The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
A Function Approximator Model for Robust Online Foot Angle Trajectory Prediction Using a Single IMU Sensor: Implication for Controlling Active Prosthetic Feet
Open accessAbstract
Lower limb dysfunction hinders the locomotive activity in individuals and compels them to use assistive devices such as prostheses or orthoses to restore the missing locomotive functions. Passive assistive devices have limitations in replacing the lost functionality. On the other hand, active devices could restore more natural locomotion using motorized joints. To make proper use of these embedded motors, a control architecture is required to translate the motion intent of the user into the intended prosthesis joint trajectories. In this article, we propose a temporal convolution-based online foot angle trajectory prediction network (FATP-N) that predicts the foot angular positions during unconstrained walking from the shank angular position measured using a single wearable motion tracker sensor. The data acquisition experiments were designed to reflect natural gait with turns, varying inclines, speeds, and mixed cadences. The trained models were prepared for real-time prediction by compressing them to reduce the storage and time complexity. The validations were performed on different motion conditions, including those that have not been used for model training, to verify the robustness of the model. The proposed FATP-N achieved high accuracy and could generate predictions in real time. The results indicate that the proposed approach offers a possible architecture for real-time control of powered intelligent prostheses.
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“| TCN | Dilated causal convolutions, parallel temporal | Efficient long-sequence handling, low inference | Real-time gesture recognition, continuous |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [242]
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Sorted by citations
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
- Dey, S., & Schilling, A. F.
- Affiliations
- Universitätsmedizin Göttingen (DE)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2023-02
- Volume
- 19
- Issue
- 2
- Pages
- 1467-1475
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4226336393
- Cited by
- 27
- References
- 26
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 6 · 2023: 9
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion, cc-by)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, other-oa)
- Funders
- German Federal Ministry of Education and Research (INOPRO16SV7657)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Locomotion and Control
- Keywords
- lower limb prosthesis, temporal convolutional network, online prediction, real-time control, gait, model compression
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