research-article

Label Propagation: An Unsupervised Similarity Based Method for Integrating New Sensors in Activity Recognition Systems

Authors:

Paul LukowiczAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 94, Pages 1 - 24

https://doi.org/10.1145/3130959

Published: 11 September 2017 Publication History

Abstract

Current activity recognition systems mostly work with static, pre-trained sensor configuration. As a consequence they are not able to leverage new sensors appearing in their environment (e.g. the user buying a new wearable devices). In this work we present a method inspired by semi-supervised graph methods that can add new sensors to an existing system in an unsupervised manner. We have evaluated our method in two well known activity recognition datasets and found that it can take advantage of the information provided by new unknown sensor sources, improving the recognition performance in most cases.

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Cited By

Ray AKolekar M(2024)Transfer learning and its extensive appositeness in human activity recognition: A surveyExpert Systems with Applications10.1016/j.eswa.2023.122538240(122538)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122538
Jones MByun CJohnson NSeppi K(2022)Understanding the Roles of Video and Sensor Data in the Annotation of Human ActivitiesInternational Journal of Human–Computer Interaction10.1080/10447318.2022.210158939:18(3634-3648)Online publication date: Aug-2022
https://doi.org/10.1080/10447318.2022.2101589
You XZhang LYu HYuan MLi X(2021)KATNProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34949575:4(1-26)Online publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1145/3494957
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Label Propagation: An Unsupervised Similarity Based Method for Integrating New Sensors in Activity Recognition Systems
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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

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Copyright © 2017 ACM.

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Association for Computing Machinery

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Publication History

Published: 11 September 2017

Accepted: 01 July 2017

Revised: 01 June 2017

Received: 01 May 2017

Published in IMWUT Volume 1, Issue 3

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Cited By

Ray AKolekar M(2024)Transfer learning and its extensive appositeness in human activity recognition: A surveyExpert Systems with Applications10.1016/j.eswa.2023.122538240(122538)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122538
Jones MByun CJohnson NSeppi K(2022)Understanding the Roles of Video and Sensor Data in the Annotation of Human ActivitiesInternational Journal of Human–Computer Interaction10.1080/10447318.2022.210158939:18(3634-3648)Online publication date: Aug-2022
https://doi.org/10.1080/10447318.2022.2101589
You XZhang LYu HYuan MLi X(2021)KATNProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34949575:4(1-26)Online publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1145/3494957
Lu WChen YWang JQin X(2021)Cross-domain activity recognition via substructural optimal transportNeurocomputing10.1016/j.neucom.2021.04.124454(65-75)Online publication date: Sep-2021
https://doi.org/10.1016/j.neucom.2021.04.124
Saeedi RSasani KGebremedhin A(2020)Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and EvaluationSensors10.3390/s2007193220:7(1932)Online publication date: 30-Mar-2020
https://doi.org/10.3390/s20071932
Rokni SNourollahi MAlinia PMirzadeh IPedram MGhasemzadeh H(2020)TransNetACM Transactions on Design Automation of Electronic Systems10.1145/341406226:1(1-31)Online publication date: 10-Sep-2020
https://dl.acm.org/doi/10.1145/3414062
Liu SYao SHuang YLiu DShao HZhao YLi JWang TWang RYang CAbdelzaher T(2020)Handling Missing Sensors in Topology-Aware IoT Applications with Gated Graph Neural NetworkProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34118184:3(1-31)Online publication date: 4-Sep-2020
https://dl.acm.org/doi/10.1145/3411818
Qin XChen YWang JYu C(2020)Cross-Dataset Activity Recognition via Adaptive Spatial-Temporal Transfer LearningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33698183:4(1-25)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1145/3369818
Papagiannaki AZacharaki EKalouris GKalogiannis SDeltouzos KEllul JMegalooikonomou V(2019)Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent DataSensors10.3390/s1904088019:4(880)Online publication date: 20-Feb-2019
https://doi.org/10.3390/s19040880
Hu CChen YPeng XYu HGao CHu L(2019)A Novel Feature Incremental Learning Method for Sensor-Based Activity RecognitionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.285515931:6(1038-1050)Online publication date: 1-Jun-2019
https://doi.org/10.1109/TKDE.2018.2855159
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