Real-Time Decoding of Simultaneous Finger Movements by Multi-Label Representation Learning
Examensarbete för masterexamen
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|Type: ||Examensarbete för masterexamen|
|Title: ||Real-Time Decoding of Simultaneous Finger Movements by Multi-Label Representation Learning|
|Authors: ||Molin, Julia|
|Abstract: ||Classification of simultaneous finger movements is a yet unsolved problem in upper limb prosthetic
control. The decoding of simultaneous movements is a crucial step towards restoration of function
after an amputation, as it would allow for natural and intuitive finger control to be performed by a
prosthetic hand in everyday tasks.
Various pattern recognition algorithms have been evaluated in research, where most presented
methods need extracted features from myoelectric signals. In recent years, deep learning
approaches such as Convolutional Neural Networks (CNN) have entered this research field. An
interesting property of these networks is that they are not only able to extract features automatically,
but also learn how to extract them, known as representation learning. Even though representation
learning has revolutionized many research areas, it is yet unknown whether these algorithms could
outperform traditional machine learning methods or shallow Feed Forward Neural Networks (FFNN)
commonly used in myoelectric signal classification.
This project compares two novel CNN architectures: Temporal Convolutional Networks (TCN) and a
CNN with Squeeze and Excitation operations (CNN-SE) against an FFNN, decoding simultaneous
finger movements. The best performance obtained during real-time tests was through the CNN-SE
with an average Motion Test completion rate of 90.5% compared to corresponding 68.5% for the
FFNN. Both CNN-SE and TCN further showed higher averages in F1-score, as well as exact match
ratio (EMR), compared to the FFNN, indicating representation learning might be beneficial in
decoding simultaneous movements.|
|Keywords: ||Representation learning;multi-label;CNN;ANN;deep learning;simultaneous control;real-time control;myoelectric;EMG;prosthetics|
|Issue Date: ||2022|
|Publisher: ||Chalmers tekniska högskola / Institutionen för data och informationsteknik|
|Collection:||Examensarbeten för masterexamen // Master Theses|
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