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Applications of mobile activity recognition

Authors:

Jeffrey W. Lockhart,

Gary M. WeissAuthors Info & Claims

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Pages 1054 - 1058

https://doi.org/10.1145/2370216.2370441

Published: 05 September 2012 Publication History

Abstract

Activity Recognition (AR), which identifies the activity that a user performs, is attracting a tremendous amount of attention, especially with the recent explosion of smart mobile devices. These ubiquitous mobile devices, most notably but not exclusively smartphones, provide the sensors, processing, and communication capabilities that enable the development of diverse and innovative activity recognition-based applications. However, although there has been a great deal of research into activity recognition, surprisingly little practical work has been done in the area of applications in mobile devices. In this paper we describe and categorize a variety of activity recognition-based applications. Our hope is that this work will encourage the development of such applications and also influence the direction of activity recognition research.

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Cited By

Sun BChen C(2024)Sequence-Information Recognition Method Based on Integrated mDTWApplied Sciences10.3390/app1419871614:19(8716)Online publication date: 27-Sep-2024
https://doi.org/10.3390/app14198716
Mohammadi Foumani NMiller LTan CWebb GForestier GSalehi M(2024)Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyACM Computing Surveys10.1145/364944856:9(1-45)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3649448
Mäder AMeegahapola LGatica-Perez D(2024)Learning About Social Context From Smartphone Data: Generalization Across Countries and Daily Life MomentsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642444(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642444
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Index Terms

Applications of mobile activity recognition
1. Information systems
  1. Information systems applications

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Information

Published In

cover image ACM Conferences

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

September 2012

1268 pages

ISBN:9781450312240

DOI:10.1145/2370216

General Chair:
Anind K. Dey
Carnegie Mellon University
,
Program Chairs:
Hao-Hua Chu
National Taiwan University, Taiwan
,
Gillian Hayes
University of California, Irvine

Copyright © 2012 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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Association for Computing Machinery

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Publication History

Published: 05 September 2012

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Ubicomp '12

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Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing

September 5 - 8, 2012

Pennsylvania, Pittsburgh

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UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Sun BChen C(2024)Sequence-Information Recognition Method Based on Integrated mDTWApplied Sciences10.3390/app1419871614:19(8716)Online publication date: 27-Sep-2024
https://doi.org/10.3390/app14198716
Mohammadi Foumani NMiller LTan CWebb GForestier GSalehi M(2024)Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyACM Computing Surveys10.1145/364944856:9(1-45)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3649448
Mäder AMeegahapola LGatica-Perez D(2024)Learning About Social Context From Smartphone Data: Generalization Across Countries and Daily Life MomentsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642444(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642444
Li YXu Y(2024)Research on human activity recognition method based on wearable sensorsFourth International Conference on Sensors and Information Technology (ICSI 2024)10.1117/12.3029327(134)Online publication date: 6-May-2024
https://doi.org/10.1117/12.3029327
Alboueishi NNagem TAbdelnabi E(2024)Human Activity Recognition Using AutoML Approach2024 IEEE 4th International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA)10.1109/MI-STA61267.2024.10599699(637-641)Online publication date: 19-May-2024
https://doi.org/10.1109/MI-STA61267.2024.10599699
Foumani NTan CWebb GRezatofighi HSalehi M(2024)Series2vec: similarity-based self-supervised representation learning for time series classificationData Mining and Knowledge Discovery10.1007/s10618-024-01043-w38:4(2520-2544)Online publication date: 20-Jun-2024
https://doi.org/10.1007/s10618-024-01043-w
Zhao BLiu XChen WDeng R(2023)CrowdFL: Privacy-Preserving Mobile Crowdsensing System Via Federated LearningIEEE Transactions on Mobile Computing10.1109/TMC.2022.315760322:8(4607-4619)Online publication date: 1-Aug-2023
https://doi.org/10.1109/TMC.2022.3157603
Hu Y(2023)BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191928(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191928
Kandpal MSharma BBarik RChowdhury SPatra SDhaou I(2023)Human Activity Recognition in Smart Cities from Smart Watch Data using LSTM Recurrent Neural Networks2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)10.1109/ICAISC56366.2023.10085688(1-6)Online publication date: 23-Jan-2023
https://doi.org/10.1109/ICAISC56366.2023.10085688
Nguyen TDiakiw SVerMilyea MDinsmore APerugini MPerugini DHall J(2023)Efficient automated error detection in medical data using deep-learning and label-clusteringScientific Reports10.1038/s41598-023-45946-y13:1Online publication date: 9-Nov-2023
https://doi.org/10.1038/s41598-023-45946-y
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