sEMG attention

Repository for the paper sEMG Gesture Recognition with a Simple Model of Attention

For cleaner code see: paper repro

Reproduction

We provide three methods to reproduce the environment used in this paper:

Nix

To reproduce down to CUDA version and compiler level, please use the reproducible package manager nix. The environment is available as a distributable binary using cachix. To pull the binaries to your system, install cachix, and run

cachix use tf-envs

Then, regardless of cachix usage (if you are not using cachix, you will have to wait for tensorflow to compile to the specifications used in this project), run

nix-shell

And the environment will be reproduced!

Pip

To install the requirements for this project, please run

pip install -r requirements.txt

Docker

Carson will make this!

Get the data

From the home directory of this project:

cd data
# always inspect scripts before downloading!
cat load_nina.sh
./load_nina.sh

Alternatively, the data is available for download (after the creation of a login) here and here.

Using the code

For the sake of thoroughness, we will demonstrate how to load the data and generate the training, validation, and test datasets used in this project:

import numpy as np
from data import dataset, ma_batch
from generator import generator

# load the data, takes about 12 GB in memory
data = dataset("data/ninaPro")

reps = np.unique(data.repetition)
# split by exercise repetition
val_reps = reps[3::2]
train_reps = reps[np.where(np.isin(reps, val_reps, invert=True))]
test_reps = val_reps[-1].copy()
val_reps = val_reps[:-1]

# create generators, these load the data, in the case of train, augment the data
# and lazily apply the moving axis. To include IMU data, add `imu = True`, and 
# to not do the moving average transformation described in the paper, add `ma = False`
train = generator(data, list(train_reps))
validation = generator(data, list(val_reps), augment=False)
test = generator(data, [test_reps][0], augment=False)

# if you want the full data as a numpy array, it is stored in the X and y attributes
test_x = np.moveaxis(ma_batch(test.X, test.ma_len), -1, 0)
test_y = test.y

# get all the data:
from typing import Iterable
def get_arrays(g: generator) -> Iterable[np.ndarray]:
	return np.moveaxis(ma_batch(g.X, g.ma_len), -1, 0), g.y

(train_x, train_y), (val_x, val_y) = (get_arrays(g) for g in [train, validation])

To reproduce the models described in the paper:

Run any of the files (cleanup in process)