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Sensors 2017, 17(7), 1631; doi:10.3390/s17071631

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

1
Department of Automatic Control Engineering, Feng Chia University (FCU), No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan
2
Department of Mechanical and Mechatronics Systems Research Labs., Industrial Technology Research Institute (ITRI), 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan
3
Department of Mechanical Engineering, National Taiwan University (NTU), No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan
4
Department of Electrical Engineering, Feng Chia University (FCU), No. 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan
*
Author to whom correspondence should be addressed.
Received: 16 June 2017 / Revised: 10 July 2017 / Accepted: 11 July 2017 / Published: 15 July 2017
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2017)

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. View Full-Text
Keywords: wearable intelligent technology; artificial intelligence; sensing data fusion; gesture recognition; indoor positioning; smart energy management; home safety; smart home automation wearable intelligent technology; artificial intelligence; sensing data fusion; gesture recognition; indoor positioning; smart energy management; home safety; smart home automation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hsu, Y.-L.; Chou, P.-H.; Chang, H.-C.; Lin, S.-L.; Yang, S.-C.; Su, H.-Y.; Chang, C.-C.; Cheng, Y.-S.; Kuo, Y.-C. Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology. Sensors 2017, 17, 1631.

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