Gait Analysis and Biomechanical Modelling of Walking with a Reciprocating Gait Orthosis
Towards Improved Exoskeleton-Assisted Walking for Individuals with Spinal Cord Injury
A.L. Zegwaard (TU Delft - Mechanical Engineering)
Rutger Osterthun – Mentor
G. Smit – Mentor (TU Delft - Medical Instruments & Bio-Inspired Technology)
S. Hörmann – Mentor (TU Delft - Biomechatronics & Human-Machine Control)
J.J. van den Dobbelsteen – Graduation committee member (TU Delft - Medical Instruments & Bio-Inspired Technology)
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Abstract
Introduction
In individuals with spinal cord injury (SCI), wheelchair use is the primary means of mobility and is often associated with a sedentary lifestyle and an increased risk of secondary health complications. Although increasing physical activity can mitigate some of these effects, achieving sufficient activity levels remains challenging. Upright standing and walking remain important rehabilitation goals for individuals with SCI and may contribute to increased physical activity and improved body image.
Various exoskeletal systems have been developed to enable upright ambulation. However, many powered devices rely on heavy and bulky actuators, limiting their practicality in daily life. Passive orthotic devices, such as the (Advanced) Reciprocating Gait Orthosis ((A)RGO), allow walking with crutches but are associated with high physical effort and substantial upper-body demands. The Cloudwalker project aims to make exoskeleton-assisted ambulation accessible to a broader population of individuals with SCI by reducing the effort required during RGO-assisted walking. This study supports its development by characterising ARGO Walker–assisted gait and developing a simplified biomechanical model to enable future simulation-based evaluation of assistive strategies and design choices aimed at reducing walking effort.
Aim
The aim of this study was to characterise ARGO Walker–assisted gait in terms of joint kinematics and kinetics, and to use these insights to develop an initial simplified biomechanical model of ARGO-assisted gait in OpenSim.
Methods
A healthy participant performed ARGO Walker–assisted walking trials using crutches while motion capture and ground reaction forces from both feet and crutches were recorded. Joint angles and joint moments were computed in OpenSim using inverse kinematics and inverse dynamics. In addition, a simplified ARGO Walker model was developed in OpenSim Creator and qualitatively evaluated by prescribing experimental motion and visually inspecting human–exoskeleton alignment.
Results
ARGO Walker–assisted walking was substantially slower than healthy gait, with a mean walking speed of 0.34 m/s and a stance-dominated gait cycle (66.9% stance, 33.1% swing). Kinematic analysis revealed clear effects of the mechanical constraints imposed by the device across multiple joints, with particularly a pronounced posterior pelvic tilt and increased lumbar flexion throughout the gait cycle. Joint kinetic analysis showed markedly increased net joint moments compared with healthy reference data, particularly for hip flexion, knee extension, and lumbar extension moments.
The simplified ARGO Walker model moved largely in synchrony with the human body during walking. However, remaining misalignments between the human and exoskeleton indicate that further refinement is required before simulation-based analyses can be performed.
Conclusion
ARGO Walker–assisted gait differs substantially from healthy walking due to the mechanical constraints of the device, resulting in restricted distal joint motion and increased reliance on proximal joints and crutch support. This walking strategy is associated with elevated joint moments, particularly at the lumbar region, indicating increased mechanical demands. The developed simplified OpenSim model provides an initial framework for future simulation-based investigations of assistive strategies and design adaptations, but requires further refinement before it can be used to reliably evaluate approaches aimed at improving accessibility of ARGO-assisted walking.