Wireless Personal Communications

, Volume 91, Issue 4, pp 1779–1797 | Cite as

Gesture Recognition Method Using Sensing Blocks

  • Yulong Xi
  • Seoungjae Cho
  • Simon Fong
  • Yong Woon Park
  • Kyungeun ChoEmail author


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.


Posture recognition Gesture recognition Natural user interface Hidden Markov model Support vector machine 



This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8501-16-1014) supervised by the IITP (Institute for Information & communications Technology Promotion) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2015R1A2A2A01003779).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Dongguk UniversitySeoulRepublic of Korea
  2. 2.University of MacauMacauChina
  3. 3.Agency for Defense DevelopmentDaejeonRepublic of Korea

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