Gait Analysis

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12 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Latest papers 42

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GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization
May 12, 2026Huiran Duan, Qian Zhou, Zhongliang Guo +4Gait Analysis Privacy-Preserving ML
City University of New York, USA · Wuhan University, China · University of Aberdeen, UK +3
Quantifying Rodda and Graham Gait Classification from 3D Markerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort
May 11, 2026Lauhitya Reddy, Seth Donahue, Jeremy Bauer +8Markerless Motion Capture Gait Analysis
Department of Biomedical Informatics, Emory University, Atlanta, GA, USA · Shriners Children’s, USA · The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
An Elastic Shape Variational Autoencoder for Skeleton Pose Trajectories
May 10, 2026Arafat Rahman, Shashwat Kumar, Laura E. Barnes +1Variational Autoencoders Skeleton-Based Action Recognition
Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA · Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA · Dept. of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
Observable Performance Does Not Fully Reflect Adaptive System Organization: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint
May 1, 2026Jacques Raynal, Pierre Slangen, Elsa Raynal +1Gait Analysis Parkinson's Disease
Laboratory of Bioengineering and Nanosciences (LBN), University of Montpellier, France · EuroMov Digital Health in Motion, University of Montpellier, IMT Mines Alès, Alès, France · Certified Sophrologist and Dental Assistant, Sensorimotor Practice, Montpellier, France +1
Gait Recognition via Deep Residual Networks and Multi-Branch Feature Fusion
Apr 30, 2026Yabo Luo, Xiaoyun Wang, Cunrong LiGait Analysis Gait Recognition
School of Mechanical and Electrical Engineering, Osh State University
GaitKD: A Universal Decoupled Distillation Framework for Efficient Gait Recognition
Apr 29, 2026Yuqi Li, Qian Zhou, Huiran Duan +5Gait Analysis Gait Recognition
The City University of New York · Wuhan University · Beijing Jiaotong University +2
Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation
Apr 27, 2026Eun Som Jeon, Jisoo Lee, Huisu Lim +3Gait Analysis Wearable Sensing
Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea · Geometric Media Lab, School of Computing and Augmented Intelligence, Arizona State University, Tempe, 85281, AZ, USA · School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, 82587, AZ, USA +1
LiDAR for Rehabilitation: A Comprehensive Survey of Applications, AI Techniques, and Future Directions
Apr 26, 2026Soumia Siyoucef, Najmeddine Dhieb, Hakim Ghazzai +3Gait Analysis
Computer, Electrical and Mathematical Sciences & Engineering (CEMSE) Division at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia · Valduce Hospital, Villa Beretta Rehabilitation Center, Costa Masnaga, Italy
MMGait: Towards Multi-Modal Gait Recognition
Apr 17, 2026Chenye Wang, Qingyuan Cai, Saihui Hou +2Cross-Modal Representation Learning Gait Analysis
School of Artificial Intelligence, Beijing Normal University
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
Human Motion Data Alone Does Not Guarantee Plausible Gait Biomechanics
Mar 12, 2026Xinyi Liu, Jangwhan Ahn, Edgar Lobaton +2Gait Analysis Imitation Learning
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
3rd Place Solution to Human Motion Challenges in Real-World and Clinical Settings (MoCha) @ECCV2026: Language-Aligned Motion Representations for Domain-Generalizable UPDRS-Gait Severity Estimation
Date pendingSoojie Kim, Muhammad Munsif, Minkyung Kim +1Representation Learning Medical Diagnosis
UNIST, South Korea

May 12, 2026, cs.CVJ/K move · Enter open · S save

GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization

Huiran Duan, Qian Zhou, Zhongliang Guo, Junhao Dong+3

City University of New York, USA · Wuhan University, China · University of Aberdeen, UK+3

Gait Analysis Privacy-Preserving ML

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Conventional gait de-identification methods often encounter an inherent trade-off: they either provide insufficient identity suppression or introduce spatiotemporal distortions that impede structure-sensitive downstream applications. We propose GaitProtector, an impersonation-driven gait de-identification framework that formulates privacy protection as a unified objective with two tightly coupled components: (i) obfuscation, which repels the protected gait from the source identity, and (ii) impersonation, which attracts it toward a selected target identity. The target identity serves as a semantic anchor that biases optimization toward structurally plausible gait patterns under the pretrained diffusion prior, helping preserve dominant body shape and motion dynamics. We instantiate this idea through a training-free diffusion latent optimization pipeline. Instead of retraining a generator for each dataset, we invert each input silhouette sequence into the latent trajectory of a pretrained 3D video diffusion model and iteratively optimize latent codes with a differentiable adversarial objective to synthesize protected gaits. Experiments on the CASIA-B dataset show that GaitProtector achieves a 56.7% impersonation success rate under black-box gait recognition and reduces Rank-1 identification accuracy from 89.6% to 15.0%, while maintaining favorable visual and temporal quality. We further evaluate downstream utility on the Scoliosis1K dataset, where diagnostic accuracy decreases only from 91.4% to 74.2%. To the best of our knowledge, this work is the first to leverage pretrained 3D diffusion priors in a training-free manner for silhouette-based gait de-identification.

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