myo-armband-nn

Gesture recognition using myo armband via neural network (tensorflow library).

Requirement

Library	Version
Python	^3.5
Tensorflow	^1.1.0
Numpy	^1.12.0
sklearn	^0.18.1
myo-python	^0.2.2

Collecting data

You can use your own scripts for collecting EMG data from Myo armband. But you need to push 64-value array with data from each sensor.
By default myo-python returns 8-value array from each sensors. Each output return by 2-value array: [datetime, [EMG DATA]].
64 - value array its 8 output from armband. Just put it to one dimension array. So you just need to collect 8 values with gesture from armband (if you read data 10 times/s its not a problem).

In repo are collected dataset from Myo armband collected by me. Dataset contains only 5 gestures:

👍 - Ok    (1)
✊️ - Fist  (2)
✌️ - Like  (3)
🤘 - Rock  (4)
🖖 - Spock (5)

Training network

python3 train.py

75k iteration will take about 20 min on GTX 960 or 2h on i3-6100.

Prediction

Prediction on data from MYO armband

python3 predict.py

You must have installed MYO SDK. Script will return number (0-5) witch represent gesture (0 - relaxed arm).

Prediction on test dataset

python3 predict_test_dataset.py

Example output:

Accuracy on Test-Set: 90.74% (49 / 54)
[14  0  0  0] (0) Fist
[0 9 0 2] (1) Paper
[ 0  0 13  1] (2) Scissors
[ 0  0  2 13] (3) Ok

Model

Fully connected 1 (528 neurons)
ReLu
Fully connected 2 (786 neurons)
ReLu
Fully connected 3 (1248 neurons)
ReLu
Dropout
Softmax_linear

License

GNU General Public License v3.0