Gaining Insight from Physical Activity Data using a Similarity-based Interactive Visualization
Abstract
This paper presents a new interactive visualization approach which aims to help and support the user in gaining insight over his physical activity data. The main novelty of the proposed visualization approach is the representation of similarities in the physical activity patterns in time using data clustering techniques, in addition to the continuous physical activity representation over a circular chart. This grouping of similar activity patterns helps identifying meaningful events or behaviors, combined with the periodicity highlighting circular charts. The user is able to interact with the visualization during the knowledge discovery process by changing the represented time-scale, time-frame and the number of clusters used for the user’s physical activity pattern categorization. Additionally, the proposed visualization approach allows to easily report and store the insights gained during the visual data analysis process, by adding a textual description linked to the particular user tailored visualization configuration which led to that insight.
BIB_text
title = {Gaining Insight from Physical Activity Data using a Similarity-based Interactive Visualization},
pages = {117-124},
volume = {2},
keywds = {
Personal Visualization, Personal Visual Analytics, Visualization Insights, Personal INFOVIS, Personal Informatics, Data Clustering, Time-series Data, Periodic Events, Physical Activity, Quantified-Self, Lifelogging.
}
abstract = {
This paper presents a new interactive visualization approach which aims to help and support the user in gaining insight over his physical activity data. The main novelty of the proposed visualization approach is the representation of similarities in the physical activity patterns in time using data clustering techniques, in addition to the continuous physical activity representation over a circular chart. This grouping of similar activity patterns helps identifying meaningful events or behaviors, combined with the periodicity highlighting circular charts. The user is able to interact with the visualization during the knowledge discovery process by changing the represented time-scale, time-frame and the number of clusters used for the user’s physical activity pattern categorization. Additionally, the proposed visualization approach allows to easily report and store the insights gained during the visual data analysis process, by adding a textual description linked to the particular user tailored visualization configuration which led to that insight.
}
isbn = {978-989-758-175-5},
date = {2016-02-27},
year = {2016},
}
