Estimation of Time-Varying Ankle Joint Stiffness Under Dynamic Conditions via System Identification Techniques

Alejandro Moya Esteban,Guillaume Durandau,Ronald C Van 'T Veld,Alfred C Schouten,Massimo Sartori,Christopher P Cop

doi:10.1109/embc.2019.8856423

Alejandro Moya Esteban, Guillaume Durandau + Show 4 more

Open Access

https://doi.org/10.1109/embc.2019.8856423

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Publication Date: Jul 1, 2019
Citations: 19	License type: cc-by

Affiliation: University of Twente, Delft University of Technology

Abstract

An important goal in the design of next-generation exoskeletons and limb prostheses is to replicate human limb dynamics. Joint impedance determines the dynamic relation between joint displacement and torque. Joint stiffness is the position-dependent component of joint impedance and is key in postural control and movement. However, the mechanisms to modulate joint stiffness are not fully understood yet. The goal of this study is to conduct a systematic analysis on how humans modulate ankle stiffness. Time-varying stiffness was estimated for six healthy subjects under isometric, as well as quick and slow dynamic conditions via system identification techniques; specifically, an ensemble-based algorithm using short segments of ankle torque and position recordings. Our results show that stiffness had the lowest magnitude under quick dynamic conditions. Under isometric conditions, with fixed position and varying muscle activity, stiffness exhibited a higher magnitude. Finally, under slow dynamic conditions, stiffness was found to be the highest. Our results highlight, for the first time, the variability in stiffness modulation strategies across conditions, especially across movement velocity.

Highlights

Humans have the capacity to adapt the mechanical properties of their limbs to enable natural physical interaction even in complex environmental conditions, such as walking on ice
A forward shift between minimum stiffness peaks and zero-torque points is observed for slow dynamic condition
We found TV ankle stiffness during quick dynamic conditions to exhibit the lowest magnitude

Summary

Introduction

Humans have the capacity to adapt the mechanical properties of their limbs to enable natural physical interaction even in complex environmental conditions, such as walking on ice. A better understanding of these control strategies would contribute to improving the functionality of robotic devices such as biomimetic prostheses or exoskeletons, which cannot replicate human limb mechanical dynamics yet [1]. New insights into these neural control strategies would improve the assessment and management of neurological disorders, like stroke or spinal cord injury, which lead to abnormal limb mechanical properties [2]. Joint impedance determines the dynamic relation between joint displacement and torque, and comprises three main components: inertia, damping and stiffness. Joint stiffness is the position-dependent component of joint impedance and is key in postural control and movement. Replicating human joint stiffness adaptations poses a major challenge in the field of biomimetic robotics. Many studies focus on analyzing joint stiffness under static conditions at a certain operation point, i.e. constant joint position and muscle activation [3]

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