (int i = 0; i < kCategoryCount; ++i) {
      if (offset == 0) {
        average_scores[i] = scores[i];
      } else {
        average_scores[i] += scores[i];
      }
    }
  }
  for (int i = 0; i < kCategoryCount; ++i) {
    average_scores[i] /= how_many_results;
  }

  // Find the current highest scoring category.
  int current_top_index = 0;
  int32_t current_top_score = 0;
  for (int i = 0; i < kCategoryCount; ++i) {
    if (average_scores[i] > current_top_score) {
      current_top_score = average_scores[i];
      current_top_index = i;
    }
  }
  const char* current_top_label = kCategoryLabels[current_top_index];

  // If we've recently had another label trigger, assume one that occurs too
  // soon afterwards is a bad result.
  int64_t time_since_last_top;
  if ((previous_top_label_ == kCategoryLabels[0]) ||
      (previous_top_label_time_ == std::numeric_limits<int32_t>::min())) {
    time_since_last_top = std::numeric_limits<int32_t>::max();
  } else {
    time_since_last_top = current_time_ms - previous_top_label_time_;
  }
  if ((current_top_score > detection_threshold_) &&
      ((current_top_label != previous_top_label_) ||
       (time_since_last_top > suppression_ms_))) {
    previous_top_label_ = current_top_label;
    previous_top_label_time_ = current_time_ms;
    *is_new_command = true;
  } else {
    *is_new_command = false;
  }
  *found_command = current_top_label;
  *score = current_top_score;

  return kTfLiteOk;
}

Main Script#21

Arduino
recognize_commands.h
#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_

#include <cstdint>

#include "tensorflow/lite/c/c_api_internal.h"
#include "micro_features_micro_model_settings.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"

// Partial implementation of std::dequeue, just providing the functionality
// that's needed to keep a record of previous neural network results over a
// short time period, so they can be averaged together to produce a more
// accurate overall prediction. This doesn't use any dynamic memory allocation
// so it's a better fit for microcontroller applications, but this does mean
// there are hard limits on the number of results it can store.
class PreviousResultsQueue {
 public:
  PreviousResultsQueue(tflite::ErrorReporter* error_reporter)
      : error_reporter_(error_reporter), front_index_(0), size_(0) {}

  // Data structure that holds an inference result, and the time when it
  // was recorded.
  struct Result {
    Result() : time_(0), scores_() {}
    Result(int32_t time, uint8_t* scores) : time_(time) {
      for (int i = 0; i < kCategoryCount; ++i) {
        scores_[i] = scores[i];
      }
    }
    int32_t time_;
    uint8_t scores_[kCategoryCount];
  };

  int size() { return size_; }
  bool empty() { return size_ == 0; }
  Result& front() { return results_[front_index_]; }
  Result& back() {
    int back_index = front_index_ + (size_ - 1);
    if (back_index >= kMaxResults) {
      back_index -= kMaxResults;
    }
    return results_[back_index];
  }

  void push_back(const Result& entry) {
    if (size() >= kMaxResults) {
      error_reporter_->Report(
          "Couldn't push_back latest result, too many already!");
      return;
    }
    size_ += 1;
    back() = entry;
  }

  Result pop_front() {
    if (size() <= 0) {
      error_reporter_->Report("Couldn't pop_front result, none present!");
      return Result();
    }
    Result result = front();
    front_index_ += 1;
    if (front_index_ >= kMaxResults) {
      front_index_ = 0;
    }
    size_ -= 1;
    return result;
  }

  // Most of the functions are duplicates of dequeue containers, but this
  // is a helper that makes it easy to iterate through the contents of the
  // queue.
  Result& from_front(int offset) {
    if ((offset < 0) || (offset >= size_)) {
      error_reporter_->Report("Attempt to read beyond the end of the queue!");
      offset = size_ - 1;
    }
    int index = front_index_ + offset;
    if (index >= kMaxResults) {
      index -= kMaxResults;
    }
    return results_[index];
  }

 private:
  tflite::ErrorReporter* error_reporter_;
  static constexpr int kMaxResults = 50;
  Result results_[kMaxResults];

  int front_index_;
  int size_;
};

