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ConvBoost : boosting ConvNets for sensor-based activity recognition
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Shao, Shuai, Guan, Yu, Zhai, Bing, Missier, Paolo and Ploetz, Thomas (2023) ConvBoost : boosting ConvNets for sensor-based activity recognition. In: UNSPECIFIED. Published in: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7 (2). doi:10.1145/3596234 (In Press)
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Official URL: https://doi.org/10.1145/3596234
Abstract
Human activity recognition (HAR) is one of the core research themes in ubiquitous and wearable computing. With the shift to
deep learning (DL) based analysis approaches, it has become possible to extract high-level features and perform classification
in an end-to-end manner. Despite their promising overall capabilities, DL-based HAR may suffer from overfitting due to the
notoriously small, often inadequate, amounts of labeled sample data that are available for typical HAR applications. In response
to such challenges, we propose ConvBoost – a novel, three-layer, structured model architecture and boosting framework for
convolutional network based HAR. Our framework generates additional training data from three different perspectives for
improved HAR, aiming to alleviate the shortness of labeled training data in the field. Specifically, with the introduction of three
conceptual layers–Sampling Layer, Data Augmentation Layer, and Resilient Layer–we develop three “boosters”–R-Frame,
Mix-up, and C-Drop–to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively.
These new conceptual layers and boosters, that are universally applicable for any kind of convolutional network, have been
designed based on the characteristics of the sensor data and the concept of frame-wise HAR. In our experimental evaluation on
three standard benchmarks (Opportunity, PAMAP2, GOTOV) we demonstrate the effectiveness of our ConvBoost framework
for HAR applications based on variants of convolutional networks: vanilla CNN, ConvLSTM, and Attention Models. We
achieved substantial performance gains for all of them, which suggests that the proposed approach is generic and can serve
as a practical solution for boosting the performance of existing ConvNet-based HAR models. This is an open-source project,
and the code can be found at https://github.com/sshao2013/ConvBoost
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Human activity recognition , Deep learning (Machine learning) , Ensemble learning (Machine learning) , Diagnostic imaging -- Data processing , Detectors , Ubiquitous computing | ||||||
Journal or Publication Title: | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies | ||||||
Publisher: | ACM | ||||||
Official Date: | June 2023 | ||||||
Dates: |
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Volume: | 7 | ||||||
Number: | 2 | ||||||
Article Number: | 75 | ||||||
DOI: | 10.1145/3596234 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | In Press | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 23 May 2023 | ||||||
Date of first compliant Open Access: | 24 May 2023 | ||||||
Conference Paper Type: | Paper | ||||||
Type of Event: | Conference | ||||||
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