Pewter is an open-source project for
1. Data acquisition
2. Analysis
3. Visualisation
of raw data from Myo and conduct experiments on it. If you are working on raw data from the Myo Armband, then you can make use of Pewter's simple GUI to acquire data and work on it. It not only reduces devlopement time but also makes the life of a machine learning engineer easier. You can create experiments and visualise the data for doing some analysis before preprocessing and feature extraction. All the experiment data will be saved in Json format which makes it even convenient for reuse. The entire project is created using Node.js and I thank Thalmiclabs for the myo.js library which made my life a lot more simpler.
Pewter was originally developed by me for data acquisition and analysis of raw data from Myo Armband for one of my projects Voice.
I would be glad if you could contribute in this initiative of mine to help other developers working on Myo Armbands and making their lives easier.
Feel free to give any suggestions or report bugs in the issues tab.
#Prerequisites
- Windows Operating System
- Node.js
- Myo Connect for windows
- Myo Armband
#Installation ###Install dependencies Enter the following command inside the pewter folder that you have cloned
npm install
###Run Pewter Enter the following command in the pewter directory to start pewter
node app.js
The app by default listens to port 3000. To change it to any other port update the listener in the the app.js file and restart.
###Start Using Pewter Open any browser and open the following link to start Pewter. Make sure to run MyoConnect before opening the page or else you will not see any data.
http://localhost:3000
The app by default listens to port 3000. To change it to any other port update the listener in the the app.js file and restart.
#How to use Pewter
Using Pewter is extremely simple. Follow the annotations on the images below and you will be able to kick off in a few minutes.
###Data Acquisition
The following are the functions of the buttons available,
- Play: Starts recording data.
- Pause: Pauses recording data.
- Undo: Undos the recording.
- save: Saves the experiment data.
- Visualizations**: Redirects to the visualization page.
Please give an 'Experiment Name' before saving data.
####Where is the data saved? The data is saved inside the 'experimentdata' directory inside the root directory of the project.
####What is the data format? The data is saved in json format. The following is the structure of a sample experiment data.
{
"expName": "test",
"emg": {
"data": [
[
13,
11,
5,
2,
22,
10,
3,
5
]
],
"timestamps": [
"1468000525763185"
]
},
"gyr": {
"data": [
[
-0.75,
-0.375,
2.0625
]
],
"timestamps": [
"1468000525755180"
]
},
"ori": {
"data": [
[
0.48699951171875,
-0.339599609375,
-0.69305419921875,
0.408935546875
]
],
"timestamps": [
"1468000525755180"
]
},
"acc": {
"data": [
[
-0.39404296875,
0.8837890625,
0.291015625
]
],
"timestamps": [
"1468000525755180"
]
}
}#####For EMG
| Key | Value (Data from sensor) |
|---|---|
| emg.data[0][0] | EMG POD 0 |
| emg.data[0][1] | EMG POD 1 |
| emg.data[0][2] | EMG POD 2 |
| emg.data[0][3] | EMG POD 3 |
| emg.data[0][4] | EMG POD 4 |
| emg.data[0][5] | EMG POD 5 |
| emg.data[0][6] | EMG POD 6 |
| emg.data[0][7] | EMG POD 7 |
| emg.timestamps[0] | Timestamp for emg.data[0] |
#####For Accelerometer
| Key | Value |
|---|---|
| acc.data[0][0] | Accelerometer_x |
| acc.data[0][1] | Accelerometer_y |
| acc.data[0][2] | Accelerometer_z |
| acc.timestamps[0] | Timestamp for acc.data[0] |
#####For Gyroscope
| Key | Value |
|---|---|
| gyr.data[0][0] | Gyroscope_x |
| gyr.data[0][1] | Gyroscope_y |
| gyr.data[0][2] | Gyroscope_z |
| gyr.timestamps[0] | Timestamp for gyr.data[0] |
#####For Orientation
| Key | Value |
|---|---|
| ori.data[0][0] | Orientation_x |
| ori.data[0][1] | Orientation_y |
| ori.data[0][2] | Orientation_z |
| ori.data[0][3] | Orientation_w |
| ori.timestamps[0] | Timestamp for ori.data[0] |
###Visualisation The visualisation page is even simpler. You can select an experiment from the experiment selection dropdown list. The graphs correspond to all the avaiable sensor data that Myo Armband provides. For visualisation I have used Ploty.js. I would like to thank them for the awesome work that they have done.
#For more information