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Personalized mobile physical activity recognition

Published: 08 September 2013 Publication History

Abstract

Personalization of activity recognition has become a topic of interest recently. This paper presents a novel concept, using a set of classifiers as general model, and retraining only the weight of the classifiers with new labeled data from a previously unknown subject. Experiments with different methods based on this concept show that it is a valid approach for personalization. An important benefit of the proposed concept is its low computational cost compared to other approaches, making it also feasible for mobile applications. Moreover, more advanced classifiers (e.g. boosted decision trees) can be combined with the new concept, to achieve good performance even on complex classification tasks. Finally, a new algorithm is introduced based on the proposed concept, which outperforms existing methods, thus further increasing the performance of personalized applications.

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Cited By

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  • (2024)Deep neural networks for wearable sensor-based activity recognition in Parkinson’s disease: investigating generalizability and model complexityBioMedical Engineering OnLine10.1186/s12938-024-01214-223:1Online publication date: 9-Feb-2024
  • (2023)Incremental Semi-Supervised Tri-Training: A framework for model personalization2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10386015(4035-4042)Online publication date: 5-Dec-2023
  • (2023)Wearable Computing Systems: State‐of‐the‐Art and Research ChallengesHandbook of Human‐Machine Systems10.1002/9781119863663.ch29(349-371)Online publication date: 7-Jul-2023
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    cover image ACM Conferences
    ISWC '13: Proceedings of the 2013 International Symposium on Wearable Computers
    September 2013
    160 pages
    ISBN:9781450321273
    DOI:10.1145/2493988
    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: 08 September 2013

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    Author Tags

    1. activity recognition
    2. algorithm
    3. personalization

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    ISWC '13 Paper Acceptance Rate 20 of 101 submissions, 20%;
    Overall Acceptance Rate 38 of 196 submissions, 19%

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    Cited By

    View all
    • (2024)Deep neural networks for wearable sensor-based activity recognition in Parkinson’s disease: investigating generalizability and model complexityBioMedical Engineering OnLine10.1186/s12938-024-01214-223:1Online publication date: 9-Feb-2024
    • (2023)Incremental Semi-Supervised Tri-Training: A framework for model personalization2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10386015(4035-4042)Online publication date: 5-Dec-2023
    • (2023)Wearable Computing Systems: State‐of‐the‐Art and Research ChallengesHandbook of Human‐Machine Systems10.1002/9781119863663.ch29(349-371)Online publication date: 7-Jul-2023
    • (2022)SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity RecognitionIEEE Transactions on Mobile Computing10.1109/TMC.2022.3171312(1-1)Online publication date: 2022
    • (2022)A Personalized Deep Neural Network to Recognize Human Activities in Healthy Subjects2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)10.1109/ICBME57741.2022.10052893(325-332)Online publication date: 21-Dec-2022
    • (2022)Deep learning and model personalization in sensor-based human activity recognitionJournal of Reliable Intelligent Environments10.1007/s40860-021-00167-w9:1(27-39)Online publication date: 7-Jan-2022
    • (2022)A Split-Then-Join Lightweight Hybrid Majority Vote ClassifierSoft Computing and its Engineering Applications10.1007/978-3-031-05767-0_14(167-180)Online publication date: 7-May-2022
    • (2021)Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer DataSensors10.3390/s2104121421:4(1214)Online publication date: 9-Feb-2021
    • (2021)Trends in human activity recognition using smartphonesJournal of Reliable Intelligent Environments10.1007/s40860-021-00147-07:3(189-213)Online publication date: 3-Jul-2021
    • (2021)A Lightweight Hybrid Majority Vote Classifier Using Top-k DatasetSoft Computing and its Engineering Applications10.1007/978-981-16-0708-0_16(182-196)Online publication date: 5-Mar-2021
    • Show More Cited By

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