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2021 | OriginalPaper | Chapter

Introducing and Benchmarking a One-Shot Learning Gesture Recognition Dataset

Authors : Panagiotis Kasnesis, Christos Chatzigeorgiou, Charalampos Z. Patrikakis, Maria Rangoussi

Published in: Big Data Technologies and Applications

Publisher: Springer International Publishing

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Abstract

Deep learning techniques have been widely and successfully applied, over the last five years, to recognize the gestures and activities performed by users wearing electronic devices. However, the collected datasets are built in an old fashioned way, mostly comprised of subjects that perform many times few different gestures/activities. This paper addresses the lack of a wearable gesture recognition dataset for exploring one-shot learning techniques. The current dataset consists of 46 gestures performed by 35 subjects, wearing a smartwatch equipped with 3 motion sensors and is publicly available. Moreover, 3 one-shot learning classification approaches are benchmarked on the dataset, exploiting two different deep learning classifiers. The results of the benchmark depict the difficulty of the one-shot learning task, exposing new challenges for wearable gesture/activity recognition.

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Metadata
Title
Introducing and Benchmarking a One-Shot Learning Gesture Recognition Dataset
Authors
Panagiotis Kasnesis
Christos Chatzigeorgiou
Charalampos Z. Patrikakis
Maria Rangoussi
Copyright Year
2021
Book
Big Data Technologies and Applications

Print ISBN: 978-3-030-72801-4

Electronic ISBN: 978-3-030-72802-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-72802-1_8

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