// This class is designed to apply a very primitive decoding model on top of the
// instantaneous results from running an audio recognition model on a single
// window of samples. It applies smoothing over time so that noisy individual
// label scores are averaged, increasing the confidence that apparent matches
// are real.
// To use it, you should create a class object with the configuration you
// want, and then feed results from running a TensorFlow model into the
// processing method. The timestamp for each subsequent call should be
// increasing from the previous, since the class is designed to process a stream
// of data over time.
class RecognizeCommands {
 public:
  // labels should be a list of the strings associated with each one-hot score.
  // The window duration controls the smoothing. Longer durations will give a
  // higher confidence that the results are correct, but may miss some commands.
  // The detection threshold has a similar effect, with high values increasing
  // the precision at the cost of recall. The minimum count controls how many
  // results need to be in the averaging window before it's seen as a reliable
  // average. This prevents erroneous results when the averaging window is
  // initially being populated for example. The suppression argument disables
  // further recognitions for a set time after one has been triggered, which can
  // help reduce spurious recognitions.
  explicit RecognizeCommands(tflite::ErrorReporter* error_reporter,
                             int32_t average_window_duration_ms = 1000,
                             uint8_t detection_threshold = 200,
                             int32_t suppression_ms = 1500,
                             int32_t minimum_count = 3);

  // Call this with the results of running a model on sample data.
  TfLiteStatus ProcessLatestResults(const TfLiteTensor* latest_results,
                                    const int32_t current_time_ms,
                                    const char** found_command, uint8_t* score,
                                    bool* is_new_command);

 private:
  // Configuration
  tflite::ErrorReporter* error_reporter_;
  int32_t average_window_duration_ms_;
  uint8_t detection_threshold_;
  int32_t suppression_ms_;
  int32_t minimum_count_;

  // Working variables
  PreviousResultsQueue previous_results_;
  const char* previous_top_label_;
  int32_t previous_top_label_time_;
};

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_

Environment Sensing with OLED

Arduino
#include <Wire.h>
#include <SparkFunBME280.h> //Click here to get the library: http://librarymanager/All#SparkFun_BME280
#include <SparkFunCCS811.h> //Click here to get the library: http://librarymanager/All#SparkFun_CCS811
#define CCS811_ADDR 0x5B //Default I2C Address
//#define CCS811_ADDR 0x5A //Alternate I2C Address
CCS811 myCCS811(CCS811_ADDR);
BME280 myBME280;
#include <SFE_MicroOLED.h>  // Include the SFE_MicroOLED library
#define PIN_RESET 9  
//The DC_JUMPER is the I2C Address Select jumper. Set to 1 if the jumper is open (Default), or set to 0 if it's closed.
#define DC_JUMPER 1 
MicroOLED oled(PIN_RESET, DC_JUMPER);

void setup()
{
  Serial.begin(9600);
  Wire.begin();
  CCS811Core::status returnCode = myCCS811.begin();
  Serial.print("CCS811 begin exited with: ");
  printDriverError( returnCode );
  Serial.println();
  myBME280.settings.commInterface = I2C_MODE;
  myBME280.settings.I2CAddress = 0x77;
  myBME280.settings.commInterface = I2C_MODE;
  myBME280.settings.I2CAddress = 0x77;
  myBME280.settings.runMode = 3; //Normal mode
  myBME280.settings.tStandby = 0;
  myBME280.settings.filter = 4;
  myBME280.settings.tempOverSample = 5;
  myBME280.settings.pressOverSample = 5;
  myBME280.settings.humidOverSample = 5;
  delay(10);  //Make sure sensor had enough time to turn on. BME280 requires 2ms to start up.
  myBME280.begin();

