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Title: Learning data science methods through a mobile device and full body motion data
This study presents a mobile app that facilitates undergraduate students to learn data science through their own full body motions. Leveraging the built-in camera of a mobile device, the proposed app captures the user and feeds their images into an open-source computer-vision algorithm that localizes the key joint points of human body. As students can participate in the entire data collection process, the obtained motion data is context-rich and personally relevant to them. The app utilizes the collected motion data to explain various concepts and methods in data science under the context of human movements. The app also visualizes the geometric interpretation of data through various visual aids, such as interactive graphs and figures. In this study, we use principal component analysis, a commonly used dimensionality reduction method, as an example to demonstrate the proposed learning framework. Strategies to encompass other learning modules are also discussed for further improvement.  more » « less
Award ID(s):
2013451
PAR ID:
10525381
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Sage Publisher
Date Published:
Journal Name:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Volume:
67
Issue:
1
ISSN:
1071-1813
Page Range / eLocation ID:
277 to 282
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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