ArticleScientific reports2024
Identification and interpretation of gait analysis features and foot conditions by explainable AI.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Applications of Artificial Intelligence in Pediatric Orthopaedics.Indian journal of orthopaedics · 2026Review
- Quantitative comparison of explainable AI methods for interpreting deep learning-based classification of 3D gait kinematics.Scientific reports · 2026Article
- Article
- Explainable machine learning to predict prolonged post-operative opioid use in rotator cuff patients.BMC musculoskeletal disorders · 2025Article
- Scoping Review of Machine Learning Techniques in Marker-Based Clinical Gait Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Sensor-based technologies for motion analysis in sports injuries: a scoping review.BMC sports science, medicine & rehabilitation · 2025Article
- Deep Learning Unravels Differences Between Kinematic and Kinetic Gait Cycle Time Series from Two Control Samples of Healthy Children Assessed in Two Different Gait Laboratories.Sensors (Basel, Switzerland) · 2024Article
- Exploration and demonstration of explainable machine learning models in prosthetic rehabilitation-based gait analysis.PloS one · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Clinical gait analysis is a crucial step for identifying foot disorders and planning surgery. Automating this process is essential for efficiently assessing the substantial amount of gait data. In this study, we explored the potential of state-of-the-art machine learning (ML) and explainable artificial intelligence (XAI) algorithms to automate all various steps involved in gait analysis for six specific foot conditions. To address the complexity of gait data, we manually created new features, followed by recursive feature elimination using Support Vector Machines (SVM) and Random Forests (RF) to eliminate low-variance features. SVM, RF, K-nearest Neighbor (KNN), and Logistic Regression (LREGR) were compared for classification, with a Majority Voting (MV) model combining trained models. KNN and MV achieved mean balanced accuracy, recall, precision, and F1 score of 0.87. All models were interpreted using Local Interpretable Model-agnostic Explanation (LIME) method and the five most relevant features were identified for each foot condition. High success scores indicate a strong relationship between selected features and foot conditions, potentially indicating clinical relevance. The proposed ML pipeline, adaptable for other foot conditions, showcases its potential in aiding experts in foot condition identification and planning surgeries.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.