  delay(100);
  Wire.begin();
  oled.begin();    // Initialize the OLED
  oled.clear(ALL); // Clear the display's internal memory
  oled.display();  // Display what's in the buffer (splashscreen)
  delay(1000);     // Delay 1000 ms
  oled.clear(PAGE); // Clear the buffer.
}
//---------------------------------------------------------------
void loop()
{
  if (myCCS811.dataAvailable())
  {
    myCCS811.readAlgorithmResults();
    printInfoSerial();
    float BMEtempC = myBME280.readTempC();
    float BMEhumid = myBME280.readFloatHumidity();
  myCCS811.setEnvironmentalData(BMEhumid, BMEtempC);
  }
  else if (myCCS811.checkForStatusError())
  {
    printSensorError();
  }
  delay(1000); //Wait for next reading
//------------------------------------------------------
  int middleZ = (oled.getLCDWidth() / 2)-14;
 int middleX = oled.getLCDWidth() / 2;
  int middleY = oled.getLCDHeight() / 2;
  oled.clear(PAGE);
   oled.setCursor(middleZ - (oled.getFontWidth() * (2)),
                 middleY - (oled.getFontHeight() / 2)-14);
oled.print("CCS / BME");
   oled.setCursor(middleZ - (oled.getFontWidth() * (2)-15),
                 middleY - (oled.getFontHeight() / 2)+5);
oled.print("CO2");
  
  oled.setCursor(middleX - (oled.getFontWidth() * (2)+5),
                 middleY - (oled.getFontHeight() / 2)+17);
  oled.print(myCCS811.getCO2());
  oled.print("ppm");
  delay(7000);
  oled.display();
  oled.clear(PAGE);
//  int middleZ = (oled.getLCDWidth() / 2)-14;
// int middleX = oled.getLCDWidth() / 2;
 // int middleY = oled.getLCDHeight() / 2;
  oled.clear(PAGE);
   oled.setCursor(middleZ - (oled.getFontWidth() * (2)+8),
                 middleY - (oled.getFontHeight() / 2)-14);
oled.print("T :");
   oled.setCursor(middleZ - (oled.getFontWidth() * (2)+8),
                 middleY - (oled.getFontHeight() / 2));
oled.print("A :");
   oled.setCursor(middleZ - (oled.getFontWidth() * (2)+8),
                 middleY - (oled.getFontHeight() / 2)+14);
oled.print("H :");
  
  oled.setCursor(middleX - (oled.getFontWidth() * (2)),
                 middleY - (oled.getFontHeight() / 2)-14);
  oled.print(myBME280.readTempC(), 1);
  oled.print(" C");
    oled.setCursor(middleX - (oled.getFontWidth() * (2)),
                 middleY - (oled.getFontHeight() / 2));
  oled.print(myBME280.readFloatAltitudeMeters(), 1);
  oled.print(" m");
  oled.setCursor(middleX - (oled.getFontWidth() * (2)),
                 middleY - (oled.getFontHeight() / 2)+14);
  oled.print(myBME280.readFloatHumidity(), 1);
  oled.print(" %");
  delay(3000);
  oled.display();
  oled.clear(PAGE);
}

//---------------------------------------------------------------
void printInfoSerial()
{
  Serial.println("CCS811 data:");
  Serial.print(" CO2 concentration : ");
  Serial.print(myCCS811.getCO2());
  Serial.println(" ppm");
  Serial.print(" TVOC concentration : ");
  Serial.print(myCCS811.getTVOC());
  Serial.println(" ppb");
  Serial.println("BME280 data:");
  Serial.print(" Temperature: ");
  Serial.print(myBME280.readTempC(), 2);
  Serial.println(" degrees C");
  Serial.print(" Pressure: ");
  Serial.print((myBME280.readFloatPressure() * 0.0002953), 2);
  Serial.println(" InHg");
  Serial.print(" Altitude: ");
  Serial.print(myBME280.readFloatAltitudeMeters(), 2);
  Serial.println("m");
  Serial.print(" %RH: ");
  Serial.print(myBME280.readFloatHumidity(), 2);
  Serial.println(" %");
  Serial.println();

