Dynamical analysis and design of active orthoses for spinal cord injured subjects by aesthetic and energetic optimization

Abstract

The dynamic analysis and simulation of human gait using multibody dynamics techniques has been a major area of research in the last decades. Nevertheless, not much attention has been paid to the analysis and simulation of robotic-assisted gait. Simulation is a very powerful tool both for assisting the design stage of active rehabilitation robots, and predicting the subject-orthoses cooperation and the resulting aesthetic gait. This paper presents a parameter optimization approach that allows simulating gait motion patterns in the particular case of a subject with incomplete spinal cord injury (SCI) wearing active knee-ankle-foot orthoses at both legs. The subject is modelled as a planar multibody system actuated through the main lower limb muscle groups. A muscle force-sharing problem is solved to obtain optimal muscle activation patterns. Furthermore, denervation of muscle groups caused by the SCI is parameterized to account for different injury severities. The active orthoses are modelled as external devices attached to the legs, and their dynamic and performance parameters are taken from a real prototype. Numerical results using energetic and aesthetic objective functions, and considering different SCI severities are obtained. Detailed discussions are given related to the different motion and actuation patterns both from muscles and orthoses. The proposed methodology opens new perspectives towards the prediction of human-assisted gait, which can be very helpful for the design of new rehabilitation robots.

Comparison of different optimal control formulations for generating dynamically consistent crutch walking simulations using a torque-driven model

“…Computational modelling provides a promising option for improving the customisation of assistive device controllers through movement simulation [4][5][6][7]. However, to date, few studies have used these methods to aid in the design of real wearable systems [8].…”

Adaptation Strategies for Personalized Gait Neuroprosthetics

“…Neuromechanical simulations of such neuromuscular, biomechanical, and mechanical interactions can be used to optimize the performance of users, hardware design (e.g., reduce size, weight or energy consumption), and controller design (e.g., seamless motor/muscle actuation and appropriate sensory feedback) (Alonso et al, 2012 ; Crago et al, 2014 ; García-Vallejo et al, 2016 ; Uchida et al, 2016 ; Sreenivasa et al, 2017 ; Michaud et al, 2019 ; Sauder et al, 2019 ). Dynamic simulations of the system composed of the human and the neuroprosthesis can predict the combined human-neuroprosthesis response, allow for device and control customization to maximize walking ability, and improve our understanding of the interaction between human and device for new movement conditions.…”

“…Dynamic simulations of the system composed of the human and the neuroprosthesis can predict the combined human-neuroprosthesis response, allow for device and control customization to maximize walking ability, and improve our understanding of the interaction between human and device for new movement conditions. We advocate to extend computational neuromusculoskeletal models to encompass paralysis-related muscular constraints (Alonso et al, 2012 ; García-Vallejo et al, 2016 ) and auxiliary assistive devices (e.g., crutches/walker, orthoses, exoskeletons, etc.) (Febrer-Nafría et al, 2020 , 2021 ) to achieve subject-specific model-based optimization of the device and its control.…”

Evaluation of Optimal Control Approaches for Predicting Active Knee-Ankle-Foot-Orthosis Motion for Individuals With Spinal Cord Injury

“…In Zhang et al's study (Zhang et al, 2017 ), a method for real-time identification of exoskeleton control parameters that minimize the metabolic energy cost of human walking was developed, and it was found that optimized assistance patterns varied widely across participants, demonstrating the importance of customization. Other studies used musculoskeletal models to estimate the user's kinetic parameters to control in real-time an exoskeleton (Cardona et al, 2020 ) or to simulate assisted human motion for identifying design parameters of assistive devices (García-Vallejo et al, 2016 ; Ong et al, 2016 ; Uchida et al, 2016 ). Moreover, optimal control has recently been used to identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort (Sreenivasa et al, 2017 ), to predict subject-exoskeleton combined motion when lifting a box using a lower back exoskeleton (Millard et al, 2017 ), and to simulate a sit-to-stand transition using a lower limb exoskeleton (Serrancolí et al, 2019 ).…”