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Sensors (Basel). 2018 Dec 24;19(1). pii: E59. doi: 10.3390/s19010059.

Hand Gesture Recognition in Automotive Human⁻Machine Interaction Using Depth Cameras.

Author information

1
Hochschule Ruhr West, University of Applied Sciences, 46236 Bottrop, Germany. nico.zengeler@hs-ruhrwest.de.
2
South Westphalia University of Applied Sciences, 59872 Meschede, Germany. kopinski.thomas@fh-swf.de.
3
Hochschule Ruhr West, University of Applied Sciences, 46236 Bottrop, Germany. uwe.handmann@hs-ruhrwest.de.

Abstract

In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.

KEYWORDS:

automotive human–machine interaction; hand gesture recognition; neural networks; time-of-flight sensors

PMID:
30586882
PMCID:
PMC6339101
DOI:
10.3390/s19010059
[Indexed for MEDLINE]
Free PMC Article

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