ExoGait-MS: Learning Periodic Dynamics with Multi-Scale Graph Network for Exoskeleton Gait Recognition

2025-05-23

AI Summary

Standardized gait patterns in exoskeleton-based rehabilitation often cause user discomfort and safety risks, necessitating high-precision individual gait recognition for personalized control. To address the challenge of discriminating subtle inter-subject gait variations—such as cadence and stride length—under joint-coordinated actuation, this paper proposes a multi-scale global dense graph convolutional network that explicitly models cross-joint coordination patterns. Additionally, a nonlinear periodic dynamics learning module is introduced to jointly encode spatiotemporal gait characteristics. Evaluated on a newly established, high-reliability individual gait dataset, our method achieves 94.34% recognition accuracy, surpassing the state-of-the-art by 3.77%. This advancement significantly enhances exoskeleton personalization capability and improves rehabilitation safety.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: State Estimation
• Machine Learning: Multimodal Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Security and Privacy: Large-scale security measurements

Abstract

Current exoskeleton control methods often face challenges in delivering personalized treatment. Standardized walking gaits can lead to patient discomfort or even injury. Therefore, personalized gait is essential for the effectiveness of exoskeleton robots, as it directly impacts their adaptability, comfort, and rehabilitation outcomes for individual users. To enable personalized treatment in exoskeleton-assisted therapy and related applications, accurate recognition of personal gait is crucial for implementing tailored gait control. The key challenge in gait recognition lies in effectively capturing individual differences in subtle gait features caused by joint synergy, such as step frequency and step length. To tackle this issue, we propose a novel approach, which uses Multi-Scale Global Dense Graph Convolutional Networks (GCN) in the spatial domain to identify latent joint synergy patterns. Moreover, we propose a Gait Non-linear Periodic Dynamics Learning module to effectively capture the periodic characteristics of gait in the temporal domain. To support our individual gait recognition task, we have constructed a comprehensive gait dataset that ensures both completeness and reliability. Our experimental results demonstrate that our method achieves an impressive accuracy of 94.34% on this dataset, surpassing the current state-of-the-art (SOTA) by 3.77%. This advancement underscores the potential of our approach to enhance personalized gait control in exoskeleton-assisted therapy.

Problem

• Personalized exoskeleton gait recognition for adaptive control
• Capturing subtle individual gait differences in joint synergy
• Learning periodic gait dynamics for accurate rehabilitation outcomes

Innovation

• Multi-Scale Global Dense GCN for joint synergy
• Gait Non-linear Periodic Dynamics Learning module
• Comprehensive gait dataset for reliable recognition