Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24–25, 2019, Proceedings

Research Article

A Smart Wearable Device for Preventing Indoor Electric Shock Hazards

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  • @INPROCEEDINGS{10.1007/978-3-030-32388-2_25,
        author={Zaipeng Xie and Hanxiang Liu and Junpeng Zhang and Xiaorui Zhu and Hongyu Lin},
        title={A Smart Wearable Device for Preventing Indoor Electric Shock Hazards},
        proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings},
        proceedings_a={MLICOM},
        year={2019},
        month={10},
        keywords={Wearable device Smart device IoT Electric shock hazard Risk analysis},
        doi={10.1007/978-3-030-32388-2_25}
    }
    
  • Zaipeng Xie
    Hanxiang Liu
    Junpeng Zhang
    Xiaorui Zhu
    Hongyu Lin
    Year: 2019
    A Smart Wearable Device for Preventing Indoor Electric Shock Hazards
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-32388-2_25
Zaipeng Xie1,*, Hanxiang Liu1, Junpeng Zhang1, Xiaorui Zhu1, Hongyu Lin2
  • 1: Hohai University
  • 2: Southeast University
*Contact email: zaipengxie@hhu.edu.cn

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.