Semi-supervised convolutional neural networks for human activity recognition
Open accessAbstract
Labeled data used for training activity recognition classifiers are usually limited in terms of size and diversity. Thus, the learned model may not generalize well when used in real-world use cases. Semi-supervised learning augments labeled examples with unlabeled examples, often resulting in improved performance. However, the semi-supervised methods studied in the activity recognition literatures assume that feature engineering is already done. In this paper, we lift this assumption and present two semi-supervised methods based on convolutional neural networks (CNNs) to learn discriminative hidden features. Our semi-supervised CNNs learn from both labeled and unlabeled data while also performing feature learning on raw sensor data. In experiments on three real world datasets, we show that our CNNs outperform supervised methods and traditional semi-supervised learning methods by up to 18% in mean F1-score (Fm).
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Zeng et al. showed an 18% higher F1-score using the convolutional ladder network on the ActiTracker dataset.
“Zeng et al. presented two semi-supervised CNN methods that utilize unlabeled data during training: The convolutional encoder-decoder and the convolutional ladder network [290] and showed an 18% higher F1-score using the convolutional ladder network on the ActiTracker dataset.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [290]
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BackgroundDeep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
“[320] presented two semi-supervised CNN methods that utilizes unlabeled data during training: the convolutional encoder-decoder and the convolutional ladder”
MethodDeep Learning for Sensor-based Human Activity Recognition
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2022) · ACM Computing Surveys · doi:10.1145/3447744 · Cited by 690 · see citing works
“lötz et al. proposed to use autoencoders for unsupervised feature learning as an alternative to Principal Component Analysis (PCA) for activity recognition in ubiquitous computing. And the authors in [33, 47, 177] employed the variants of autoencoders such as stacked autoencoders [33], stacked denoising autoencoders […”
BackgroundMethodA Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition
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Self-citationDeep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
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“lötz et al. proposed to use autoencoders for unsupervised feature learning as an alternative to Principal Component Analysis (PCA) for activity recognition in ubiquitous computing. And the authors in [33, 47, 177] employed the variants of autoencoders such as stacked autoencoders [33], stacked denoising autoencoders […”
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“[30] propose the CNN encoder-decoder and CNN ladder for HAR in the semi-supervised setting.”
MethodSelf-citationFedHAR: Semi-Supervised Online Learning for Personalized Federated Human Activity Recognition
Yu, H., Chen, Z., Zhang, X., Chen, X., Zhuang, F., Xiong, H., & Cheng, X. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3136853 · Cited by 113 · see citing works
Cheng, D., Zhang, L., Bu, C., Wu, H., & Song, A. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110789 · Cited by 55 · see citing works
- Authors
- Zeng, M., Yu, T., Wang, X., Nguyen, L. T., Mengshoel, O. J., & Lane, I.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- 2017 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2017-12
- Pages
- 522-529
- OpenAlex
- W2963054573
- Cited by
- 85
- References
- 20
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 8 · 2023: 13 · 2022: 16 · 2021: 18 · 2020: 9 · 2019: 6 · 2018: 6
- FWCI
- 1.68
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- human activity recognition, semi-supervised learning, convolutional neural networks, representation learning, unlabeled data, raw sensor data, generalization
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