Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2016

06.05.2016

Gesture Recognition Method Using Sensing Blocks

verfasst von: Yulong Xi, Seoungjae Cho, Simon Fong, Yong Woon Park, Kyungeun Cho

Erschienen in: Wireless Personal Communications | Ausgabe 4/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Recently, the recognition of posture and gesture has been widely used in fields such as medical treatment and human–computer interaction. Previous research into the recognition of posture and gesture has mainly used human skeletons and an RGB-D camera. The resulting recognition methods utilize models of the human skeleton, with different numbers of joints. The processing of the resulting large amounts of feature data needed to recognize a gesture leads to the recognition being delayed. To overcome this issue, we designed and developed a system for learning and recognizing postures and gestures. This paper proposes a gesture recognition method with enhanced generality and processing speed. The proposed method consists of feature collection part, feature optimization part, and a posture and gesture recognition part. We have verified the solution proposed in this paper through the learning and subsequent recognition of 29 postures and 8 gestures.
Vorheriger Artikel Design and Implementation of a Reliable Message Transmission System Based on MQTT Protocol in IoT
Nächster Artikel Energy Consumption and Efficiency Issues in Human Activity Monitoring System

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Omelina, L., Jansen, B., Bonnechere, B., Van Sint Jan, S., & Cornelis, J. (2012). Serious games for physical rehabilitation: designing highly configurable and adaptable games. In Proceedings of the 9th international conference on disability, virutal reality & associated technologies, Laval, France, pp. 195–201.
2.
Pirani, E., & Kolte, M. (2010). Gesture based educational software for children with acquired brain injuries. International Journal in Computer Science and Engineering, 2(3), 790–794.
3.
Alves, S., Marques, A., Queirós, C., & Orvalho, V. (2013). LIFEisGAME prototype: A serious game about emotions for children with autism spectrum disorders. PsychNology Journal, 11(3), 191–211.
4.
Zhang, K., Zhai, Y., Leong, H. W., & Wang, S. (2012). An interaction educational computer game framework using hand gesture recognition. In Proceedings of the 4th international conference on internet multimedia computing and service, pp. 219–222.
5.
He, G. F., Park, J. W., Kang, S. K., & Jung, S. T. (2012). Development of gesture recognition-based serious games. In 2012 IEEE-EMBS International Conference on Biomedical and health informatics (BHI), pp. 922–925.
6.
Kühnel, C., Westermann, T., Hemmert, F., Kratz, S., Müller, A., & Möller, S. (2011). I’m home: Defining and evaluating a gesture set for smart-home control. International Journal of Human–Computer Studies, 69(11), 693–704.CrossRef
7.
Taranta, E. M, I. I., Simons, T. K., Sukthankar, R., & Laviola, J. J, Jr. (2015). Exploring the benefits of context in 3D gesture recognition for game-based virtual environments. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(1), 1–34.CrossRef
8.
Zhou, Y., Jing, L., Wang, J., & Cheng, Z. (2012). Analysis and selection of features for gesture recognition based on a micro wearable device. Graduate School of Computer Science and Engineering, University of Aizu Wakamatsu, Japan,(IJACSA) International Journal of Advanced Computer Science and Applications 3(1), 1–7.
9.
Kang, S. K., Chung, K. Y., Rim, K. W., & Lee, J. H. (2011). Skin color based hand and finger detection for gesture recognition in CCTV surveillance. The Journal of the Korea Contents Association, 11(10), 1–10.CrossRef
10.
Juhyun, L., Hanbyul, C., & Kicheon, H. (2015). A fainting condition detection system using thermal imaging cameras based object tracking algorithm. Journal of Convergence., 6(3), 1–15.CrossRef
11.
Bostanci, E., Kanwal, N., & Clark, A. F. (2015). Augmented reality applications for cultural heritage using Kinect. Human-Centric Computing and Information Sciences, 5(1), 1–18.CrossRef
12.
Kessous, L., Castellano, G., & Caridakis, G. (2010). Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis. Journal on Multimodal User Interfaces, 3(1–2), 33–48.CrossRef
13.
Zhang, Z., Liu, Y., Li, A., & Wang, M. (2014, October). A novel method for user-defined human posture recognition using Kinect. In 2014 7th international congress on image and signal processing (CISP), pp. 736–740.
14.
Patsadu, O., Nukoolkit, C., & Watanapa, B. (2012, May). Human gesture recognition using Kinect camera. In 2012 international joint conference on computer science and software engineering (JCSSE), pp. 28–32.
15.
Le, T. L., Nguyen, M. Q., & Nguyen, T. T. M. (2013, January). Human posture recognition using human skeleton provided by Kinect. In 2013 international conference on computing, management and telecommunications (ComManTel), pp. 340–345.
16.
Wu, J., Cheng, J., & Feng, W. (2014, July). 3D dynamic gesture recognition based on improved HMMs with entropy. In 2014 IEEE international conference on information and automation (ICIA), pp. 213–218.
17.
Park, C. B., & Lee, S. W. (2011). Real-time 3D pointing gesture recognition for mobile robots with cascade HMM and particle filter. Image and Vision Computing, 29(1), 51–63.CrossRef
18.
Fujii, T., Lee, J. H., & Okamoto, S. (2014). Gesture recognition system for human–robot interaction and its application to robotic service task. In Lecture notes in engineering and computer science: proceedings of the international multiconference of engineers and computer scientists, pp. 1–6.
19.
Yang, M., Lin, Z., Tang, W., Zheng, L., & Zhou, J. (2014). Human action recognition based on Kinect. Journal of Computational Information Systems, 10(12), 5347–5354.
20.
Huang, X., Zheng, L., Liang, R., & Wang, W. (2012). Human action recognition based on SVM using multiple features. International Conference on Artificial Intelligence and Soft Computing, 12, 160–165.
21.
Li, P. C., & Xu, S. H. (2005). Support vector machine and kernel function characteristic analysis in pattern recognition. Computer Engineering and Design, 26(2), 302–304.
22.
Baruah, U., & Hazarika, S. M. (2015). A dataset of online handwritten assamese characters. Journal of Information Processing Systems, 11(3), 325–341.
Metadaten
Titel
Gesture Recognition Method Using Sensing Blocks
verfasst von
Yulong Xi
Seoungjae Cho
Simon Fong
Yong Woon Park
Kyungeun Cho
Publikationsdatum
06.05.2016
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2016
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-016-3356-z

Weitere Artikel der Ausgabe 4/2016

Wireless Personal Communications 4/2016 Zur Ausgabe

OriginalPaper

Efficient Resource Management Scheme for Storage Processing in Cloud Infrastructure with Internet of Things

OriginalPaper

Combining Layer Coding and Bandwidth Allocation for Video Distribution on Content Delivery Networks

OriginalPaper

Proxy Based Seamless Connection Management Scheme for Smart Transportation Service

OriginalPaper

Intelligent Security Model of Smart Phone Based on Human Behavior in Mobile Cloud Computing

OriginalPaper

Design and Evaluation of a Services Interface for the Internet of Things

Editorial

User Experience and Quality-of-Service in Internet of Things: Practice and Trends

Neuer Inhalt

    Bildnachweise