Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 81, Issue 15, Pages 20509-20539Publisher
SPRINGER
DOI: 10.1007/s11042-022-12355-8
Keywords
Gesture recognition; Skeleton data; HMM; HCI
Categories
Funding
- National Natural Science Foundation of China [71901061, 71871056]
- Science and Technology on Avionics Integration Laboratory and Aeronautical Science Fund [20185569008]
- Fundamental Research Funds for the Central Universities [2242019k1G016]
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This paper presents a novel non-trajectory-based gesture recognition method (NT-GRM) based on hand skeleton information and a hidden Markov model (HMM). It can accurately and quickly recognize static and dynamic gestures with high recognition accuracy and speed.
Currently, no efficient, accurate and flexible gesture recognition algorithm has been developed to recognize non-trajectory-based gesture recognition. Therefore, we aim to construct a gesture recognition algorithm to not only complete gesture recognition accurately and quickly but also adapt to individual differences. In this paper, we present a novel non-trajectory-based gesture recognition method (NT-GRM) based on hand skeleton information and a hidden Markov model (HMM). To recognize a static gesture, the direction information of each bone section of the hands was taken as the observation data to construct the HMM. In addition, multiple static gestures were detected in turn to identify a dynamic gesture. As determined by experimental verification, the NT-GRM can complete recognition in a system containing ten interactive gestures with a recognition accuracy of over 95% and a recognition speed of 21.73 ms. The training time required for each static gesture model is 2.56 s. And the NT-GRM can identify static and dynamic gestures accurately and quickly with small training samples in different functional modes. In conclusion, the NT-GRM can be applied to the development of gesture interaction systems to help developers realize practical functions such as gesture library construction, user gesture customization, and user gesture adaptation.
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