The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
A Transformer-Based Neural Network for Gait Prediction in Lower Limb Exoskeleton Robots Using Plantar Force
Open accessAbstract
Lower limb exoskeleton robots have shown significant research value due to their capabilities of providing assistance to wearers and improving physical motion functions. As a type of robotic technology, wearable robots are directly in contact with the wearer’s limbs during operation, necessitating a high level of human–robot collaboration to ensure safety and efficacy. Furthermore, gait prediction for the wearer, which helps to compensate for sensor delays and provide references for controller design, is crucial for improving the the human–robot collaboration capability. For gait prediction, the plantar force intrinsically reflects crucial gait patterns regardless of individual differences. To be exact, the plantar force encompasses a doubled three-axis force, which varies over time concerning the two feet, which also reflects the gait patterns indistinctly. In this paper, we developed a transformer-based neural network (TFSformer) comprising convolution and variational mode decomposition (VMD) to predict bilateral hip and knee joint angles utilizing the plantar pressure. Given the distinct information contained in the temporal and the force-space dimensions of plantar pressure, the encoder uses 1D convolution to obtain the integrated features in the two dimensions. As for the decoder, it utilizes a multi-channel attention mechanism to simultaneously focus on both dimensions and a deep multi-channel attention structure to reduce the computational and memory consumption. Furthermore, VMD is applied to networks to better distinguish the trends and changes in data. The model is trained and tested on a self-constructed dataset that consists of data from 35 volunteers. The experimental results show that FTSformer reduces the mean absolute error (MAE) up to 10.83%, 15.04% and 8.05% and the mean squared error (MSE) by 20.40%, 29.90% and 12.60% compared to the CNN model, the transformer model and the CNN transformer model, respectively.
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![Figure 6 Convolutional diagram of the encoder module. C1 represents the one-dimensional convolutional operation performed along the temporal dimension and C2 denotes the one-dimensional convolutional operation performed along the force-space dimension. [t,t1,t2,t3] and [f,f1,f2,f3] represent the lengths of the temporal and force-space dimensions, respectively. In our research, t, t1, t2 and t3 are set as 32, 64, 128 and 256, respectively, and f,f1,f2 and f3 are designed as 6, 36, 108 and 256 in turn.](/paper-figures/04ed8c01449f40e6949de316cd570f95/a-transformer-based-neural-network-for-gait-prediction-in-lower-limb-exoskeleton-figure-6.jpg)
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“| Transformer | Global modeling via self-attention | Adaptive multimodal fusion, strong | Cross-user intent transfer learning, multi-sensor |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [152]
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Sorted by citations
Deep Learning Approaches for Enhanced Lower-Limb Exoskeleton Control: A Review
Belal, M., Alsheikh, N., Aljarah, A., & Hussain, I. (2024) · IEEE Access · doi:10.1109/access.2024.3414175 · Cited by 42
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
- Ren, J., Wang, A., Li, H., Yue, X., & Meng, L.
- Affiliations
- Zhongyuan University of Technology (CN)
- Ritsumeikan University (JP)
- Corresponding author
- Wang, Aihui
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-07-20
- Volume
- 23
- Issue
- 14
- Pages
- 6547
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10384092
- OpenAlex
- W4384931412
- Cited by
- 34
- References
- 35
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 9 · 2023: 1
- FWCI
- 2.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (62073297)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- lower limb exoskeleton, gait prediction, plantar force, plantar pressure, transformer models, variational mode decomposition, hip joint angle, knee joint angle, multi-channel attention, wearable robotics, human-robot collaboration
- MeSH terms
- Exoskeleton Device, Gait, Humans, Robotics, Neural Networks, Computer, Lower Extremity
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