research-article

Real-time gait classification for persuasive smartphone apps: structuring the literature and pushing the limits

Authors:

Oliver S. Schneider,

Karon E. MacLean,

Michael M.A. WuAuthors Info & Claims

IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces

Pages 161 - 172

https://doi.org/10.1145/2449396.2449418

Published: 19 March 2013 Publication History

Abstract

Persuasive technology is now mobile and context-aware. Intelligent analysis of accelerometer signals in smartphones and other specialized devices has recently been used to classify activity (e.g., distinguishing walking from cycling) to encourage physical activity, sustainable transport, and other social goals. Unfortunately, results vary drastically due to differences in methodology and problem domain. The present report begins by structuring a survey of current work within a new framework, which highlights comparable characteristics between studies; this provided a tool by which we and others can understand the current state-of-the art and guide research towards existing gaps. We then present a new user study, positioned in an identified gap, that pushes limits of current success with a challenging problem: the real-time classification of 15 similar and novel gaits suitable for several persuasive application areas, focused on the growing phenomenon of exercise games. We achieve a mean correct classification rate of 78.1% of all 15 gaits with a minimal amount of personalized training of the classifier for each participant when carried in any of 6 different carrying locations (not known a priori). When narrowed to a subset of four gaits and one location that is known, this improves to means of 92.2% with and 87.2% without personalization. Finally, we group our findings into design guidelines and quantify variation in accuracy when an algorithm is trained for a known location and participant.

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Cited By

Vec VTomažič SKos AUmek A(2024)Trends in real-time artificial intelligence methods in sports: a systematic reviewJournal of Big Data10.1186/s40537-024-01026-011:1Online publication date: 26-Oct-2024
https://doi.org/10.1186/s40537-024-01026-0
Feely CCaulfield BLawlor ASmyth B(2022)An Extended Case-Based Approach to Race-Time Prediction for Recreational Marathon RunnersCase-Based Reasoning Research and Development10.1007/978-3-031-14923-8_22(335-349)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-14923-8_22
Prasanth HCaban MKeller UCourtine GIjspeert AVallery Hvon Zitzewitz J(2021)Wearable Sensor-Based Real-Time Gait Detection: A Systematic ReviewSensors10.3390/s2108272721:8(2727)Online publication date: 13-Apr-2021
https://doi.org/10.3390/s21082727
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Index Terms

Real-time gait classification for persuasive smartphone apps: structuring the literature and pushing the limits
1. Human-centered computing

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cover image ACM Conferences

IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces

March 2013

470 pages

ISBN:9781450319652

DOI:10.1145/2449396

General Chair:
Jihie Kim
University of Southern California, USA
,
Program Chairs:
Jeffrey Nichols
IBM Research -- Almaden, USA
,
Pedro Szekely
University of Southern California, USA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 19 March 2013

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IUI '13

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IUI '13: 18th International Conference on Intelligent User Interfaces

March 19 - 22, 2013

California, Santa Monica, USA

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IUI '13 Paper Acceptance Rate 43 of 192 submissions, 22%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Vec VTomažič SKos AUmek A(2024)Trends in real-time artificial intelligence methods in sports: a systematic reviewJournal of Big Data10.1186/s40537-024-01026-011:1Online publication date: 26-Oct-2024
https://doi.org/10.1186/s40537-024-01026-0
Feely CCaulfield BLawlor ASmyth B(2022)An Extended Case-Based Approach to Race-Time Prediction for Recreational Marathon RunnersCase-Based Reasoning Research and Development10.1007/978-3-031-14923-8_22(335-349)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-14923-8_22
Prasanth HCaban MKeller UCourtine GIjspeert AVallery Hvon Zitzewitz J(2021)Wearable Sensor-Based Real-Time Gait Detection: A Systematic ReviewSensors10.3390/s2108272721:8(2727)Online publication date: 13-Apr-2021
https://doi.org/10.3390/s21082727
Feely CCaulfield BLawlor ASmyth B(2020)Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training PlansCase-Based Reasoning Research and Development10.1007/978-3-030-58342-2_5(67-81)Online publication date: 3-Oct-2020
https://doi.org/10.1007/978-3-030-58342-2_5
Ivanov KMei ZLubich LGuo NXile DZhao ZOmisore OHo DWang L(2019)Design of a Sensor Insole for Gait AnalysisIntelligent Robotics and Applications10.1007/978-3-030-27538-9_37(433-444)Online publication date: 3-Aug-2019
https://doi.org/10.1007/978-3-030-27538-9_37
Mei ZIvanov KLubich LWang L(2019)Recognition of Pes Cavus Foot Using Smart Insole: A Pilot StudyIntelligent Robotics and Applications10.1007/978-3-030-27535-8_58(654-662)Online publication date: 2-Aug-2019
https://doi.org/10.1007/978-3-030-27535-8_58
Bayındır L(2017)A survey of people-centric sensing studies utilizing mobile phone sensorsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-1704469:4(421-448)Online publication date: 19-Jun-2017
https://doi.org/10.3233/AIS-170446
Riaz QVögele AKrüger BWeber A(2015)One Small Step for a Man: Estimation of Gender, Age and Height from Recordings of One Step by a Single Inertial SensorSensors10.3390/s15122990715:12(31999-32019)Online publication date: 19-Dec-2015
https://doi.org/10.3390/s151229907
Del Rosario MRedmond SLovell N(2015)Tracking the Evolution of Smartphone Sensing for Monitoring Human MovementSensors10.3390/s15081890115:8(18901-18933)Online publication date: 31-Jul-2015
https://doi.org/10.3390/s150818901
Karuei ISchneider OStern BChuang MMaclean K(2014)RRACEPervasive and Mobile Computing10.1016/j.pmcj.2013.09.00613(52-66)Online publication date: 1-Aug-2014
https://dl.acm.org/doi/10.1016/j.pmcj.2013.09.006

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