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Introducing GaitLib : a library for real-time gait analysis in smartphones Wu, Michael Ming-An; Schneider, Oliver Stirling; Karuei, Idin; Leong, Larissa; MacLean, Karon

Abstract

Modern smartphones are pervasive, powerful, and richly endowed with sensors. These have recently enabled smartphone use for gait analysis, a powerful resource for many applications including biometric identification and context-aware apps that motivate exercises. However, there is little support for software R&D with mobile gait analysis beyond basic sensing. Through a participatory design process, we developed GaitLib, a library for real-time gait analysis in smart- phones. With on-board accelerometers and other sensors, GaitLib supports both cadence estimation and gait classification. The library is implemented on the Android platform, using Weka as the classification engine while supporting customizable gait analysis algorithms. An end user who participated in the design team used successive versions of the library in a series of studies, providing design input which was used to improve the library’s functionality and usability. This library can support and stimulate future research in gait analysis and the development of innovative applications.

Item Metadata

Title
Introducing GaitLib : a library for real-time gait analysis in smartphones
Creator
Wu, Michael Ming-An; Schneider, Oliver Stirling; Karuei, Idin; Leong, Larissa; MacLean, Karon
Date Issued
2014-05-28
Description
Modern smartphones are pervasive, powerful, and richly endowed with sensors. These have recently enabled smartphone use for gait analysis, a powerful resource for many applications including biometric identification and context-aware apps that motivate exercises. However, there is little support for software R&D with mobile gait analysis beyond basic sensing. Through a participatory design process, we developed GaitLib, a library for real-time gait analysis in smart- phones. With on-board accelerometers and other sensors, GaitLib supports both cadence estimation and gait classification. The library is implemented on the Android platform, using Weka as the classification engine while supporting customizable gait analysis algorithms. An end user who participated in the design team used successive versions of the library in a series of studies, providing design input which was used to improve the library’s functionality and usability. This library can support and stimulate future research in gait analysis and the development of innovative applications.
Subject
Computer Science
Genre
Article
Type
Text
Language
eng
Date Available
2014-05-28
Provider
Vancouver : University of British Columbia Library
Rights
Attribution-NonCommercial 2.5 Canada
DOI
10.14288/1.0050993
URI
http://hdl.handle.net/2429/46848
Affiliation
Science, Faculty of; Computer Science, Department of
Peer Review Status
Unreviewed
Scholarly Level
Faculty; Graduate; Undergraduate
Rights URI
http://creativecommons.org/licenses/by-nc/2.5/ca/
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Attribution-NonCommercial 2.5 Canada