Running Faucet Detection - Seeed XIAO Sense + Blues Cellular

A smart device that detects running faucets using a machine learning model, and sends alert messages over a cellular network.

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Running Faucet Detection - Seeed XIAO Sense + Blues Cellular

A smart device that detects running faucets using a machine learning model, and sends alert messages over a cellular network.

Created By: Naveen Kumar

Public Project Link:

GitHub Repository:

Project Demo

Story

Poor memory is only one of the many unpleasant experiences that accompany old age and these problems can have far-reaching implications on the comfort and security of seniors. Dementia is one of the most common neurological problems associated with the elderly. Imagine a case of seniors leaving the faucet on. The kind of water damage that might ensue is simply unimaginable. Not to mention lots of safety concerns such as electrocution and drowning. Also, sometimes kids or even adults forget to stop the faucet after use. It also adds up to your monthly water usage bills. According to the US EPA, leaving a faucet on for just five minutes wastes ten gallons of water. In this project, I have built a proof-of-concept of an AIoT (Artificial intelligence of things) device that can detect running faucets using a microphone and send an alert notification message.

Hardware Selection

The Notecarrier-B and Seeed XIAO nRF52840 Sense are connected over I2C.

The schematics are given below.

Model creation and training

Faucet - faucet is running, with a variety of background activities.
Noise - just background activities.

$ unzip faucet.zip
$ cd faucet
$ edge-impulse-uploader --clean
Edge Impulse uploader v1.16.0
? What is your user name or e-mail address (edgeimpulse.com)?

You will be prompted for your username, password, and the project where you want to add the dataset.

$ edge-impulse-uploader --category training faucet/training/*.cbor
$ edge-impulse-uploader --category testing faucet/testing/*.cbor

After uploading is finished we can see the data on the Data Acquisition page.

In the Impulse Design > Create Impulse page, we can add a processing block and learning block. We have chosen MFE for the processing block which extracts a spectrogram from audio signals using Mel-filterbank energy features, great for non-voice audio, and for the learning block, we have chosen Neural Network (Keras) which learns patterns from data and can apply these to new data for recognizing audio.

Now we need to generate features in the Impulse Design > MFE page. We can go with the default parameters.

After clicking on the Save Parameters button the page will redirect to the Generate Features page where we can start generating features which would take a few minutes. After feature generation, we can see the output in the Feature Explorer.

Now we can go to the Impulse Design > NN Classifier page where we can define the Neural Network architecture. We are using a 1-D convolutional network which is suitable for audio classification.

After finalizing the architecture, we can start training which will take a couple of minutes to finish. We can see the accuracy and confusion matrix below.

For such a small dataset 99.2% accuracy is pretty good so we will use this model.

Testing

We can test the model on the test datasets by going to the Model testing page and clicking on the Classify all button. The model has 91.24% accuracy on the test datasets, so we are confident that the model should work in a real environment.

Deployment

The Edge Impulse Studio and Blues Wireless Notecard both support Arduino libraries, so we will choose the Create Library > Arduino library option on the Deployment page. For the Select optimizations option, we will choose Enable EON Compiler, which reduces the memory usage of the model. Also, we will opt for the Quantized (Int8) model. Now click the Build button, and in a few seconds, the library bundle will be downloaded to your local computer.

Set up Blues Wireless Notecard and Notehub

We should copy the ProjectUID which is used by Notehub to associate the Notecard to the project created.

In the Filters section, we have to specify which Notecard outbound file data we want to route to Twilio. It would make sure that we always send out the intended data. In the application code, we would add notes to the twilio.qo file.

To send SMS messages, the Twilio API expects form data with three key/value pairs (Body, From, and To). This can be achieved using a JSONata (a query and transformation language for JSON data) expression to format the data into the required form. We should choose JSONata Expression in the Data > Transform field and we can enter the JSONata expression in the text area as shown below.

The JSONata expression is given below that formats the JSON payload to a Twilio API consumable message format.

( 
    "&Body=" & body.event & "&From=" & body.from & "&To=" & body.to & "&"
)

Running Inferencing

After the board package installation is completed, choose the Seeed XIAO BLE Sense from Tools > Board > Seeed nRF52 OS mbed-enabled Boards menu and select the serial port of the connected board from Tools > Port menu. We need to install the Blues Wireless Notecard library using the Library Manager (Tool > Manage Libraries...) as shown below.

Below is the Arduino sketch for inferencing. For continuous audio event detection, the application uses two threads, one for inferencing and another for audio data sampling so that no events should miss.

