EconPapers    
Economics at your fingertips  
 

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

JinSoo Park and Sungroul Kim
Additional contact information
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

IJERPH, 2020, vol. 17, issue 18, 1-11

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.

Keywords: machine learning; activity-pattern analysis; environmental data; PM 2.5 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/18/6573/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/18/6573/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:18:p:6573-:d:411191

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6573-:d:411191