• Jun 17] Visual Storytelling Contest videos and materials are updated.
• Apr 14] Workshops (PacificVAST) schedules are updated.
• Mar 7] Conference program and keynote talks are updated.
• Feb] Registration is now available!
• Feb] Accommodation information is updated.

• 2016
  Taipei
  Apr 19 - Apr 22
• 2015
  Hangzhou
  Apr 14 - Apr 17
• 2014
  Yokohama
  Mar 04 - Mar 07
• 2013
  Sydney
  Feb 26 - Mar 01
• 2012
  Songdo
  Feb 28 - Mar 02
• 2011
  Hong Kong
  Mar 01 - Mar 04
• 2010
  Taipei
  Mar 02 - Mar 05
• 2009
  Beijing
  Apr 20 - Apr 23
• 2008
  Kyoto
  Mar 04 - Mar 07

• Facebook Official Facebook Page

• Twitter Official Twitter Account

• WeChat Official WeChat Account (亚太地区可视化会议微信公众号)

Outlier Identification of Plantar Pressure Data

• Chen-Hsiang Liao
  National Taipei University of Technology
• Tung-Ju Hsieh tjhsieh (at) ntut.edu.tw
  National Taipei University of Technology

Abstract

Plantar pressure data is an important foundation for sports studies. It includes 20 parameters from the index arch, left foot, and right foot plantar pressure. It causes data observation inconvenient because of complicated property and outlier. We developed a database platform for users to import plantar pressure to the database. We used outlier identification to help sports researchers studying plantar pressure data. We used plantar pressure devisee to collect plantar pressure data sets obtained from different groups of college athletes. The data sets contain a total of 1,006 participants from 26 different sport teams. In order to research foot symptoms, we propose using visualization of plantar pressure data sets for comparative visualization using parallel coordinate. Average values and standard deviation of data sets are computed. We look for outliers in the data sets using a range of statistical values such as standard deviation. It can help sports researchers to identify outliers who have high risk of symptoms. We studied tennis, table tennis, and badminton. Sports researchers can use comparative visualization to compare difference data sets. It can save sports researchers time in operate plantar data sets and help them to make observation between different sports. Further, they can diagnose foot symptoms from the clustering in the data sets.