$ cd ~/projects

  sEMG Gesture Recognition Using LSTM Networks
  Person-Independent — For Human-Computer Interaction
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  ## abstract

  Hand gestures feel natural to perform, which makes them well-suited to use as Human-Computer Interaction interfaces. But detecting them with high accuracy in real-time is a challenging task. This paper presents an approach based on the Long Short-Term Memory Neural Network architecture to evaluate Surface Electromyography signals and determine the gesture performed.
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  ## conclusion

  Surface Electromyography (sEMG) is a convenient way to gather EMG data for HCI interfaces. Hyperparameter tuning is used to refine the model. Transfer Learning can be used to translate a pre-trained model to a new person with very little additional data and training time. The resulting NN was able to detect gestures with high accuracy.
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$ ls ./downloads

  paper.pdf    /files/projects/semg-gesture-lstm/paper.pdf
  slides.pdf   /files/projects/semg-gesture-lstm/slides.pdf
  uni wiki     https://wiki.tum.de/display/infar/%5B19WS+-+GR%5D+Person-Independent+sEMG+Gesture+Recognition+Using+LSTM+Networks+for+Human-Computer+Interaction
  dataset      https://github.com/michidk/myo-dataset
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$ ls ~/pages

  > about        curl www.lohr.dev
  > projects     curl www.lohr.dev/projects
  > blog         curl www.lohr.dev/blog
  > contact      curl www.lohr.dev/contact
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                                    https://lohr.dev