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Machine Learning-Based Activity Pattern Classification Using Personal PM 2.5 Exposure Information

Author

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  • JinSoo Park

    (Department of Industrial Cooperation, Soonchunhyang University, Asan 31538, Korea)

  • Sungroul Kim

    (Department of ICT Environmental Health System, Graduate School, Soonchunhyang University, Asan 31538, Korea)

Abstract

The activity pattern is a significant factor in identifying hotspots of personal exposure to air pollutants, such as PM 2.5 . However, the recording process of an activity pattern can be annoying to study participants, because they are often asked to bring a diary or a tracking recorder to write or validate their activity patterns when they change their activity profiles. Furthermore, the accuracy of the records of activity patterns can be lower, because people can mistakenly record them. Thus, this paper proposes an idea to overcome these problems and make the whole data-collection process easier and more reliable. Our idea was based on transforming training data using the statistical properties of the children’s personal exposure level to PM 2.5 , temperature, and relative humidity and applying the properties to a decision tree algorithm for classification of activity patterns. From our final machine-learning modeling processes, we observed that the accuracy for activity-pattern classification was more than 90% in both the training and test data. We believe that our methodology can be used effectively in data-collection tasks and alleviate the annoyance that study participants may feel.

Suggested Citation

  • JinSoo Park & Sungroul Kim, 2020. "Machine Learning-Based Activity Pattern Classification Using Personal PM 2.5 Exposure Information," IJERPH, MDPI, vol. 17(18), pages 1-11, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6573-:d:411191
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    References listed on IDEAS

    as
    1. Jingmei Dong & Su Zhang & Li Xia & Yi Yu & Shuangshuang Hu & Jingyu Sun & Ping Zhou & Peijie Chen, 2018. "Physical Activity, a Critical Exposure Factor of Environmental Pollution in Children and Adolescents Health Risk Assessment," IJERPH, MDPI, vol. 15(2), pages 1-16, January.
    2. Yizheng Wu & Guohua Song, 2019. "The Impact of Activity-Based Mobility Pattern on Assessing Fine-Grained Traffic-Induced Air Pollution Exposure," IJERPH, MDPI, vol. 16(18), pages 1-13, September.
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    Cited by:

    1. Kyent-Yon Yie & Tsair-Wei Chien & Yu-Tsen Yeh & Willy Chou & Shih-Bin Su, 2021. "Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development," IJERPH, MDPI, vol. 18(5), pages 1-15, March.
    2. Rok Novak & Ioannis Petridis & David Kocman & Johanna Amalia Robinson & Tjaša Kanduč & Dimitris Chapizanis & Spyros Karakitsios & Benjamin Flückiger & Danielle Vienneau & Ondřej Mikeš & Céline Degrend, 2021. "Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign," IJERPH, MDPI, vol. 18(21), pages 1-18, November.
    3. Raj P. Fadadu & John R. Balmes & Stephanie M. Holm, 2020. "Differences in the Estimation of Wildfire-Associated Air Pollution by Satellite Mapping of Smoke Plumes and Ground-Level Monitoring," IJERPH, MDPI, vol. 17(21), pages 1-9, November.

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