IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i18p6573-d411191.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/18/6573/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/18/6573/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ke Zhou & Sensen He & Yanli Zhou & Biljana Popeska & Garry Kuan & Li Chen & Ming-Kai Chin & Magdalena Mo Ching Mok & Christopher R. Edginton & Ian Culpan & J. Larry Durstine, 2021. "Implementation of Brain Breaks ® in the Classroom and Its Effects on Attitudes towards Physical Activity in a Chinese School Setting," IJERPH, MDPI, vol. 18(1), pages 1-14, January.
    2. Eun-hye Yoo & Qiang Pu & Youngseob Eum & Xiangyu Jiang, 2021. "The Impact of Individual Mobility on Long-Term Exposure to Ambient PM 2.5 : Assessing Effect Modification by Travel Patterns and Spatial Variability of PM 2.5," IJERPH, MDPI, vol. 18(4), pages 1-16, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6573-:d:411191. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.