Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

Authors

  • Yu-Liang Hsu
    Department of Automatic Control Engineering, Feng Chia University (FCU), No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan. hsuyl@fcu.edu.tw.
  • Po-Huan Chou
    Department of Mechanical and Mechatronics Systems Research Labs., Industrial Technology Research Institute (ITRI), 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan. phchou@itri.org.tw.
  • Hsing-Cheng Chang
    Department of Automatic Control Engineering, Feng Chia University (FCU), No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan. hcchang@fcu.edu.tw.
  • Shyan-Lung Lin
    Department of Automatic Control Engineering, Feng Chia University (FCU), No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan. sllin@fcu.edu.tw.
  • Shih-Chin Yang
    Department of Mechanical Engineering, National Taiwan University (NTU), No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan. scy99@ntu.edu.tw.
  • Heng-Yi Su
    Department of Electrical Engineering, Feng Chia University (FCU), No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan. hengyisu@fcu.edu.tw.
  • Chih-Chien Chang
    Department of Automatic Control Engineering, Feng Chia University (FCU), No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan. d0243331@fcu.edu.tw.
  • Yuan-Sheng Cheng
    Department of Automatic Control Engineering, Feng Chia University (FCU), No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan. d0243225@fcu.edu.tw.
  • Yu-Chen Kuo
    Department of Automatic Control Engineering, Feng Chia University (FCU), No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan. m0429322@fcu.edu.tw.