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Article

A practical approach to recognizing physical activities

Authors:

Jonathan Lester,

Tanzeem Choudhury,

Gaetano BorrielloAuthors Info & Claims

PERVASIVE'06: Proceedings of the 4th international conference on Pervasive Computing

Pages 1 - 16

https://doi.org/10.1007/11748625_1

Published: 07 May 2006 Publication History

Abstract

We are developing a personal activity recognition system that is practical, reliable, and can be incorporated into a variety of health-care related applications ranging from personal fitness to elder care. To make our system appealing and useful, we require it to have the following properties: (i) data only from a single body location needed, and it is not required to be from the same point for every user; (ii) should work out of the box across individuals, with personalization only enhancing its recognition abilities; and (iii) should be effective even with a cost-sensitive subset of the sensors and data features. In this paper, we present an approach to building a system that exhibits these properties and provide evidence based on data for 8 different activities collected from 12 different subjects. Our results indicate that the system has an accuracy rate of approximately 90% while meeting our requirements. We are now developing a fully embedded version of our system based on a cell-phone platform augmented with a Bluetooth-connected sensor board.

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Cited By

Kouaho WEpstein D(2024)Investigating Perspectives of and Experiences with Low Cost Commercial Fitness WearablesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997408:4(1-22)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699740
Staab SBröning LLuderschmidt JMartin L(2022)Performance Comparison of Motion-Related Sensor Technology and Acoustic Sensor Technology in the Field of Human Health MonitoringProceedings of the 2022 ACM Conference on Information Technology for Social Good10.1145/3524458.3547220(198-204)Online publication date: 7-Sep-2022
https://dl.acm.org/doi/10.1145/3524458.3547220
Abidine MFergani B(2021)Activity Recognition From Smartphone Data Using WSVM-HMM ClassificationInternational Journal of E-Health and Medical Communications10.4018/IJEHMC.20211101.oa1112:6(1-20)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.4018/IJEHMC.20211101.oa11
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A practical approach to recognizing physical activities
1. Computing methodologies

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Information & Contributors

Information

Published In

cover image Guide Proceedings

PERVASIVE'06: Proceedings of the 4th international conference on Pervasive Computing

May 2006

2 pages

ISBN:3540338942

Editors:
Kenneth P. Fishkin
Google, Inc., 720 4th Ave. #400, Kirkland, WA
,
Bernt Schiele
Computer Science Department, TU Darmstadt, 720 4th Ave. #400, Kirkland, WA
,
Paddy Nixon
System Research Group, School of Computer Science and Informatics, UCD, 720 4th Ave. #400, Dublin, WA, Ireland
,
Aaron Quigley
Imaging, Visualisation & Graphics Lab, Systems Research Group, School of Computer Science & Informatics, University College Dublin, 720 4th Ave. #400, Dublin 4, WA, Ireland

Sponsors

SFI: Science Foundation Ireland
LERO: The Irish Software Engineering Research Centre
Intel Research
UCD: University College Dublin
Intel Ireland: Intel Ireland

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 07 May 2006

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Cited By

Kouaho WEpstein D(2024)Investigating Perspectives of and Experiences with Low Cost Commercial Fitness WearablesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997408:4(1-22)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699740
Staab SBröning LLuderschmidt JMartin L(2022)Performance Comparison of Motion-Related Sensor Technology and Acoustic Sensor Technology in the Field of Human Health MonitoringProceedings of the 2022 ACM Conference on Information Technology for Social Good10.1145/3524458.3547220(198-204)Online publication date: 7-Sep-2022
https://dl.acm.org/doi/10.1145/3524458.3547220
Abidine MFergani B(2021)Activity Recognition From Smartphone Data Using WSVM-HMM ClassificationInternational Journal of E-Health and Medical Communications10.4018/IJEHMC.20211101.oa1112:6(1-20)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.4018/IJEHMC.20211101.oa11
Alia SAdachi KHossain TLe NKaneko HLago POkita TInoue S(2021)Summary of the Third Nurse Care Activity Recognition Challenge - Can We Do from the Field Data?Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479391(428-433)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3479391
Kaneko HHossain TInoue S(2021)Analysis of Feature Importances for Automatic Generation of Care RecordsAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479354(316-321)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3479354
Leland JStanfill ECherian JHammond T(2021)Recognizing Seatbelt-Fastening Behavior with Wearable Technology and Machine LearningExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3451705(1-6)Online publication date: 8-May-2021
https://dl.acm.org/doi/10.1145/3411763.3451705
Xia QKorpela JNamioka YMaekawa T(2020)Robust Unsupervised Factory Activity Recognition with Body-worn Accelerometer Using Temporal Structure of Multiple Sensor Data MotifsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34118364:3(1-30)Online publication date: 4-Sep-2020
https://dl.acm.org/doi/10.1145/3411836
Faisal MSiraj MAbdullah MShahid OAbir FAhad MTentori MWeibel NVan Laerhoven KAbowd GSalim F(2020)A pragmatic signal processing approach for nurse care activity recognition using classical machine learningAdjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers10.1145/3410530.3414337(396-401)Online publication date: 10-Sep-2020
https://dl.acm.org/doi/10.1145/3410530.3414337
Chinpanthana NLiu Y(2020)Human Activities of Daily Living Recognition with Graph Convolutional NetworkProceedings of the 2020 6th International Conference on Computing and Artificial Intelligence10.1145/3404555.3404557(305-310)Online publication date: 23-Apr-2020
https://dl.acm.org/doi/10.1145/3404555.3404557
Phoophuangpairoj RCharnkeitkong P(2020)Developing a Mobile Application to Detect Improper Sitting Using Regression Analysis and an AccelerometerProceedings of the 4th International Conference on Machine Learning and Soft Computing10.1145/3380688.3380698(124-128)Online publication date: 17-Jan-2020
https://dl.acm.org/doi/10.1145/3380688.3380698
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