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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“| 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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
(2010) · Cited by 14,134
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Bai, S., Kolter, J. Z., & Koltun, V. (2018) · arXiv · doi:10.48550/arxiv.1803.01271 · Cited by 4,655
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 1×
“While typical implementations of the TCN [22], [24] uses the same kernel size across layers with exponentially increasing dilations [23] to allow large receptive fields, we propose a threelevel stacked temporal convolutional network with a dilation factor, d, of one and kernel size, K growing with the level of the sta…”
MethodControl strategies for active lower extremity prosthetics and orthotics: a review
Tucker, M. R., Olivier, J., Pagel, A., Bleuler, H., Bouri, M., Lambercy, O., Millán, J. D. R., Riener, R., Vallery, H., & Gassert, R. (2015) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-12-1 · Cited by 1,047
Huang, H., Zhang, F., Hargrove, L. J., Dou, Z., Rogers, D. R., & Englehart, K. B. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2161671 · Cited by 542
Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis
Varol, H. A., Sup, F. C., & Goldfarb, M. T. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2034734 · Cited by 433
Temporal Convolutional Networks for the Advance Prediction of ENSO
Yan, J., Mu, L., Wang, L., Ranjan, R. K., & Zomaya, A. Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-65070-5 · Cited by 304
On weight initialization in deep neural networksPreprint
Kumar, S. K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.08863 · Cited by 156
Flagged first, then by citations
Research works printed but not linked: 1
- shuangfeng, 2020, Tensorflow lite: On-device machine learning framework, J Comput Res Develop, 57
Cited by
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
