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 take about 20 min on GTX 960 or 2h on i3-6100.

Accuracy after ~75k iteration (98.75%):

Loose after ~75k iteration (1.28):

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 training dataset

python3 predict_train_dataset.py

Example output:

Accuracy on Test-Set: 98.27% (19235 / 19573)
[2438    5    9    6    4   20] (0) Relax
[   4 2652   45    1    3    9] (1) Ok
[   8   44 4989    1    1    9] (2) Fist
[   8    2    2 4152   28   13] (3) Like
[   2    5    6   27 1839    1] (4) Rock
[  14   22   13   21    5 3165] (5) Spock
 (0) (1) (2) (3) (4) (5)

I know that making prediction on training dataset wrong. But i don't have time to make testing dataset(

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

hassanyousufx/myo-armband-nn