IEEE Consumer Electronics Magazine - January/February 2020 - 21

Table 3. Precision rate.

6. P. Jung, G. Lim, S. Kim, and K. Kong, "A wearable
gesture recognition device for detecting muscular
activities based on air-pressure sensors," IEEE
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Apr. 2015.
7. M. I. Chacon-Murguia and S. Gonzalez-Duarte, "An
adaptive neural-fuzzy approach for object detection in
dynamic backgrounds for surveillance systems," IEEE

sented in Table 3. The precision rate is the percentage of true positives in true and false positives
detected by the proposed system. As shown in
Table 3, the average precision rate for the subject of
adult and child is 90.23% and 90.33%, respectively.

CONCLUSION
This work proposes an intelligent homecare
appliance system for the detection and reporting
of potentially dangerous situations in the home.
The system is constructed based on intelligent
techniques which use video surveillance and an
IoT scheme. As a human-centered concern, the
system includes the novel safety feature of
preventing children from activating smart appliances. When a potentially dangerous situation
arises, a mobile App can notify remote users of
the situation immediately.

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Tsung-Han Tsai is a Professor with the National
Central University, Taoyuan, Taiwan. Contact him at
han@ee.ncu.edu.tw

Syst., Jun. 2015, pp. 12-18.
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Kung-Long Zhang received the M.S. degree in
electrical engineering from the National Central University, Taoyuan, Taiwan, in 2016. Contact him at
dontmove@dsp.ee.ncu.edu.tw
Yih-Ru Tsai received the M.S. degree in electrical
engineering from the National Central University,
Taoyuan, Taiwan, in 2017. Contact him at
raymail11@dsp.ee.edu.edu.tw

2011.

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