Abstract
The emerging wearable IoT technology is evolving dramatically in recent years and resulted in a wide adoption in various applications. Electric shock hazard is one of the major indoor hazards for consumers who may fail to recognize potential electrical risks. This paper proposes a smart wearable IoT device with risk assessment algorithms for preventing indoor electrical shock hazards. This device consists of two hardware components: a receiver and a detector embedded in a power switch. The detector consists of a Wi-Fi module, a current sensor, a NFC module, and an Arduino mini module that communicates with a software routine monitoring the status of the power switch and its connected appliances. The receiver is a passive NFC tag that can be designed as an accessory or clothing that customers may wear. A risk assessment algorithm is proposed using a set of predefined inference rules. The software routine is developed to provide early warnings to customers where potential electrical shock risk level is high. This paper describes the implementation details as well as the algorithms. Experimental results are summarized and they demonstrate that the proposed smart wearable device can be effective in predicting electric shock hazards in an indoor environment.
Supported by the Innovative and Entrepreneurial Talent Program of Jiangsu Province (Grant No. 2016B17078) and partially supported by the National Key R&D Program of China (Grant No. 2016YFC0402710).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Xie, Z., Liu, H., Zhang, J., Zhu, X., Lin, H. (2019). A Smart Wearable Device for Preventing Indoor Electric Shock Hazards. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_25
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DOI: https://doi.org/10.1007/978-3-030-32388-2_25
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