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Gait Neural Network for Human-Exoskeleton Interaction
Open accessAbstract
Robotic exoskeletons are developed with the aim of enhancing convenience and physical possibilities in daily life. However, at present, these devices lack sufficient synchronization with human movements. To optimize human-exoskeleton interaction, this article proposes a gait recognition and prediction model, called the gait neural network (GNN), which is based on the temporal convolutional network. It consists of an intermediate network, a target network, and a recognition and prediction model. The novel structure of the algorithm can make full use of the historical information from sensors. The performance of the GNN is evaluated based on the publicly available HuGaDB dataset, as well as on data collected by an inertial-based wearable motion capture device. The results show that the proposed approach is highly effective and achieves superior performance compared with existing methods.
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GNN models sensor topological relationships via graph structures, provides explicit spatial correlation inference, and is used for whole-body motion intent network analysis.
“| GNN | Model sensor topological relationships via graph | Explicit spatial correlation inference | Whole-body motion intent network analysis 270-272 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [272]
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Flagged first, then by citations
Research works printed but not linked: 2
- Bai, 2018, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
- Romero-Hernandez, 2019, “Deep learning prediction of gait based on inertial measurements,”, 284
Cited by
Sorted by citations
Dey, S., & Schilling, A. F. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3158935 · Cited by 27
Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9
Metadata
- Authors
- Fang, B., Zhou, Q., Sun, F., Shan, J., Wang, M., Xiang, C., & Zhang, Q.
- Affiliations
- Beijing National Research Center for Information Science and Technology (CN)
- Anhui University of Technology (CN)
- Iowa State University (US)
- State Key Laboratory of Digital Manufacturing Equipment and Technology (CN)
- Corresponding author
- Sun, Fuchun
- Shan, Jianhua
- Venue
- Frontiers in Neurorobotics
- Publisher
- Frontiers Media SA
- Published
- 2020-10-29
- Volume
- 14
- Pages
- 58-58
- ISSN
- 1662-5218 (ISSN-L)
- PMC
- PMC7658381
- OpenAlex
- W3095743069
- Cited by
- 39
- References
- 21
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2023: 14 · 2022: 8 · 2021: 4
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Neurorobotics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2017YFB1302302)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- robotic exoskeleton, human-exoskeleton interaction, gait recognition, gait prediction, temporal convolutional network, inertial sensors, wearable motion capture
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