}

void printDriverError( CCS811Core::status errorCode )
{
  switch ( errorCode )
  {
    case CCS811Core::SENSOR_SUCCESS:
      Serial.print("SUCCESS");
      break;
    case CCS811Core::SENSOR_ID_ERROR:
      Serial.print("ID_ERROR");
      break;
    case CCS811Core::SENSOR_I2C_ERROR:
      Serial.print("I2C_ERROR");
      break;
    case CCS811Core::SENSOR_INTERNAL_ERROR:
      Serial.print("INTERNAL_ERROR");
      break;
    case CCS811Core::SENSOR_GENERIC_ERROR:
      Serial.print("GENERIC_ERROR");
      break;
    default:
      Serial.print("Unspecified error.");
  }
}

void printSensorError()
{
  uint8_t error = myCCS811.getErrorRegister();

  if ( error == 0xFF ) //comm error
  {
    Serial.println("Failed to get ERROR_ID register.");
  }
  else
  {
    Serial.print("Error: ");
    if (error & 1 << 5) Serial.print("HeaterSupply");
    if (error & 1 << 4) Serial.print("HeaterFault");
    if (error & 1 << 3) Serial.print("MaxResistance");
    if (error & 1 << 2) Serial.print("MeasModeInvalid");
    if (error & 1 << 1) Serial.print("ReadRegInvalid");
    if (error & 1 << 0) Serial.print("MsgInvalid");
    Serial.println();
  }
}

Biosig acquisition Artemis_AD8232

Arduino
void setup() {
  // initialize the serial communication:
  Serial.begin(9600);
  pinMode(22, INPUT); //leads off detection LO +
  pinMode(23, INPUT); //leads off detection LO -

}

void loop() {
  
  if((digitalRead(22) == 1)||(digitalRead(23) == 1)){
    Serial.println('^');
  }
  else{
      Serial.println(analogRead(A29));
  }
  delay(50);
}

Biosig acquistion HRV & Sync. OLED

Arduino
#include <Wire.h>
#include "MAX30105.h"
#include "heartRate.h"
MAX30105 particleSensor;
#include <SFE_MicroOLED.h>  // Include the SFE_MicroOLED library
#define PIN_RESET 9  
#define DC_JUMPER 1 
MicroOLED oled(PIN_RESET, DC_JUMPER);
#define blinkPin LED_BUILTIN
#include "SparkFun_VCNL4040_Arduino_Library.h"

VCNL4040 proximitySensor;

const byte RATE_SIZE = 4; //Increase this for more averaging. 4 is good.
byte rates[RATE_SIZE]; //Array of heart rates
byte rateSpot = 0;
long lastBeat = 0; //Time at which the last beat occurred
float beatsPerMinute;
int beatAvg;

void setup()
{
  Serial.begin(9600);
  if (!particleSensor.begin(Wire, I2C_SPEED_FAST)) //Use default I2C port, 400kHz speed
  {
    Serial.println("MAX30105 was not found. Please check wiring/power. ");
    while (1);
  }
  particleSensor.setup(); //Configure sensor with default settings
  particleSensor.setPulseAmplitudeRed(0x0A); //Turn Red LED to low to indicate sensor is running
  particleSensor.setPulseAmplitudeGreen(0); //Turn off Green LED

  if (proximitySensor.begin() == false)
  {
    Serial.println("Device not found. Please check wiring.");
    while (1); //Freeze!
  }
  pinMode(blinkPin, OUTPUT);
  Wire.begin();
  oled.begin();    // Initialize the OLED
  oled.clear(ALL); // Clear the display's internal memory
  oled.display();  // Display what's in the buffer (splashscreen)
  delay(1000);     // Delay 1000 ms
  oled.clear(PAGE); // Clear the buffer.
}

