ICALEPCS2019 - List of Authors (Bacher, R.)

Paper	Title	Page
MOPHA011	Improving Gesture Recognition with Machine Learning: A Comparison of Traditional Machine Learning and Deep Learning	214
	R. Bacher DESY, Hamburg, Germany
	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.
	Poster MOPHA011 [0.927 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA011
About •	paper received ※ 27 August 2019 paper accepted ※ 08 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Improving Gesture Recognition with Machine Learning: A Comparison of Traditional Machine Learning and Deep Learning

214

R. Bacher
DESY, Hamburg, Germany

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.

Poster MOPHA011 [0.927 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA011

About •

paper received ※ 27 August 2019 paper accepted ※ 08 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)