Time to work on uploading data to Edge Impulse ...

 

Here is a view of my project on my Edge Impulse dashboard:

 

There are a few different methods for acquiring the data needed to train the model (impulse).  Using the Edge Impulse CLI, you can stream data from your device using the Data forwarder or upload data files using the Uploader.  You can also send data files to the Ingestion service https://docs.edgeimpulse.com/reference  using HTTP/HTTPS.

 

Here are the choices you are presented if you press the "LET'S COLLECT SOME DATA" button:

Data forwarder

The quickest way to start acquiring data is using the data forwarder.  It streams serial data to Edge Impulse.  It requires a 3 step process.

 

Step 1: Run a program on the your device to stream data to the serial port of the host computer (I'm using a Win10 PC) and the WioTerminal is connected on COM8.

 

The program simply starts sending data at a fixed sample rate from the vibration sensor to the serial port.

I'm sampling at 500Hz and the vibration sensor is on Pin A0.

 

Wio_Terminal_Vibration_Forwarder.ino

#define FREQUENCY_HZ        500
#define INTERVAL_MS         (1000 / (FREQUENCY_HZ + 1))

void setup() {
    Serial.begin(115200);
    pinMode(A0, INPUT);
}

void loop() {
    static unsigned long last_interval_ms = 0;
    int VSBV203;

     if (millis() > last_interval_ms + INTERVAL_MS) {
        last_interval_ms = millis();
        VSBV203 = analogRead(A0);
        Serial.println(VSBV203);
     }
}

 

Step 2: Start the data forwarder.

 

The data forwarder is a node.js batch program (edge-impulse-data-forwarder) that is installed with the Edge Impulse CLI https://docs.edgeimpulse.com/docs/cli-installation  .

The first time you start the data forwarder, it requires your Edge Impulse username and password.  It then connects to your Edge Impulse dashboard and once you select the Project to associate the data, it downloads your credentials and connects the stream to Edge Impulse.  It also detects the data frequency on the port (in my case it is either 501 or 502 Hz which is slightly off the programmed rate of 500Hz).  Thereafter, it will continue to use these parameters until you reset them with a --clean argument.

 

Here is the transaction that followed the initial login:

 

Step 3: Start sampling on the Edge Impulse dashboard.

 

Once the data forwarder is running, a new recording panel will appear on the Data Acquisition panel.

 

You need to set the Label and Sample length.  Hit the "Start sampling" button and it will capture the data set.

 

 

The highlighted file and the associated plot are what was just captured.

I've been experimenting, so there are a lot of other files captured.  I'll need to go back and clean these out.

 

The data forwarder is a good way to quickly visualize the effects of program changes or using different methods of sensor attachment/positioning, but for acquiring the bulk of the training data I'm going to use the file capture methodology that I described in my previous post and I'll use the data uploader to move the data to Edge Impulse.

 

 

Uploader

Using the data uploader also requires a 3 step process.

 

Step 1:  Capture sample data into individual labelled files on an SD card per my previous post.

 

Step 2:  Process files on host computer to encode data into the required Data Acquisition format https://docs.edgeimpulse.com/reference#data-acquisition-format .

 

The encoded data is in either CBOR or JSON format, a file description header is added and the data is signed.

Edge Impulse has examples of conversion programs in C/C++,  Node.js, and Python.  I'm going to use the Python example that converts the CSV file into JSON format.

 

When I did my first trial of this process, I realized that because I had generated my timestamps using the millis() function, that my intervals were correct but I did not have a correct date/time reference because millis() records time relative to when the program started instead of the Unix time origin.  Precise date/time isn't important to me, but I'd like to know about when the samples files were captured.  I decided a good compromise would be to add a time offset to indicate the date/time that the sample file was converted for upload.

