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@InProceedings{bacher:icalepcs2019-mopha011,
author = {R. Bacher},
title = {{Improving Gesture Recognition with Machine Learning: A Comparison of Traditional Machine Learning and Deep Learning}},
booktitle = {Proc. ICALEPCS'19},
pages = {214--218},
paper = {MOPHA011},
language = {english},
keywords = {network, GUI, real-time, interface, controls},
venue = {New York, NY, USA},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {17},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2020},
issn = {2226-0358},
isbn = {978-3-95450-209-7},
doi = {10.18429/JACoW-ICALEPCS2019-MOPHA011},
url = {https://jacow.org/icalepcs2019/papers/mopha011.pdf},
note = {https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA011},
abstract = {Meaningful gesturing is important for an intuitive human-machine communication. This paper deals with methods suitable for identifying different finger, hand and head movements using supervised machine learning algorithms. On the one hand it discusses an implementation based on the k-nearest neighbor classification algorithm (traditional machine learning approach). On the other hand it demonstrates the classification potential of a convolutional neural network (deep learning approach). Both methods are capable of distinguishing between fast and slow, short and long, up and down, or right and left linear as well as clockwise and counterclockwise circular movements. The details of the different methods with respect to recognition accuracy and performance will be presented.},
}