Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders
Open accessPublished version of a preprint
Abstract
Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected from 734 patients with gait disorders is used. As the patient walks, kinematic data is collected during the gait session. This data is processed to generate the Gait Profile Score (GPS) for each gait cycle. Tracking potential GPS variations enables detecting changes in gait quality. In this regard, our work is driven by predicting such future variations. Two approaches were considered: signal-based and image-based. The signal-based one uses raw gait cycles, while the image-based one employs a two-dimensional Fast Fourier Transform (2D FFT) representation of gait cycles. Several architectures were developed, and the obtained Area Under the Curve (AUC) was above 0.72 for both approaches. To the best of our knowledge, our study is the first to apply neural networks for gait prediction tasks.
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
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?
Fast Fourier Transform (FFT) extracts periodicity and frequency components and is used for periodic motion pattern recognition.
“| Fast Fourier Transform (FFT) | Extract periodicity and frequency | Analyze frequency-domain features in | Periodic motion pattern recognition 255,256 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [255]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works
Deep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342 · cited 2× · see citing works
“The obtained results were compared to five state-of-the-art architectures 10 , including Fully Convolutional neural Network (FCN), Residual Network (ResNet), Encoder, Time Le-Net (t-LeNet), and Transformer.”
ResultMeasurement of lower extremity kinematics during level walking
Kadaba, M. P., Ramakrishnan, H. K., & Wootten, M. E. (1990) · Journal of Orthopaedic Research · doi:10.1002/jor.1100080310 · Cited by 3,216 · see citing works
The Gait Profile Score and Movement Analysis Profile
Baker, R. J., McGinley, J. L., Schwartz, M., et al. (2009) · Gait & Posture · doi:10.1016/j.gaitpost.2009.05.020 · Cited by 696 · see citing works
Gait disorders in adults and the elderly
Pirker, W., & Katzenschlager, R. (2016) · Wiener klinische Wochenschrift · doi:10.1007/s00508-016-1096-4 · Cited by 671 · see citing works
Binary cross entropy with deep learning technique for Image classification
Ruby, A. U. (2020) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2020/175942020 · Cited by 637 · see citing works
Balance and gait in the elderly: A contemporary review
Osoba, M., Rao, A. K., Agrawal, S. K., & Lalwani, A. K. (2019) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.252 · Cited by 506 · see citing works
Systematic review on wearable lower-limb exoskeletons for gait training in neuromuscular impairments
Fernández, A. R., Lobo-Prat, J., & Font-Llagunes, J. M. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00815-5 · Cited by 461 · see citing works
A Review on Aging, Sarcopenia, Falls, and Resistance Training in Community-Dwelling Older Adults
Rodrigues, F., Domingos, C., Monteiro, D., & Morouço, P. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19020874 · Cited by 362 · see citing works
The effectiveness of robotic-assisted gait training for paediatric gait disorders: systematic review
Lefmann, S. A., Russo, R. N., & Hillier, S. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-016-0214-x · Cited by 191 · see citing works
Measuring freezing of gait during daily-life: an open-source, wearable sensors approach
Mancini, M., Shah, V. V., Stuart, S., Curtze, C., Horak, F. B., Safarpour, D., & Nutt, J. G. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-00774-3 · Cited by 180 · see citing works
Yang, C.-L., Chen, Z.-X., & Yang, C.-Y. (2019) · Sensors · doi:10.3390/s20010168 · Cited by 163 · see citing works
Auvinet, B., Touzard, C., Montestruc, F., Delafond, A., & Goëb, V. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-017-0218-1 · Cited by 89 · see citing works
Bishnoi, A., & Hernandez, M. E. (2020) · Aging & Mental Health · doi:10.1080/13607863.2020.1802576 · Cited by 81 · see citing works
Barton, G., Hawken, M. B., Scott, M., et al. (2010) · Human Movement Science · doi:10.1016/j.humov.2010.06.003 · Cited by 61 · see citing works
Observational Gait Assessment Scales in Patients with Walking Disorders: Systematic Review
Ridao-Fernández, C., Pinto, E. P., & Chamorro-Moriana, G. (2019) · BioMed Research International · doi:10.1155/2019/2085039 · Cited by 46 · see citing works
Robinson, L. W., Clement, N. D., Fullarton, M. E., et al. (2015) · Gait & Posture · doi:10.1016/j.gaitpost.2015.01.022 · Cited by 32 · see citing works
Holmes, S. J., Mudge, A. J., Wojciechowski, E., et al. (2018) · Clinical Biomechanics · doi:10.1016/j.clinbiomech.2018.08.002 · Cited by 26 · see citing works
The gait profile score characterises walking performance impairments in young stroke survivors
Jarvis, H. L., Brown, S. J., Butterworth, C., et al. (2021) · Gait & Posture · doi:10.1016/j.gaitpost.2021.10.037 · Cited by 14 · see citing works
Experimental Methods for Studying the Contact Mechanics of Joints
Zdero, R., Brzozowski, P., & Schemitsch, E. H. (2023) · BioMed Research International · doi:10.1155/2023/4914082 · Cited by 8 · see citing works
Christian, J., Kröll, J., & Schwameder, H. (2017) · Gait & Posture · doi:10.1016/j.gaitpost.2017.04.007 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [1]Ataullah, A. H. M. & De Jesus, O. Gait Disturbances. in StatPearls (StatPearls Publishing, 2022). 32809445
- [20]Vaswani, A. et al. Attention is all you need. Adv. Neural Inform. Process. Syst. 30 (2017).
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 · see citing works
- Authors
- Ben Chaabane, N., Conze, P.-H., Lempereur, M., Quellec, G., Rémy-Néris, O., Brochard, S., Cochener, B., & Lamard, M.
- Affiliations
- Inserm (FR)
- ORCID
- Venue
- Scientific Reports
- Published
- 2023-12-28
- Volume
- 13
- Issue
- 1
- Pages
- 23099-23099
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10754876
- OpenAlex
- W4390336556
- Cited by
- 24
- References
- 22
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 8
- FWCI
- 1.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Followknee (ANR-17- RHUS-0005)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
- Keywords
- quantitative gait analysis, gait disturbance, Gait Profile Score, kinematic data, artificial neural networks, two-dimensional fast Fourier transform, gait cycle, gait database
- MeSH terms
- Gait Analysis, Artificial Intelligence, Biomechanical Phenomena, Fourier Analysis, Gait, Humans, Neural Networks, Computer
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
