Gait Analysis

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Gait analysis studies how people and legged robots walk, using motion, forces, and timing to describe identity, impairment, function, and stability. Methods range from marker-based motion capture and force plates to camera-based pose estimation and wearable inertial sensors, paired with biomechanical models and sequence-learning methods to estimate gait events, joint motion, forces, or diagnostic patterns. Applications include assessment and monitoring of Parkinson’s disease, stroke, frailty, falls, and freezing of gait, as well as gait recognition, assistive-device control, and robot locomotion; evaluations use subject-held-out and cross-site tests alongside recognition benchmarks such as CASIA-B. Models can perform well in constrained tests, but robustness across people, clinics, sensors, and pathological gait remains unresolved, and markerless estimates—especially of forces—still need validation against instrumented measurements before clinical reliance.

Latest papers

75 papers9 papers in the last four weeks, down 44% on the four weeks before. 0.1% of all new papers.

GenGait: A Transformer-Based Model for Human Gait Anomaly Detection and Normative Twin Generation
Apr 2, 2026Elisa Motta, Marta Lorenzini, Clara Mouawad +3Gait Analysis Anomaly Localization
HRI2 Laboratory, Istituto Italiano di Tecnologia (IIT), Genoa, Italy · Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Rome, Italy · Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome, Italy
A Gait Foundation Model Predicts Multi-System Health Phenotypes from 3D Skeletal Motion
Mar 26, 2026Adam Gabet, Sarah Kohn, Guy Lutsker +11Gait Analysis Clinical Prediction
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. · Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel. · Newton VR LTD., Tel Aviv-Yafo, Israel +2
The Gait Signature of Frailty: Transfer Learning based Deep Gait Models for Scalable Frailty Assessment
Mar 25, 2026Laura McDaniel, Basudha Pal, Crystal Szczesny +5Gait Analysis Transfer Learning
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. · Division of Geriatrics and Gerontology Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. · Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. +3
PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis
Mar 15, 2026Mritula Chandrasekaran, Sanket Kachole, Jarek Francik +1Human Motion Generation Gait Analysis
Kingston University, Kingston Upon Thames, London, UK · Indiana University Health, Indianapolis, Indiana
Imitation Learning from Human Motion Alone Does Not Guarantee Biomechanically Plausible Gait Kinetics
Mar 12, 2026Xinyi Liu, Jangwhan Ahn, Edgar Lobaton +2Gait Analysis Imitation Learning
Joint Department of Biomedical Engineering, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA · Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA · School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA
BioGait-VLM: A Tri-Modal Vision-Language-Biomechanics Framework for Interpretable Clinical Gait Assessment
Mar 9, 2026Erdong Chen, Yuyang Ji, Jacob K. Greenberg +5Gait Analysis Medical VLMs
Department of Computer Science, Drexel University · Department of Neurological Surgery, Washington University · University of California, Berkeley
Topological descriptors of foot clearance gait dynamics improve differential diagnosis of Parkinsonism
Mar 6, 2026Jhonathan Barrios, Wolfram Erlhagen, Miguel F. Gago +2Parkinson's Disease Classification Differential Diagnosis
Centre of Mathematics (CMAT), School of Sciences, University of Minho, Portugal · Neurology Departmet, Hospital da Senhora da Oliveira, Portugal · ICVS, School of Medicine, University of Minho, Portugal +3
Evaluating Large Language Models for Gait Classification Using Text-Encoded Kinematic Waveforms
Mar 4, 2026Carlo Dindorf, Jonas Dully, Rebecca Keilhauer +2LLM Evaluation Gait Analysis
Department of Sports Science, University of Kaiserslautern-Landau (RPTU), Kaiserslautern, Germany · Fraunhofer Institute for Experimental Software Engineering IESE, Kaiserslautern, Germany · German Research Center of Artificial Intelligence, Kaiserslautern, Germany
Freezing of Gait Prediction using Proactive Agent that Learns from Selected Experience and DDQN Algorithm
Mar 4, 2026Septian Enggar Sukmana, Sang Won Bae, Tomohiro ShibataPrioritized Experience Replay Parkinson's Disease
Kyushu Institute of Technology · Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan · Stevens Institute of Technology +1
Biomechanically Accurate Gait Analysis: A 3d Human Reconstruction Framework for Markerless Estimation of Gait Parameters
Mar 3, 2026Akila Pemasiri, Ethan Goan, Glen Lichtwark +3Musculoskeletal Modeling Gait Analysis
Queensland University of Technology, Brisbane · Griffith University, Gold Coast
Biomechanical Comparisons Reveal Divergence of Human and Humanoid Gaits
Feb 25, 2026Luying Feng, Yaochu Jin, Hanze Hu +1Humanoid Robot Locomotion Gait Analysis
Zhejiang University, Hangzhou, China. · School of Engineering, Westlake University, Hangzhou, China. · Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.
Automating Timed Up and Go Phase Segmentation and Gait Analysis via the tugturn Markerless 3D Pipeline
Feb 24, 2026Abel Gonçalves Chinaglia, Guilherme Manna Cesar, Paulo Roberto Pereira SantiagoGait Analysis Markerless Motion Capture
Biomechanics and Motor Control Laboratory, School of Physical Education and Sport of Ribeirao Preto, University of Sao Paulo, Brazil · Graduate Program in Rehabilitation and Functional Performance, Ribeirao Preto Medical School, University of Sao Paulo, Brazil · Department of Physical Therapy, Brooks College of Health, University of North Florida, USA
Gait Asymmetry from Unilateral Weakness and Improvement With Ankle Assistance: a Reinforcement Learning based Simulation Study
Feb 21, 2026Yifei Yuan, Ghaith Androwis, Xianlian ZhouGait Analysis Ankle Exoskeleton Control
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA · Kessler Foundation, West Orange, NJ, 07052, USA
Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models
Mar 2, 2025Longbin Zhang, Zhizhang Li, Xinyi Fu +11Gait Analysis Long Short-Term Memory Networks
School of Artificial Intelligence and Robotics, Hunan University, Changsha, China · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore · Fourth Hospital of Changsha, Hunan Normal University, Changsha, China +2
Evidence for Phenotype-Driven Disparities in Freezing of Gait Detection and Approaches to Bias Mitigation
Jan 29, 2025Timothy Odonga, Jeanne M. Powell, Claire Bae +5Algorithmic Fairness Human Activity Recognition
Emory University · Georgia Institute of Technology