 

Here is the CSV to JSON encoder:

 

offset_convert.py

import csv, json, math, hmac, hashlib
import time

header = None
# keep track of the first row to know the beginning timestamp
first_row = True
begin_ts = 0
next_ts = 0
values = []

HMAC_KEY = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbb"

# offset timestamp to conversion time 
time_offset = int(time.time() * 1000)

# Parse the CSV file
with open('idle_6.csv', newline='') as csvfile:
    rows = csv.reader(csvfile, delimiter=',')
    for row in rows:
        if (not header):
            header = row
            continue

        if not begin_ts:
            begin_ts = int(row[0]) + time_offset
        elif not next_ts:
            next_ts = int(row[0]) + time_offset

        # skip over timestamp column, and add the rest
        values.append([ float(x) for x in row[1:] ])

# empty signature (all zeros). HS256 gives 32 byte signature, and we encode in hex, so we need 64 characters here
emptySignature = ''.join(['0'] * 64)

# This is the Edge Impulse Data Acquisition Format, it has the protected header
data = {
    "protected": {
        "ver": "v1",
        "alg": "HS256",
        "iat": math.floor(begin_ts / 1000) # epoch time, seconds since 1970 (the timestamp earlier was in ms.)
    },
    "signature": emptySignature,
    "payload": {
        "device_name": "WioTerminal",
        "device_type": "CSV_IMPORTER",
        "interval_ms": next_ts - begin_ts,
        "sensors": [ { "name": x, "units": "mV" } for x in header[1:] ],
        "values": values
    }
}

# encode in JSON
encoded = json.dumps(data)

# sign message
signature = hmac.new(bytes(HMAC_KEY, 'utf-8'), msg = encoded.encode('utf-8'), digestmod = hashlib.sha256).hexdigest()

# set the signature again in the message, and encode again
data['signature'] = signature
encoded = json.dumps(data)

print(encoded)

 

 

I'm running Python3.7 on the Win10 host, so I simply run the converter in a CMD window and redirect its output to a JSON file:

 

Here is the resulting JSON file:

 

{"protected": {"ver": "v1", "alg": "HS256", "iat": 1610601116}, "signature": "76073e977ae623f0817a0082e0cc60af2c9b9c330e7016803e9cdcc81093ee35", "payload": {"device_name": "WioTerminal", "device_type": "CSV_IMPORTER", "interval_ms": 2, "sensors": [{"name": "VSBV203", "units": "mV"}], "values": [[515.0], [516.0], [515.0], [516.0], [515.0], [516.0], [515.0], [515.0], [514.0], [516.0], [515.0], [515.0], [515.0], [515.0], [516.0], [516.0], [516.0], [515.0], [514.0], [515.0], [517.0], [516.0], [516.0], [515.0], [516.0], [515.0], [515.0], [515.0], [515.0], [514.0], [516.0], [515.0], [516.0], [515.0], [516.0], [516.0], [515.0], [516.0], [516.0], [515.0], [515.0], [514.0], [515.0], [515.0], [515.0], [515.0], [515.0], [516.0], [515.0], [515.0], [515.0], [514.0], [516.0], [515.0], [515.0], [516.0], [515.0], [515.0], [516.0], [515.0], [515.0], [515.0], [515.0], [516.0], [515.0], [515.0], [515.0], [514.0], [515.0], [515.0], [515.0], [516.0], [515.0], [515.0], [515.0], [515.0], [516.0], [515.0], 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Step 3: Upload the encoded file to Edge Impulse

 

On the Data Acquisition panel there is an "Upload" button.

That brings up an Upload Data panel where you can select the files to upload, whether they should be part of the Training or Testing data set, and what Label to apply:

 

An interesting thing that I've noted is that it is easy to identify which method I've used because the sample rate is always slightly off when using the data forwarder but not the uploader.  I imagine that's some consequence of using the serial interface for uploading.  You can see that in the two files below.

A similar data plot:

 

 

Next, on to collecting the real training data and training the model (impulse).

 

 

Links to my other blogs for this challenge

Person Identification using Footstep Vibration Patterns

Vibration Sensor Setup

Vibration Sensor - First Look

Vibration Sensor - Data Capture Setup

Vibration Sensor - Data Capture Program