#include <Notecard.h>
#include <PDM.h>
#include <Wire.h>

#define EIDSP_QUANTIZE_FILTERBANK   0
#define EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW 3
#include <Running_Faucet_Blues_Wireless_inferencing.h>

#define serialDebugOut Serial
#define MY_PRODUCT_ID       "com.xxxxx.xxxxxxxx:running_faucet_detector"
#define FROM_PHONE_NUMBER   "+16xxxxxxxxx" // Twilio provided
#define TO_PHONE_NUMBER     "+8195xxxxxxxxx" // Target phone
#define CONTINUOUS_THRESOLD_SECS  (20)
#define NOTE_THRESOLD_SECS   (30)
#define LED_RED LEDR
#define LED_BLUE LEDB
#define FAUCET_IDX 0
#define NOISE_IDX  1

// Notecard instance
Notecard notecard;

static rtos::Thread inference_thread(osPriorityLow);

/** Audio buffers, pointers and selectors */
typedef struct {
  signed short *buffers[2];
  unsigned char buf_select;
  unsigned char buf_ready;
  unsigned int buf_count;
  unsigned int n_samples;
} inference_t;

static inference_t inference;
static bool record_ready = false;
static signed short *sampleBuffer;
static bool debug_nn = false;
static int print_results = -(EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW);

uint32_t continous_faucet_running_start_time;
uint32_t last_notification_sent_time;
uint8_t  prev_prediction = NOISE_IDX;

/* Forward declaration */
void run_inference_background();


void notecard_success()
{
  digitalWrite(LED_BLUE, LOW);
  delay(1000);
  digitalWrite(LED_BLUE, HIGH);
}

void notecard_error()
{
  digitalWrite(LED_RED, LOW);
  delay(1000);
  digitalWrite(LED_RED, HIGH);
}

void configure_notehub()
{
  // Setup Notehub
  J *req = notecard.newRequest("hub.set");
  if (req) {
    JAddStringToObject(req, "product", MY_PRODUCT_ID);
    JAddBoolToObject(req, "sync", true);
    JAddStringToObject(req, "mode", "periodic");
    JAddNumberToObject(req, "outbound", 24 * 60); // 1 day
    JAddNumberToObject(req, "inbound", 60); // 60 mins
    if (!notecard.sendRequest(req)) {
      notecard.logDebug("ERROR: Setup Notehub request\n");
      notecard_error();
    }
  } else {
    notecard.logDebug("ERROR: Failed to set notehub!\n");
    notecard_error();
  }
  notecard_success();
}

uint32_t get_current_timestamp_from_notecard()
{
  uint32_t timestamp = 0;

  J *rsp = notecard.requestAndResponse(notecard.newRequest("card.time"));

  if (rsp != NULL) {
    String zone = JGetString(rsp, "zone");
    if (zone != "UTC,Unknown") {
      timestamp = JGetNumber(rsp, "time");
    }
    notecard.deleteResponse(rsp);
  }

  return timestamp;
}

void send_alert_message()
{
  // Add a note
  J *req = notecard.newRequest("note.add");
  if (req != NULL) {
    // send immediately
    JAddBoolToObject(req, "sync", true);
    JAddStringToObject(req, "file", "twilio.qo");
    J *body = JCreateObject();
    if (body != NULL) {
      JAddStringToObject(body, "event", "Running Faucet Alert");
      JAddStringToObject(body, "from", FROM_PHONE_NUMBER);
      JAddStringToObject(body, "to", TO_PHONE_NUMBER);
      JAddItemToObject(req, "body", body);
    }

    if (!notecard.sendRequest(req)) {
      notecard.logDebug("ERROR: add note request\n");
      notecard_error();
    } else {
      ei_printf("Note sent!\n");
    }
  }
  notecard_success();
}

void setup()
{
  serialDebugOut.begin(115200);
  delay(1000);
  pinMode(LED_RED, OUTPUT);
  pinMode(LED_BLUE, OUTPUT);

  digitalWrite(LED_RED, HIGH);  // Off
  digitalWrite(LED_BLUE, HIGH); // Off

  //while (!serialDebugOut) {}

  Wire.begin();

  // Initialize Notecard with I2C communication
  notecard.begin(NOTE_I2C_ADDR_DEFAULT, NOTE_I2C_MAX_DEFAULT, Wire);
  notecard.setDebugOutputStream(serialDebugOut);

  configure_notehub();

  if (microphone_inference_start(EI_CLASSIFIER_SLICE_SIZE) == false) {
    ei_printf("ERR: Failed to setup audio sampling\r\n");
    return;
  }

  inference_thread.start(mbed::callback(&run_inference_background));

  last_notification_sent_time = get_current_timestamp_from_notecard();
}


// this loop only samples the audio data
void loop()
{
  bool m = microphone_inference_record();
  if (!m) {
    ei_printf("ERR: Failed to record audio...\n");
    return;
  }
}


// this loop inference continuously on another thread
void run_inference_background()
{
  run_classifier_init();

  while (1) {
    signal_t signal;
    signal.total_length = EI_CLASSIFIER_SLICE_SIZE;
    signal.get_data = &microphone_audio_signal_get_data;
    ei_impulse_result_t result = {0};

