计算机科学 ›› 2015, Vol. 42 ›› Issue (10): 20-24.

• 第四届全国可穿戴计算学术会议 • 上一篇    下一篇

面向可穿戴设备的超声波手势识别方法

杨晓东,陈益强,于汉超,刘军发,李展歌   

  1. 中国科学院计算技术研究所 北京100190;移动计算与新型终端北京市重点实验室 北京100190;中国科学院大学 北京100049,中国科学院计算技术研究所 北京100190;移动计算与新型终端北京市重点实验室 北京100190,中国科学院计算技术研究所 北京100190;移动计算与新型终端北京市重点实验室 北京100190;中国科学院大学 北京100049,中国科学院计算技术研究所 北京100190;移动计算与新型终端北京市重点实验室 北京100190,中国科学院计算技术研究所 北京100190;移动计算与新型终端北京市重点实验室 北京100190;天津大学软件学院 天津300072
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家国际科技合作专项项目(2014DFG12750),国家自然科学基金(61070110,6)资助

Ultrasonic Waves Based Gesture Recognition Method for Wearable Equipment

YANG Xiao-dong, CHEN Yi-qiang, YU Han-chao, LIU Jun-fa and LI Zhan-ge   

  • Online:2018-11-14 Published:2018-11-14

摘要: 针对传统的基于触摸屏和计算机视觉的手势交互方法无法应对智能可穿戴设备逐渐趋向小型化和低功耗等问题,依据声波的Doppler效应和运动物体能够改变声波频率的特性,提出了一种基于超声波的低功耗鲁棒手势识别方法。该方法利用Goertzel算法进行超声波频率特征提取和分析,得到手势的移动方向并形成时间序列,进而利用隐马尔科夫模型实现用户手势识别。在微软移动终端Surface上的实验表明,此方法在安静和嘈杂的环境下都能达到较高的手势识别准确率、更高的鲁棒性、更低的计算成本和功耗,能够满足可穿戴设备发展对于手势识别的需求。

关键词: 可穿戴设备,手势识别,超声波,Goertzel算法,隐马尔可夫模型,Doppler效应

Abstract: Wearable equipment has several limitions such as the smaller shape,limited power and CPU performance,which the traditional methods of human-computer interaction based on the touch screen and the computer vision cannot deal with.Based on the Doppler effect of sound waves,we proposed a low-power robust method.The method depends on Goertzel algorithm to extract the features of soundwave’s frequency-shifted, so that the moving direction of the user’s hand can be got ,and furthermore depends on the HMMs to classify the hand gestures.Using the proposed method,we conducted a series of comparative experiments on Surface Pro,one of the Microsoft mobile terminals.The experiment results show that no matter in the quiet environment or in the noisy one,the proposed method both has quite high precision rate,lower computational complexity which can lead to lower power consuming and better rubustness.So the proposed method can meet the needs of wearable equipment development for gesture recognition.

Key words: Wearable equipment,Gesture recognition,Ultrasonic waves,Goertzel algorithm,Hidden Markov model,Doppler effect

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