void loop()
{
  long irValue = particleSensor.getIR();
  if (checkForBeat(irValue) == true)
  {
    //We sensed a beat!
    long delta = millis() - lastBeat;
    lastBeat = millis();
    beatsPerMinute = 60 / (delta / 1000.0);
    if (beatsPerMinute < 255 && beatsPerMinute > 20)
    {
      rates[rateSpot++] = (byte)beatsPerMinute; //Store this reading in the array
      rateSpot %= RATE_SIZE; //Wrap variable
      //Take average of readings
      beatAvg = 0;
      for (byte x = 0 ; x < RATE_SIZE ; x++)
        beatAvg += rates[x];
      beatAvg /= RATE_SIZE;
    }
  }

if (irValue > 60000)
{
  //Serial.print("IR=");
  //Serial.print(irValue);
  Serial.print(" BPM=");
  Serial.print(beatsPerMinute);
  Serial.print(", Avg BPM=");
  Serial.print(beatAvg);
  Serial.println();
}

  unsigned int proxValue = proximitySensor.getProximity(); 
if (proxValue>14000){
  Serial.println("");
  Serial.println("Tap Activated");
  Serial.print("...Sending the accumulated Sensor data to Local-Hub");
  //Serial.print(proxValue);
  Serial.println();
  delay(200);
  digitalWrite(blinkPin, HIGH);
  delay(150);
  digitalWrite(blinkPin, LOW);
  delay(400);
  digitalWrite(blinkPin, HIGH);
  delay(150);
  digitalWrite(blinkPin, LOW);
  delay(400);
  digitalWrite(blinkPin, HIGH);
  delay(80);
  digitalWrite(blinkPin, LOW);
  delay(240);
  digitalWrite(blinkPin, HIGH);
  delay(80);
  digitalWrite(blinkPin, LOW);
  delay(240);
  digitalWrite(blinkPin, HIGH);
  delay(80);
  digitalWrite(blinkPin, LOW);
  delay(240);
  
}

int middleZ = (oled.getLCDWidth() / 2)-14;
 int middleX = oled.getLCDWidth() / 2;
  int middleY = oled.getLCDHeight() / 2;
  oled.clear(PAGE);
   oled.setCursor(middleZ - (oled.getFontWidth() * (2)+3),
                 middleY - (oled.getFontHeight() / 2)-17);
oled.print("HEART-RATE");
//   oled.setCursor(middleZ - (oled.getFontWidth() * (2)-6),
  //               middleY - (oled.getFontHeight() / 2)-8);
//oled.print("Be Heart SMART!");
  
  oled.setCursor(middleX - (oled.getFontWidth() * (2)+1),
                 middleY - (oled.getFontHeight() / 2)+17);
  oled.print(beatAvg);
  oled.print("bpm");
  //delay(7000);
  oled.display();
  oled.clear(PAGE);
}

AlexaRing script for ESP32 with support for TFlite mic.

Arduino
#include <Adafruit_NeoPixel.h>
    
#define DATA_PIN 13
#define ArtemisMIC 12
#define SIZE 13 /* Echo (Dot) has 12 LEDs, but code adapts to higher numbers */

Adafruit_NeoPixel ring = Adafruit_NeoPixel(24, DATA_PIN, NEO_GRB + NEO_KHZ800);

const int DURATION = 950; // animation duration (ms)
const int STEP_SIZE = DURATION/SIZE;
const uint32_t NOTHING = ring.Color(255, 0, 0);
const uint32_t MUTE = ring.Color(153, 0, 0);
const uint32_t BOOTING_BG = ring.Color(0, 0, 90);
const uint32_t BOOTING_FG = ring.Color(0, 230, 230);
const uint32_t SPEAK_MIN = ring.Color(0, 0, 255);
const uint32_t SPEAK_MAX = ring.Color(0, 255, 255);
const uint32_t ERR_MIN = ring.Color(17, 17, 0);
const uint32_t ERR_MAX = ring.Color(255, 17, 0);
const uint32_t MSG_MIN = ring.Color(17, 17, 0);   // unverified
const uint32_t MSG_MAX = ring.Color(255, 255, 0); // unverified
const uint32_t CALL_MIN = ring.Color(0, 17, 0);   // unverified
const uint32_t CALL_MAX = ring.Color(0, 255, 0);  // unverified
// TODO: purple (do not disturb), orange spin (connecting)