    EI_IMPULSE_ERROR r = run_classifier_continuous(&signal, &result, debug_nn);
    if (r != EI_IMPULSE_OK) {
      ei_printf("ERR: Failed to run classifier (%d)\n", r);
      return;
    }

    if (++print_results >= (EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW)) {
      ei_printf("Predictions ");
      ei_printf("(DSP: %d ms., Classification: %d ms.)",
                result.timing.dsp, result.timing.classification, result.timing.anomaly);
      ei_printf(": \n");
      for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
        ei_printf("    %s: %.5f\n", result.classification[ix].label,
                  result.classification[ix].value);
      }
      // above 80% confidence score
      if (result.classification[FAUCET_IDX].value > 0.8f) {
        uint32_t current_time = get_current_timestamp_from_notecard();
        if (prev_prediction == FAUCET_IDX) {
          ei_printf("Continuously Faucet running time: %ld\n", (current_time - continous_faucet_running_start_time));

          if ((current_time - continous_faucet_running_start_time) > CONTINUOUS_THRESOLD_SECS) {
            if (current_time - last_notification_sent_time > NOTE_THRESOLD_SECS) {
              send_alert_message();
              last_notification_sent_time = current_time;
            }
          }
        } else {
          // reset counter
          continous_faucet_running_start_time = current_time;
          ei_printf("Faucet running time reset\n");
        }
        prev_prediction = FAUCET_IDX;
      } else {
        prev_prediction = NOISE_IDX;
      }
      print_results = 0;
    }
  }
}

/**
   @brief      PDM buffer full callback
               Get data and call audio thread callback
*/
static void pdm_data_ready_inference_callback(void)
{
  int bytesAvailable = PDM.available();

  // read into the sample buffer
  int bytesRead = PDM.read((char *)&sampleBuffer[0], bytesAvailable);

  if (record_ready == true) {
    for (int i = 0; i<bytesRead >> 1; i++) {
      inference.buffers[inference.buf_select][inference.buf_count++] = sampleBuffer[i];

      if (inference.buf_count >= inference.n_samples) {
        inference.buf_select ^= 1;
        inference.buf_count = 0;
        inference.buf_ready = 1;
      }
    }
  }
}

static bool microphone_inference_start(uint32_t n_samples)
{
  inference.buffers[0] = (signed short *)malloc(n_samples * sizeof(signed short));

  if (inference.buffers[0] == NULL) {
    return false;
  }

  inference.buffers[1] = (signed short *)malloc(n_samples * sizeof(signed short));

  if (inference.buffers[1] == NULL) {
    free(inference.buffers[0]);
    return false;
  }

  sampleBuffer = (signed short *)malloc((n_samples >> 1) * sizeof(signed short));

  if (sampleBuffer == NULL) {
    free(inference.buffers[0]);
    free(inference.buffers[1]);
    return false;
  }

  inference.buf_select = 0;
  inference.buf_count = 0;
  inference.n_samples = n_samples;
  inference.buf_ready = 0;

  // configure the data receive callback
  PDM.onReceive(&pdm_data_ready_inference_callback);

  PDM.setBufferSize((n_samples >> 1) * sizeof(int16_t));

  // initialize PDM with:
  // - one channel (mono mode)
  // - a 16 kHz sample rate
  if (!PDM.begin(1, EI_CLASSIFIER_FREQUENCY)) {
    ei_printf("Failed to start PDM!");
  }

  // set the gain, defaults to 20
  PDM.setGain(75);

  record_ready = true;

  return true;
}

static bool microphone_inference_record(void)
{
  bool ret = true;

  if (inference.buf_ready == 1) {
    ei_printf(
      "Error sample buffer overrun. Decrease the number of slices per model window "
      "(EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW)\n");
    ret = false;
  }

  while (inference.buf_ready == 0) {
    delay(1);
  }

  inference.buf_ready = 0;

  return ret;
}

/**
   Get raw audio signal data
*/
static int microphone_audio_signal_get_data(size_t offset, size_t length, float *out_ptr)
{
  numpy::int16_to_float(&inference.buffers[inference.buf_select ^ 1][offset], out_ptr, length);

  return 0;
}

static void microphone_inference_end(void)
{
  PDM.end();
  free(inference.buffers[0]);
  free(inference.buffers[1]);
  free(sampleBuffer);
}

To run the inferencing sketch, clone the application GitHub repository using the command below.

$ git clone https://github.com/metanav/running_faucet_detection_blues_wireless.git

Import the library bundle running_faucet_blues_wireless_inferencing*.zip* using the menu Sketch > Include Library > Add.ZIP Library in the Arduino IDE. Open the inferencing sketch notecard_nano_ble_sense_running_faucet_detection.ino and compile/upload the firmware to the connected Seeed XIAO nRF52840 Sense board. We can monitor the inferencing output and Notecard debug logs using the Tools > Serial Monitor with a baud rate of 115200 bps.

Casing

For protection, the device is placed inside a plastic box that can be mounted on a wall.

The flexible cellular antenna is stuck to the side of the box.

Conclusion

Although this proof-of-concept device is used in the house with a wall outlet, it can be powered using batteries. Being equipped with cellular connectivity, it can be installed in those areas where there is no WiFi network. This is an easy-to-use and convenient device that respects users' privacy by running the inferencing at the edge and sending alert notifications on time.

PreviousEnvironmental Noise Classification - Nordic Thingy:53 NextVandalism Detection via Audio Classification - Arduino Nano 33 BLE Sense

Last updated 1 year ago

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