research-article

ACTIVITY RECOGNITION ON SMART DEVICES: Dealing with diversity in the wild

Authors:

Mikkel Baun Kjærgaard,

Anind Dey,

Mads Møller Jensen,

Tobias SonneAuthors Info & Claims

GetMobile: Mobile Computing and Communications, Volume 20, Issue 1

Pages 34 - 38

https://doi.org/10.1145/2972413.2972425

Published: 14 July 2016 Publication History

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Abstract

Smart consumer devices are omnipresent in our everyday lives. We find novel interesting applications of smart devices, such as mobile phones, tablets, smartwatches and smartglasses, in monitoring personal health, tracking sporting performances, identifying physical activities and obtaining navigation information among many others. These novel applications make use of a large variety of the smart devices' sensors, such as accelerometer, gyroscope and GPS. However, the usefulness of these applications relies often primarily on the abilities of interpreting noisy, and often biased, measurements from said sensors in order to extract high-level context information, such as the activity currently performed by the user.

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Kaseris MKostavelis IMalassiotis S(2024)A Comprehensive Survey on Deep Learning Methods in Human Activity RecognitionMachine Learning and Knowledge Extraction10.3390/make60200406:2(842-876)Online publication date: 18-Apr-2024
https://doi.org/10.3390/make6020040
Khan PChaudhuri SSingh DAmaan F(2024)KGAN-Based Semisupervised Domain Adapted Human Activity RecognitionIEEE Sensors Letters10.1109/LSENS.2024.34811498:11(1-4)Online publication date: Nov-2024
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Abdel-Basset MHawash HChang VChakrabortty RRyan M(2022)Deep Learning for Heterogeneous Human Activity Recognition in Complex IoT ApplicationsIEEE Internet of Things Journal10.1109/JIOT.2020.30384169:8(5653-5665)Online publication date: 15-Apr-2022
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Published In

cover image GetMobile: Mobile Computing and Communications

GetMobile: Mobile Computing and Communications Volume 20, Issue 1

January 2016

34 pages

ISSN:2375-0529

EISSN:2375-0537

DOI:10.1145/2972413

Editor:
Eyal de Lara
University of Toronto

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 July 2016

Published in SIGMOBILE-GETMOBILE Volume 20, Issue 1

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Kaseris MKostavelis IMalassiotis S(2024)A Comprehensive Survey on Deep Learning Methods in Human Activity RecognitionMachine Learning and Knowledge Extraction10.3390/make60200406:2(842-876)Online publication date: 18-Apr-2024
https://doi.org/10.3390/make6020040
Khan PChaudhuri SSingh DAmaan F(2024)KGAN-Based Semisupervised Domain Adapted Human Activity RecognitionIEEE Sensors Letters10.1109/LSENS.2024.34811498:11(1-4)Online publication date: Nov-2024
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Abdel-Basset MHawash HChang VChakrabortty RRyan M(2022)Deep Learning for Heterogeneous Human Activity Recognition in Complex IoT ApplicationsIEEE Internet of Things Journal10.1109/JIOT.2020.30384169:8(5653-5665)Online publication date: 15-Apr-2022
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Hajifar SLamooki SCavuoto LMegahed FSun H(2021)Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer LearningSensors10.3390/s2119667721:19(6677)Online publication date: 8-Oct-2021
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Alajaji AGerych WBuquicchio LChandrasekaran KMansoor HAgu ERundensteiner E(2021)Smartphone Health Biomarkers: Positive Unlabeled Learning of In-the-Wild ContextsIEEE Pervasive Computing10.1109/MPRV.2021.305186920:1(50-61)Online publication date: 1-Jan-2021
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Buquicchio LGerych WAlajaji AChandrasekaran KMansoor HRundensteiner EAgu E(2021)Few-Shot Classification for Human Context Recognition Using Smartphone Data Traces2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA52953.2021.00060(345-350)Online publication date: Dec-2021
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Antar AAhmed MAhad M(2021)Sensor-Based Human Activity and Behavior ComputingVision, Sensing and Analytics: Integrative Approaches10.1007/978-3-030-75490-7_6(147-176)Online publication date: 6-Jun-2021
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Wang XLiang YYu ZGuo B(2020)Learning Latent Correlation of Heterogeneous Sensors Using Attention based Temporal Convolutional Network2020 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW51313.2020.00076(526-534)Online publication date: Nov-2020
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Ahad MAntar AAhmed MAhad MAntar AAhmed M(2020)Sensor-Based Benchmark Datasets: Comparison and AnalysisIoT Sensor-Based Activity Recognition10.1007/978-3-030-51379-5_6(95-121)Online publication date: 31-Jul-2020
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