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A Real-Time Approach for Gesture Recognition using the Kinect Sensor

Authors:

Georgios Paraskevopoulos,

Evaggelos Spyrou,

Dimitrios SgouropoulosAuthors Info & Claims

SETN '16: Proceedings of the 9th Hellenic Conference on Artificial Intelligence

Article No.: 31, Pages 1 - 4

https://doi.org/10.1145/2903220.2903241

Published: 18 May 2016 Publication History

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Abstract

In this paper we present an approach for real-time gesture recognition using the Kinect sensor and a set of machine learning techniques. We propose a novel approach for feature extraction using measurements on skeletal joints. We select a set of simple gestures and construct a data set. We train classifiers under the assumptions that they shall be evaluated to both known and unknown users. Experimental results prove the effectiveness and the potential of our approach.

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Torres ASilubrico CTorralba DTomas J(2019)Detection of Proper Form on Upper Limb Strength Training Using Extremely Randomized Trees for Joint PositionsProceedings of the 2nd International Conference on Computing and Big Data10.1145/3366650.3366680(111-115)Online publication date: 18-Oct-2019
https://dl.acm.org/doi/10.1145/3366650.3366680
Mathe EMitsou ASpyrou EMylonas P(2018)Arm Gesture Recognition using a Convolutional Neural Network2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)10.1109/SMAP.2018.8501886(37-42)Online publication date: Sep-2018
https://doi.org/10.1109/SMAP.2018.8501886

A Real-Time Approach for Gesture Recognition using the Kinect Sensor
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SETN '16: Proceedings of the 9th Hellenic Conference on Artificial Intelligence

May 2016

249 pages

ISBN:9781450337342

DOI:10.1145/2903220

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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EETN: Hellenic Artificial Intelligence Society

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Association for Computing Machinery

New York, NY, United States

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Published: 18 May 2016

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SETN '16: 9th Hellenic Conference on Artificial Intelligence

May 18 - 20, 2016

Thessaloniki, Greece

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Torres ASilubrico CTorralba DTomas J(2019)Detection of Proper Form on Upper Limb Strength Training Using Extremely Randomized Trees for Joint PositionsProceedings of the 2nd International Conference on Computing and Big Data10.1145/3366650.3366680(111-115)Online publication date: 18-Oct-2019
https://dl.acm.org/doi/10.1145/3366650.3366680
Mathe EMitsou ASpyrou EMylonas P(2018)Arm Gesture Recognition using a Convolutional Neural Network2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)10.1109/SMAP.2018.8501886(37-42)Online publication date: Sep-2018
https://doi.org/10.1109/SMAP.2018.8501886

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Multi-scenario gesture recognition using Kinect

Robust hand gesture recognition with kinect sensor

Human-Machine Interaction based on Hand Gesture Recognition using Skeleton Information of Kinect Sensor

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