Abstract
The activity pattern is a significant factor in identifying hotspots of personal exposure to air pollutants, such as PM2.5. 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 PM2.5, 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.
Highlights
Environmental risk assessment [1,2] plays a major role in finding out relationships between the level of exposure to environmental pollutants and its effect on our bodies caused by the exposure
Unlike studies examining PM2.5 exposure levels by activity pattern with or without acceleration data [3,4,5], as an opposite way, this study aimed to develop a predicting model identifying human activity patterns by analyzing environmental information
After re-arranging the obtained PM2.5 data according to activity patterns, the statistical values of the PM2.5 data representing each activity pattern were extracted and used as added feature values, which means that the statistics of PM2.5 data were used as training data
Summary
Environmental risk assessment [1,2] plays a major role in finding out relationships between the level of exposure to environmental pollutants and its effect on our bodies caused by the exposure. For this purpose, people have been gathering numerous environmental data to find out the relationship for a long time. It can lead to errors when one chooses the wrong options in the menu. To minimize these problems and make the whole data-collection process smoother, it is necessary to make the activity-pattern recording process automatic. In this paper, we propose an idea to solve the inconvenience of manual activity-pattern input in data collection for risk assessment
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