Daily Life Human Activity Recognition Using Smart Watch 


Vol. 42,  No. 12, pp. 2441-2449, Dec.  2017
10.7840/kics.2017.42.12.2441


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  Abstract

Recently, human activity recognition has been actively studied with smart phone and wearable devices using machine learning. Most human activity recognition techniques, however, consider active activities only such as walking and running. Using smart watch, this paper proposes a new human activity recognition technique which can recognize in-active activities in daily life such as relaxation, office work, and reading, as well as active activities. An experiment was performed to collect realistic smart watch data from a range of activities of daily life. The experimental results showed that the proposed machine learning model can achieve the maximum prediction rate of 93%.

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  Cite this article

[IEEE Style]

M. Kwon and S. Choi, "Daily Life Human Activity Recognition Using Smart Watch," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 12, pp. 2441-2449, 2017. DOI: 10.7840/kics.2017.42.12.2441.

[ACM Style]

Min-Cheol Kwon and Sunwoong Choi. 2017. Daily Life Human Activity Recognition Using Smart Watch. The Journal of Korean Institute of Communications and Information Sciences, 42, 12, (2017), 2441-2449. DOI: 10.7840/kics.2017.42.12.2441.

[KICS Style]

Min-Cheol Kwon and Sunwoong Choi, "Daily Life Human Activity Recognition Using Smart Watch," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 12, pp. 2441-2449, 12. 2017. (https://doi.org/10.7840/kics.2017.42.12.2441)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880