Analyzing Gait Adaptation with Hemiplegia Simulation Suits and Digital Twins

2025-09-05

AI Summary

To address safety risks associated with early-stage user testing in assistive and rehabilitation robotics, this study proposes an experimental framework integrating a hemiparesis-simulating suit with digital twin technology. Healthy participants wear a custom-designed suit to safely emulate impaired gait, while synchronized kinematic (Vicon), electromyographic (Delsys), and inertial data are acquired. Multimodal fusion and machine learning models characterize dynamic gait adaptation mechanisms and human–walker interaction patterns. The key innovation lies in the first closed-loop coupling of a physical simulation suit with a digital twin, enabling interpretable, dynamic representation of pathological gait. Experimental validation confirms that the suit significantly alters joint kinematics and muscle activation timing; a highly discriminative sensor feature set is identified, enabling robust detection of assistive device usage states and turning intent. This framework establishes a transferable, rapid-prototyping validation paradigm for rehabilitation robots.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Interaction Techniques and Devices

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

To advance the development of assistive and rehabilitation robots, it is essential to conduct experiments early in the design cycle. However, testing early prototypes directly with users can pose safety risks. To address this, we explore the use of condition-specific simulation suits worn by healthy participants in controlled environments as a means to study gait changes associated with various impairments and support rapid prototyping. This paper presents a study analyzing the impact of a hemiplegia simulation suit on gait. We collected biomechanical data using a Vicon motion capture system and Delsys Trigno EMG and IMU sensors under four walking conditions: with and without a rollator, and with and without the simulation suit. The gait data was integrated into a digital twin model, enabling machine learning analyses to detect the use of the simulation suit and rollator, identify turning behavior, and evaluate how the suit affects gait over time. Our findings show that the simulation suit significantly alters movement and muscle activation patterns, prompting users to compensate with more abrupt motions. We also identify key features and sensor modalities that are most informative for accurately capturing gait dynamics and modeling human-rollator interaction within the digital twin framework.

Problem

• Studying gait changes using hemiplegia simulation suits
• Testing assistive robots safely with digital twins
• Analyzing movement alterations from impairment simulations

Innovation

• Hemiplegia simulation suits for gait analysis
• Digital twin model integrating biomechanical data
• Machine learning detects suit and rollator use