Online Learning of Wearable Sensing for Human Activity Recognition. Zhang, Y., Gao, B., Yang, D., Woo, W. L., & Wen, H. IEEE Internet of Things Journal, 2022. Conference Name: IEEE Internet of Things Journaldoi abstract bibtex The paper presents a novel semi supervised learning method for wearable sensors to recognize human activities. The proposed method is termed as tri-VFDT (Very Fast Decision Tree). The proposed method is a more efficient version of the Hoeffding tree and three VFDT are generated from the original labeled example set and refined using unlabeled examples. Based on the heuristic growth characteristics of VFDT, a tri training framework is proposed which uses unlabeled data to update the model without labeled data. This significantly reduces the computational time and storage of the data processing. In addition, the proposed method is embedded into wearable devices for online learning, while the test data flow is regarded as the unlabeled data to update model. The experiment collects data stream of 16 minutes with motion state switching frequently while the wearable devices recognize motions in real time. An experimental comparison has also been undertaken for performance evaluation between the wearable and computation using desktop computer. The obtained results show that only minor difference in terms of the f1-score rendered by the proposed method online or offline. This is a prominent characteristic for wearable computing within internet of thing (IOT).
@article{zhang_online_2022,
title = {Online {Learning} of {Wearable} {Sensing} for {Human} {Activity} {Recognition}},
issn = {2327-4662},
doi = {10.1109/JIOT.2022.3188785},
abstract = {The paper presents a novel semi supervised learning method for wearable sensors to recognize human activities. The proposed method is termed as tri-VFDT (Very Fast Decision Tree). The proposed method is a more efficient version of the Hoeffding tree and three VFDT are generated from the original labeled example set and refined using unlabeled examples. Based on the heuristic growth characteristics of VFDT, a tri training framework is proposed which uses unlabeled data to update the model without labeled data. This significantly reduces the computational time and storage of the data processing. In addition, the proposed method is embedded into wearable devices for online learning, while the test data flow is regarded as the unlabeled data to update model. The experiment collects data stream of 16 minutes with motion state switching frequently while the wearable devices recognize motions in real time. An experimental comparison has also been undertaken for performance evaluation between the wearable and computation using desktop computer. The obtained results show that only minor difference in terms of the f1-score rendered by the proposed method online or offline. This is a prominent characteristic for wearable computing within internet of thing (IOT).},
journal = {IEEE Internet of Things Journal},
author = {Zhang, Yiwei and Gao, Bin and Yang, Daili and Woo, Wai Lok and Wen, Houlai},
year = {2022},
note = {Conference Name: IEEE Internet of Things Journal},
keywords = {Classification algorithms, Computational modeling, Feature extraction, Histograms, Predictive models, Training, Wearable computers, online learning, real-time activity recognition, semi-supervised learning, wearable device},
pages = {1--1},
}
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In addition, the proposed method is embedded into wearable devices for online learning, while the test data flow is regarded as the unlabeled data to update model. The experiment collects data stream of 16 minutes with motion state switching frequently while the wearable devices recognize motions in real time. An experimental comparison has also been undertaken for performance evaluation between the wearable and computation using desktop computer. The obtained results show that only minor difference in terms of the f1-score rendered by the proposed method online or offline. 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