ArticleScientific reports2019
Explaining the unique nature of individual gait patterns with deep learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
107 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Advances in gait research related to Alzheimer's disease.Frontiers in neurology · 2025Pooled it
- The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Deep Convolutional Neural Network-Based Hemiplegic Gait Detection Using an Inertial Sensor Located Freely in a Pocket.Sensors (Basel, Switzerland) · 2022Trial
- A least action principle for interceptive walking.Scientific reports · 2021Trial
- Clinical Reference Values for Dynamic Plantar Pressure Assessment in Healthy Young Adults: Sex Differences and Bilateral Symmetry.Life (Basel, Switzerland) · 2026Article
- Neuromechanical gait signatures reveal holistic biomechanical responses to walking speed modulation in stroke survivors.Scientific reports · 2026Article
- Physics-informed hierarchical transformer for wearable sensor-based gait fatigue assessment.Frontiers in public health · 2026Article
- A Structural Vibration-based Dataset for Human Gait Recognition.Scientific data · 2025Article
- NONAN GaitPrint: An IMU gait database of healthy middle-aged adults.Scientific data · 2025Article
- Interpretable prediction of knee joint loading during tennis serves based on GNN-GRU model and layer-wise relevance propagation.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2025Article
- The individuality of plantar pressure indices enables the reduction of descriptive gait data required for diagnostic purposes.Scientific reports · 2025Article
- Entropy, Irreversibility, and Time-Series Deep Learning of Kinematic and Kinetic Data for Gait Classification in Children with Cerebral Palsy, Idiopathic Toe Walking, and Hereditary Spastic Paraplegia.Sensors (Basel, Switzerland) · 2025Article
- Evolution of joint power across the lifespan during walking.Journal of neuroengineering and rehabilitation · 2025Article
- Scoping Review of Machine Learning Techniques in Marker-Based Clinical Gait Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Person Recognition via Gait: A Review of Covariate Impact and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Using Surface Topography to Visualize Spinal Motion During Gait-Examples of Possible Applications and All Tools for Open Science.Bioengineering (Basel, Switzerland) · 2025Article
- Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed.Scientific reports · 2025Article
- Explainable Siamese Neural Networks for Detection of High Fall Risk Older Adults in the Community Based on Gait Analysis.Journal of functional morphology and kinesiology · 2025Article
- Deep Temporal Clustering of Pathological Gait Patterns in Post-Stroke Patients Using Joint Angle Trajectories: A Cross-Sectional Study.Bioengineering (Basel, Switzerland) · 2025Article
47 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely providing information about what made them arrive at a particular prediction. This black box aspect of ML techniques can be problematic especially in medical diagnoses, so far hampering a clinical acceptance. The present paper studies the uniqueness of individual gait patterns in clinical biomechanics using DNNs. By attributing portions of the model predictions back to the input variables (ground reaction forces and full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique reliably demonstrates which variables at what time windows of the gait cycle are most relevant for the characterisation of gait patterns from a certain individual. By measuring the time-resolved contribution of each input variable to the prediction of ML techniques such as DNNs, our method describes the first general framework that enables to understand and interpret non-linear ML methods in (biomechanical) gait analysis and thereby supplies a powerful tool for analysis, diagnosis and treatment of human gait.
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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.