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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 9/2018

09.04.2017 | Original Article

OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition

verfasst von: Lisha Hu, Yiqiang Chen, Jindong Wang, Chunyu Hu, Xinlong Jiang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 9/2018

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Abstract

Miscellaneous mini-wearable devices (Jawbone Up, Apple Watch, Google Glass, et al.) have emerged in recent years to recognize the user’s activities of daily living (ADLs) such as walking, running, climbing and bicycling. To better suits a target user, a generic activity recognition (AR) model inside the wearable devices requires to adapt itself according to the user’s personality in terms of wearing styles and so on. In this paper, an online kernelized and regularized extreme learning machine (OKRELM) is proposed for wearable-based activity recognition. A small-scale but important subset of every incoming data chunk is chosen to go through the update stage during the online sequential learning. Therefore, OKRELM is a lightweight incremental learning model with less time consumption during the update and prediction phase, a robust and effective classifier compared with the batch learning scheme. The performance of OKRELM is evaluated and compared with several related approaches on a UCI online available AR dataset and experimental results show the efficiency and effectiveness of OKRELM.
Vorheriger Artikel Low-rank matrix recovery via smooth rank function and its application in image restoration

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Metadaten
Titel
OKRELM: online kernelized and regularized extreme learning machine for wearable-based activity recognition
verfasst von
Lisha Hu
Yiqiang Chen
Jindong Wang
Chunyu Hu
Xinlong Jiang
Publikationsdatum
09.04.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 9/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0666-8

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