智能穿戴设备基于动态模板匹配算法的3D手势识别

李云鹤

doi:10.11959/j.issn.2096-3750.2019.00094

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智能穿戴设备基于动态模板匹配算法的3D手势识别

服务与应用 | 更新时间：2024-06-05

- 智能穿戴设备基于动态模板匹配算法的3D手势识别
- 3D gesture recognition based on dynamic template matching algorithm for intelligent wearable devices
- 物联网学报 2019年3卷第1期页码：97-105
- 作者机构：
- 作者简介：
  
  [ "李云鹤（1983- ），男，黑龙江齐齐哈尔人，博士，肇庆学院副教授，主要研究方向为物联网、网络编码、稀疏信号表征与处理。" ]
- 基金信息：
  
  广东省自然科学基金资助项目;The Natural Science Foundation of Guangdong Province(2018A030313346)
- DOI：10.11959/j.issn.2096-3750.2019.00094
  中图分类号： TN915
- 纸质出版日期：2019-03-30，
  
  网络出版日期：2019-03，
- 稿件说明：
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李云鹤. 智能穿戴设备基于动态模板匹配算法的3D手势识别[J]. 物联网学报, 2019,3(1):97-105.

YUNHE LI. 3D gesture recognition based on dynamic template matching algorithm for intelligent wearable devices. [J]. Chinese journal on internet of things, 2019, 3(1): 97-105.
李云鹤. 智能穿戴设备基于动态模板匹配算法的3D手势识别[J]. 物联网学报, 2019,3(1):97-105. DOI： 10.11959/j.issn.2096-3750.2019.00094.

YUNHE LI. 3D gesture recognition based on dynamic template matching algorithm for intelligent wearable devices. [J]. Chinese journal on internet of things, 2019, 3(1): 97-105. DOI： 10.11959/j.issn.2096-3750.2019.00094.

摘要

随着物联网设备的日益普及，智能穿戴设备行业发展迅速，其中以腕带类的智能手环、手表为主。智能穿戴设备具有丰富的传感器和一定的计算能力，通过手势识别作为自身以及面向其他物联网设备的人机交互，具有广泛的用户需求。提出基于动态模板匹配算法的3D手势识别系统，通过智能穿戴设备收集用户的特定手势来判断手势的含义，从而利用更自然的人机交互技术实现对智能设备的控制。使用智能设备的运动传感器读取相应的3D手势数据，结合优化的动态时间规整算法来识别手势；基于移动设备的特征和动态编程，通过斜率来界定曲线路径；同时，通过预存储失真阈值减少模板匹配的计算量和手势识别成本。在手机上进行测试，所提算法与传统算法相比，耗时更少，识别效率和精度更高，可以带来更好的人机交互体验。

Abstract

With the popularity of Internet of things equipment

the smart wearing equipment industry develops rapidly

in which wristband smart bracelets and watches are the mainstream.Intelligent wearable devices have abundant sensors and certain computing power.As human-computer interaction for itself and other devices of the Internet of things through gesture recognition

they have a wide range of needs.A 3D gesture recognition system based on dynamic template matching algorithm was proposed.The gesture meaning was judged by collecting user’s specific gesture from hand-held smart devices

and the control of smart devices was realized by using more natural human-computer interaction technology.The motion sensor of intelligent devices was used to read the corresponding 3D gesture data

and the optimized dynamic time warping algorithm was used to recognize gesture.Based on the characteristics of mobile devices and dynamic programming

the curve path was defined by slope.At the same time

the calculation of template matching and the cost of gesture recognition were reduced by pre-storage distortion threshold.The test is carried out on mobile phones

compared with the traditional algorithm

the proposed algorithm takes less time

has higher recognition efficiency and accuracy

and can bring better human-computer interaction experience.

关键词

智能穿戴手势识别人机交互动态模板匹配算法动态时间规整

Keywords

intelligent wearinggesture recognitionhuman-computer interactiondynamic template matching algorithmdynamic time warping

references

YAO Y, FU Y . Contour model-based hand-gesture recognition using the Kinect sensor[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014,24(11): 1935-1944.

LU Z, CHEN X, LI Q ,et al. A hand gesture recognition framework and wearable gesture-based interaction prototype for mobile devices[J]. IEEE Transactions on Human-Machine Systems, 2014,44(2): 293-299.

MARIN G, DOMINIO F, ZANUTTIGH P . Hand gesture recognition with leap motion and Kinect devices[C]// 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014: 1565-1569.

MOLCHANOV P, GUPTA S, KIM K ,et al. Hand gesture recognition with 3D convolutional neural networks[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE Computer Society, 2015: 1-7.

CARAMIAUX B, MONTECCHIO N, TANAKA A ,et al. Adaptive gesture recognition with variation estimation for interactive systems[J]. ACM Transactions on Interactive Intelligent Systems, 2014,4(4): 1-34.

ZHANG C, TIAN Y . Histogram of 3D facets:a depth descriptor for human action and hand gesture recognition[J]. Computer Vision and Image Understanding, 2015(139): 29-39.

TSAI J C, CHANG S M, YEN S H ,et al. A real-time hand gesture recognition system for daily information retrieval from the Internet[J]. International Journal of Computational Science and Engineering, 2015,11(2): 105.

CHENG H, YANG L, LIU Z . Survey on 3D hand gesture recognition[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016,26(9): 1659-1673.

SENIN. Dynamic time warping algorithm review[J]. Science, 2007: 1-23.

PLOUFFE G, CRETU A M . Static and dynamic hand gesture recognition in depth data using dynamic time warping[J]. IEEE Transactions on Instrumentation and Measurement, 2016,65(2): 305-316.

WANG H, LI Z . Accelerometer-based gesture recognition using dynamic time warping and sparse representation[J]. Multimedia Tools and Applications, 2016,75(14): 8637-8655.

LI Z, ZHANG F, ZHANG X ,et al. Flight data denoising method based on stationary wavelet transform[J]. Journal of Computer Applications, 2011,31(10): 2790-2810.

WANG J, LI Z, WANG D . A method for wavelet threshold denoising of seismic data based on CEEMD[J]. Geophysical Prospecting for Petroleum, 2017,53(2): 164-172.

WU X, ZHANG B, ZHANG Z ,et al. A new threshold model for gesture recognition based on HMM[J]. Pattern Recognition and Artificial Intelligence, 2003,16(1): 122-125.

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