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EMG-based Gesture Recognition using Capsule Network

Data

The NinaPro dataset will be downloaded automatically by running the main.py script if it is not found.

Requirements

  1. python 3.7.4
  2. numpy 1.18.1
  3. scikit-image 0.16.2
  4. torch 1.4.0
  5. tqdm 4.44.1
  6. matplotlib 3.1.3

TODO

  • Build dataset downloader and loader
  • Build feature extractors
  • Build general classification engine
  • Build general training/evaluation engine
  • Build conventional classifiers (Support Vector Machine, Random Forest)
  • Build Neural Network based classifiers (Fully Connected Network, Convolutional Neural Network)
  • Build Capsule Network based classifier
  • Create logging and plotting engines
  • Train and Evaluate

Train

Multiple data types, feature types and models could be chosen. For instance, the following command line runs a training of a capsule net using the base dataset and the root mean square features.

python train.py --model capsnet --features rms --data intrasubjects --epochs 50 --chkpt_period 1 --valid_period 1 --batch_size 5 --lr 0.001 --verbose 1

Run python train.py -h to see the possible values of the arguments.

Results

Testing on Intrasubject Fixed Window Dataset

Features rf SVM FCN CNN EMGCaps
rms 66.56% 52.06% 67.67% 55.54% 76.04%
hist 56.34% 38.58% 49.28% 53.88% 65.67%
multirms 65.29% 50.93% 57.26% 61.47% 77.85%
pmrms 66.44% 63.15% 63.29% 61.72% 76.41%
kmrms 54.4% 25.57% 38.73% 39.65% 67.47%
fourier 62.32% 48.34% 57.14% 56.77% 69.18%

Testing on Intrasubject Variable Window Dataset

Features rf SVM FCN CNN EMGCaps
rms 79.53% 53.27% 74.67% 66.26% 90.56%
multirms 50.93% 66.82% 48.69% 81.21% 93.27%
pmrms 81.96% 82.42% 48.69% 81.21% 92.52%
kmrms 64.67% 23.92% 15.88% 39.65% 85.04%

Testing on Intersubject Fixed Window Dataset

Features rf SVM FCN CNN EMGCaps
rms 15% 14.8% 17.42% 16.2% 15.56%
hist 14.82% 14.95% 14.67% 14.88% 15.21%
multirms 15.97% 15.03% 15.51% 15.49% 17.57%
pmrms 9.8% 13.4% 11.93% 10.97% 11.78%
kmrms 11.85% 11.07% 14.09% 13.4% 15.00%
fourier 16.02% 14.95% 16.98% 17.42% 20.54%

Testing on Intersubject Variable Window Dataset

Features rf SVM FCN CNN EMGCaps
rms 15.2% 15.67% 19.27% 19.59% 20.84%
multirms 22.88% 20.53% 27.27% 27.11% 28.99%
pmrms 18.18% 23.19% 20.68% 20.06% 23.51%
kmrms 14.89% 10.81% 12.06% 18.02% 19.74%