Apr 2, 2026J/K move · Enter open · S save

GenGait: A Transformer-Based Model for Human Gait Anomaly Detection and Normative Twin Generation

Elisa Motta, Marta Lorenzini, Clara Mouawad, Alberto Ranavolo+2

HRI2 Laboratory, Istituto Italiano di Tecnologia (IIT), Genoa, Italy · Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Rome, Italy · Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome, Italy

Gait Analysis Anomaly Localization Reconstruction-Based Anomaly Detection Anomaly Detection 3D Human Motion Reconstruction

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Gait analysis provides an objective characterization of locomotor function and is widely used to support diagnosis and rehabilitation monitoring across neurological and orthopedic disorders. Deep learning has been increasingly applied to this domain, yet most approaches rely on supervised classifiers trained on disease-labeled data, limiting generalization to heterogeneous pathological presentations. The methodological objective of this work is to develop a label-free framework for joint-level anomaly detection and kinematic correction based on a Transformer masked autoencoder trained exclusively on normative gait sequences from 150 adults, acquired with a markerless multi-camera motion-capture system. At inference, a two-pass procedure is applied to potentially pathological input sequences: first, it estimates joint inconsistency scores by occluding individual joints and measuring deviations from the learned normative prior. Then, it withholds the flagged joints from the encoder input and reconstructs the full skeleton from the remaining spatiotemporal context, yielding corrected kinematic trajectories at the flagged positions. The validation objective is to assess whether the framework preserves unseen normative gait and reduces angular deviation in simulated abnormal gait patterns. In this proof-of-concept evaluation, data from 10 held-out normative participants, who performed seven simulated abnormal gait patterns, showed a significant reduction in angular deviation across all analyzed joints with large effect sizes, and preservation of normative kinematics. The proposed approach enables interpretable, subject-specific localization of joints that are inconsistent with learned normative gait patterns and generation of an individualized normative reconstruction without requiring disease labels. Video is available at https://youtu.be/Rcm3jqR5pN4.