Fine-grained activity classification in assembly based on multi-visual modalities
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works
Temporal Convolutional Networks for Action Segmentation and Detection
Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.113 · Cited by 2,238 · cited 2× · see citing works
“We used the t-SNE (t-Distributed Stochastic Neighbor Embedding) method (Ullah et al., 2022) to visually compare the performance of our model with that in Lea et al. (2017); Simonyan & Zisserman, (2014b); Ma et al., (2021) on our dataset.”
InfluentialDeep 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 · cited 1× · see citing works
“The goal of activity recognition is to understand the nature of the work environment, which allows for a better understanding of how people perform their jobs and what they are operating at any given time (Ahn et al., 2023; Chen et al., 2021; Rude et al., 2018; Xiao et al., 2022).”
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
Singh, B., Marks, T. K., Jones, M., Tuzel, O., & Shao, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.216 · Cited by 461 · cited 1× · see citing works
“Singh et al. (Singh et al., 2016) presented a multi-stream bi-directional network for fine-grained activity detection.”
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314 · cited 1× · see citing works
“The goal of activity recognition is to understand the nature of the work environment, which allows for a better understanding of how people perform their jobs and what they are operating at any given time (Ahn et al., 2023; Chen et al., 2021; Rude et al., 2018; Xiao et al., 2022).”
Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning
Fu, Z., He, X., Wang, E., Huo, J., Huang, J., & Wu, D. (2021) · Sensors · doi:10.3390/s21030885 · Cited by 79 · see citing works
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- Authors
- Chen, H., Zendehdel, N., Leu, M. C., & Yin, Z.
- Affiliations
- Missouri University of Science and Technology (US)
- Stony Brook University (US)
- Corresponding author
- Chen, Haodong
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2023-06-08
- Volume
- 35
- Issue
- 5
- Pages
- 2215-2233
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W4380052665
- Cited by
- 47
- References
- 6
- Citations by year
- 2026: 13 · 2025: 30 · 2024: 4
- FWCI
- 4.27
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Intelligent Manufacturing (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (CMMI-1646162, CMMI-1954548)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- temporal feature extraction, recurrent neural networks, LSTM, GRU, activity prediction, two-stage network, smart factory, human activity recognition
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