Research on Barrier-free Home Environment System Based on Speech Recognition_Journal of Biomedical Engineering

Authors：

ZHUHusheng ,  YUHongliu , SHIPing , FANGYoufang , JIANZhuo

Institute of Rehabilitation Engineering and Technology, School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;

Corresponding author：

YUHongliu, Email: yhl98@hotmail.com

Keywords：

physical disability; speech recognition; barrier-free environment; embedded computer

DOI：

10.7507/1001-5515.20150181

Video：

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Abstract Full text Figures/Tables Video References Cited by

The number of people with physical disabilities is increasing year by year, and the trend of population aging is more and more serious. In order to improve the quality of the life, a control system of accessible home environment for the patients with serious disabilities was developed to control the home electrical devices with the voice of the patients. The control system includes a central control platform, a speech recognition module, a terminal operation module, etc. The system combines the speech recognition control technology and wireless information transmission technology with the embedded mobile computing technology, and interconnects the lamp, electronic locks, alarms, TV and other electrical devices in the home environment as a whole system through a wireless network node. The experimental results showed that speech recognition success rate was more than 84% in the home environment.

Citation： ZHUHusheng, YUHongliu, SHIPing, FANGYoufang, JIANZhuo. Research on Barrier-free Home Environment System Based on Speech Recognition. Journal of Biomedical Engineering, 2015, 32(5): 1019-1025. doi: 10.7507/1001-5515.20150181 Copy

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1. 陈志翔,信琴琴,朱月秀,等.虚拟人舌运动可视化在发声中的研究[J].中国康复理论与实践,2013,19(10):993-997.
2. JONES R D, HOOPER R H, ARMSTRONG D I, et al. Microprocessor-based multi-patient environmental-control system for a spinal injuries unit[J]. Med Biol Eng Comput, 1980, 18:607-616.
3. SURDILOVIC T, ZHANG Y Q. Convenient intelligent cursor control web systems for Internet users with severe motor-impairments[J]. Int J Med Inform, 2006, 75(1):86-100.
4. JOJIC N, BRUMITT B, MEYERS B, et al. Detection and estimation of pointing gestures in dense disparity maps[C]//Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble:2000:468-475.
5. SATO S, SAKANE S. A human-robot interface using an interactive hand pointer that projects a mark in the real work space[C]//Proceedings ICRA'00.IEEE International Conference on Robotics and Automation. San Francisco:2000:589-595.
6. STERGIOPOULOU E, PAPAMARKOS N. Hand gesture recognition using a neural network shape fitting technique[J]. Eng Appl Artif Intell, 2009, 22(8):1141-1158.
7. HWAN H, WON O L, JI W L, et al. Object recognition and selection method by gaze tracking and SURF algorithm[C]//International Conference on Multimedia and Signal Processing. Guiliin:2011:261-265.
8. 任宇鹏,王广志,程明,等.基于脑-机接口的康复辅助机械手控制[J].中国康复医学杂志,2004,19(5):330-333.
9. 朱磊,唐庆玉,崔子经.基于语音识别技术的残疾人环境控制系统的研制[J].北京生物医学工程,2000,19(2):65-69.
10. 卢艳,黄云龙.一种语音识别声控车模的设计[J].微计算机信息,2010,26(25):200-202.
11. 李瑞峰,李麟.基于RSC4128的家用机器人语音人机交互系统的设计[J].制造业自动化,2007,29(10):30-33.
12. 陈立万.基于语音识别系统中DTW算法改进技术研究[J].微计算机信息,2006,22(5):267-269.
13. GRANT P M. Speech recognition techniques[J]. Electron Commun Eng J, 1991, 3(1):37-48.
14. 卢博睿.重残患者无障碍居家环境控制系统研究[D].上海:上海理工大学,2013.
15. CHEN X, HUANG J, WANG Y, et al. Incremental feedback learning methods for voice recognition based on DTW[C]//Proceedings of International Conference on Modeling, Identification & Control (ICMIC). Wuhan:2012:1011-1016.
16. 张金艳,张建江.安静居住小区环境噪声监测技术的探讨[J].噪声与振动控制,2007,27(4):127-128.
17. 张崇,于晓琳,刘建平.单片2.4GHz无线收发一体芯片nRF2401及其应用[J].国外电子元器件,2004,7(6):34-36.

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