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.
Highlights
According to the World Health Organization (WHO) Global Health and Aging report, approximately 524 million people, representing 8% of the world’s population, were aged 65 or older in 2010
The effectiveness of the proposed 3D gesture recognition algorithm, indoor positioning algorithm, and intelligent fire detection and alarm algorithm is validated via the experimental results of household appliances remote control, indoor positioning and smart energy
3D gesture recognition algorithm, indoor management, home safety and detection in the indoor environment of the experimental testbed, respectively. All human materials such as human gesture motion and walking signals positioning algorithm, and intelligent fire detection and alarm algorithm is used validated via the in this study were approved by Institutional Review Board (IRB) of the National Cheng Kung experimental results of household appliances remote control, indoor positioning and smart energy
Summary
According to the World Health Organization (WHO) Global Health and Aging report, approximately 524 million people, representing 8% of the world’s population, were aged 65 or older in 2010. A number of researchers have developed diverse technologies for smart homes, such as internet of things (IoT), intelligent control, home automation, energy management, and wearable devices [3,8,9,10,11,12,13,14,15]. In this paper we propose a smart home system incorporating wearable intelligent technology, artificial intelligence, and multisensor data fusion technology, which can control household appliances remotely using an inertial-sensing-based gesture recognition algorithm, locate residents’ position in the indoor environment using an inertial- sensing-based indoor positioning algorithm, and determine the environmental conditions of the living spaces using an intelligent fire detection and alarm algorithm, for implementing features such as home automation control, smart energy management, and home safety. The recognition rates for recognizing eight 3-dimensional (3D) gestures reached 98.1% and
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