uint32_t interpolate(uint32_t c1, uint32_t c2, double t) {
  // assumes 0.0 <= t <= 1.0
  uint8_t r = (c1>>16 & 0xFF)*(1-t) + (c2>>16 & 0xFF)*t;
  uint8_t g = (c1>>8  & 0xFF)*(1-t) + (c2>>8  & 0xFF)*t;
  uint8_t b = (c1     & 0xFF)*(1-t) + (c2     & 0xFF)*t;
  return ((uint32_t)r << 16) | ((uint32_t)g << 8) | b;
}


// for: nothing/listening, muted, do not disturb
void fill(uint32_t colour) {
  for (int i=0; i<SIZE; i++) {
    ring.setPixelColor(i, colour);
  }
}

// for: booting
void spinner(uint32_t colour_bg, uint32_t colour_fg, int pos, int width=ceil(SIZE/12.0));
void spinner(uint32_t colour_bg, uint32_t colour_fg, int pos, int width) {
  fill(colour_bg);
  for (int i=pos; i<pos+width; i++) {
    ring.setPixelColor(i % SIZE, colour_fg); // rotate clockwise
  }
}

// for: speaking, error, don't disturb, incoming call, ..
void pulse(uint32_t colour_min, uint32_t colour_max, double ratio, bool up) {
  double t = (up) ? ratio : 1 - ratio;
  uint32_t colour = interpolate(colour_min, colour_max, t);
  fill(colour);
}


/*** SKETCH ***/

int counter = 0;  // step function (spinner)
double ratio = 0; // continuous (pulse)
bool up = true;   // direction (pulse)
unsigned long last_increment = millis();
enum states {
  BLANK,
  MUTED,
  BOOTING,
  SPEAKING,
  ERROR,
  MESSAGE,
  CALL,
} state;

void setup() {
  ring.begin();
  ring.show();
  ring.setBrightness(100);
  pinMode(ArtemisMIC, INPUT_PULLUP);

  state = BOOTING;
}

void loop() {
  switch (state) {
    case MUTED:    fill(MUTE);                               break;
    case BOOTING:  spinner(BOOTING_BG, BOOTING_FG, counter); break;
    case SPEAKING: pulse(SPEAK_MIN, SPEAK_MAX, ratio, up);   break;
    case ERROR:    pulse(ERR_MIN, ERR_MAX, ratio, up);       break;
    case MESSAGE:  pulse(MSG_MIN, MSG_MAX, ratio, up);       break;
    case CALL:     pulse(CALL_MIN, CALL_MAX, ratio, up);     break;
    case BLANK:
    default:       fill(MESSAGE);                            break;
  }
  ring.show();

  if (millis() - last_increment > STEP_SIZE || millis() < last_increment) {
    // 0->SIZE per DURATION
    counter = ++counter % SIZE;
    if (counter == 0) {
      up = !up; // swap
    }
    last_increment = millis();
  }
  // 0.0->1.0 per duration
  ratio = min(1.0, (double)(counter*STEP_SIZE + (millis() - last_increment)) / DURATION);


  // YOUR LOGIC (state transitions)
  // This example sketch shows the 'booting' animation,
  // and switches to the 'muted' animation as long as the S.R._mic behaviour changes
  int MICresponse = digitalRead(ArtemisMIC);
  switch (state) {
    case BOOTING: if (!MICresponse) { state = MUTED;   }  break;
    case MUTED:   if (MICresponse)  { state = BOOTING; }  break;
    default:      state = MESSAGE;                          break;
  }
  
  delay(1);
}

Credits

Night R

Night R

8 projects • 13 followers
R&D Engineer @ IoT Solutions Provider, Robotics Engineer @ SIS Corp., Passionate for Hardware hacking, 12+ years experience in programming..

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