Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study

Frontiers in Bioengineering and Biotechnology

Bioengineering and Biotechnology

Joan Lobo-Prat¹  Marta Rey-Prieto²  Jesús De Miguel-Fernández²  Miguel Salazar-Del Rio²  Josep M. Font-Llagunes²  Cristina Bayón³  Lluis Guirao-Cano⁴ 

[1] ABLE Human Motion, Barcelona, Spain;Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain;Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain;Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands;Hospital Universitari Múttua de Terrassa, Barcelona, Spain;

关键词: stroke;    wearable sensors;    inertial sensors;    IMU;    gait analysis;    gait assessment;    rehabilitation;    exoskeleton;   

DOI  :  10.3389/fbioe.2023.1208561

 received in 2023-04-19, accepted in 2023-08-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: Tuning the control parameters is one of the main challenges in robotic gait therapy. Control strategies that vary the control parameters based on the user’s performance are still scarce and do not exploit the potential of using spatiotemporal metrics. The goal of this study was to validate the feasibility of using shank-worn Inertial Measurement Units (IMUs) for clinical gait analysis after stroke and evaluate their preliminary applicability in designing an automatic and adaptive controller for a knee exoskeleton (ABLE-KS).Methods: First, we estimated the temporal (i.e., stride time, stance, and swing duration) and spatial (i.e., stride length, maximum vertical displacement, foot clearance, and circumduction) metrics in six post-stroke participants while walking on a treadmill and overground and compared these estimates with data from an optical motion tracking system. Next, we analyzed the relationships between the IMU-estimated metrics and an exoskeleton control parameter related to the peak knee flexion torque. Finally, we trained two machine learning algorithms, i.e., linear regression and neural network, to model the relationship between the exoskeleton torque and maximum vertical displacement, which was the metric that showed the strongest correlations with the data from the optical system [r = 0.84; ICC(A,1) = 0.73; ICC(C,1) = 0.81] and peak knee flexion torque (r = 0.957).Results: Offline validation of both neural network and linear regression models showed good predictions (R2 = 0.70–0.80; MAE = 0.48–0.58 Nm) of the peak torque based on the maximum vertical displacement metric for the participants with better gait function, i.e., gait speed > 0.7 m/s. For the participants with worse gait function, both models failed to provide good predictions (R2 = 0.00–0.19; MAE = 1.15–1.29 Nm) of the peak torque despite having a moderate-to-strong correlation between the spatiotemporal metric and control parameter.Discussion: Our preliminary results indicate that the stride-by-stride estimations of shank-worn IMUs show potential to design automatic and adaptive exoskeleton control strategies for people with moderate impairments in gait function due to stroke.

【 授权许可】

Unknown   
Copyright © 2023 De Miguel-Fernández, Salazar-Del Rio, Rey-Prieto, Bayón, Guirao-Cano, Font-Llagunes and Lobo-Prat.