• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/
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Deep Learning-Based User Emergency Event Detection Algorithms Fusing Vision, Audio, Activity and Dust Sensors


Ju-ho Jung, Do-hyun Lee, Seong-su Kim, Jun-ho Ahn, Journal of Internet Computing and Services, Vol. 21, No. 5, pp. 109-118, Oct. 2020
10.7472/jksii.2020.21.5.109, Full Text:  HTML
Keywords: vision, Audio, Activity, Dust, Sensors, Deep Learning, abnormal event, patterns

Abstract

Recently, people are spending a lot of time inside their homes because of various diseases. It is difficult to ask others for help in the case of a single-person household that is injured in the house or infected with a disease and needs help from others. In this study, an algorithm is proposed to detect emergency event, which are situations in which single-person households need help from others, such as injuries or disease infections, in their homes. It proposes vision pattern detection algorithms using home CCTVs, audio pattern detection algorithms using artificial intelligence speakers, activity pattern detection algorithms using acceleration sensors in smartphones, and dust pattern detection algorithms using air purifiers. However, if it is difficult to use due to security issues of home CCTVs, it proposes a fusion method combining audio, activity and dust pattern sensors. Each algorithm collected data through YouTube and experiments to measure accuracy.


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Cite this article
[APA Style]
Jung, J., Lee, D., Kim, S., & Ahn, J. (2020). Deep Learning-Based User Emergency Event Detection Algorithms Fusing Vision, Audio, Activity and Dust Sensors. Journal of Internet Computing and Services, 21(5), 109-118. DOI: 10.7472/jksii.2020.21.5.109.

[IEEE Style]
J. Jung, D. Lee, S. Kim, J. Ahn, "Deep Learning-Based User Emergency Event Detection Algorithms Fusing Vision, Audio, Activity and Dust Sensors," Journal of Internet Computing and Services, vol. 21, no. 5, pp. 109-118, 2020. DOI: 10.7472/jksii.2020.21.5.109.

[ACM Style]
Ju-ho Jung, Do-hyun Lee, Seong-su Kim, and Jun-ho Ahn. 2020. Deep Learning-Based User Emergency Event Detection Algorithms Fusing Vision, Audio, Activity and Dust Sensors. Journal of Internet Computing and Services, 21, 5, (2020), 109-118. DOI: 10.7472/jksii.2020.21.5.109.