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Journal on Multimodal User Interfaces

, Volume 11, Issue 1, pp 1–7 | Cite as

Gesture recognition based on HMM-FNN model using a Kinect

  • Xiao-Li Guo
  • Ting-Ting Yang
Original Paper

Abstract

Addressing the problem of complex dynamic gesture recognition, this paper obtains the body depth image through the body feeling sensor device—Kinect; the threshold segmentation method is used to segment the gestures depth image, on the basis of the common distance between hand and body. Then, the HMM-FNN model, which combines the hidden markov model (HMM) and the fuzzy neural network (FNN), is used for dynamic gesture recognition. This paper mainly focuses on the trainees’ common operations of equipment in virtual substation to set the custom gesture interaction sets. Based on the characteristic of the complex dynamic gesture, gesture image was decomposed into three feature sequences—hand shape change, hand position changes in the two-dimensional plane, and movement in the Z-axis direction, for feature extraction. The HMM model is respectively built according to the three sub sequences, and the FNN was connected to judge the semantics of gesture using the fuzzy reasoning. By experimental verification, the HMM-FNN model can quickly and effectively identify complicated dynamic hand gestures. Meanwhile, it has strong robustness. The recognition effect is superior to that of the simple HMM model.

Keywords

Kinect Threshold segmentation method Complex dynamic gesture HMM-FNN  Gesture recognition 

Notes

Compliance with ethical standards

Funding

This study was funded by the key transformation project of provincial science and technology plan (No. 20140307008GX) and the “Double ten” cultivation project of Jilin provincial education department ( Open image in new window [2014] No. 109).

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

In this research, ten people were chose to participate in the gesture recognition experiment. They are Tingting Yang, Yanli Wen, Liqing Sun, Chunlei Shi, Xudong Ma, Jun Qi, Qing Li, Yang Yu, Jiajia Zhang, Ning Zhou.

Informed consent

All participants voluntarily agreed to participate in this study and all gave written informed consent.

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Copyright information

© OpenInterface Association 2016

Authors and Affiliations

  1. 1.Information Engineering CollegeNortheast Dianli UniversityJilinChina

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