TY - JOUR
T1 - Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke
T2 - a feasibility study
AU - De Miguel-Fernández, Jesús
AU - Salazar-Del Rio, Miguel
AU - Rey-Prieto, Marta
AU - Bayón, Cristina
AU - Guirao-Cano, Lluis
AU - Font-Llagunes, Josep M.
AU - Lobo-Prat, Joan
N1 - Funding Information:
This work was supported by grant No. 2020 FI_B 00331 funded by the Agency for Management of University and Research Grants (AGAUR) along with the Secretariat of Universities and Research of the Catalan Ministry of Research and Universities and the European Social Fund (ESF), grant No. 2021 SGR 01052 funded by the Agency for Management of University and Research Grants (AGAUR) and the Catalan Ministry of Research and Universities, and grant PTQ2018-010227 funded by the Spanish Ministry of Science and Innovation (MCI)–Agencia Estatal de Investigación (AEI). The project that gave rise to these results has received funding from the “la Caixa” Foundation under the grant agreement LCF/TR/CC20/52480002.
Publisher Copyright:
Copyright © 2023 De Miguel-Fernández, Salazar-Del Rio, Rey-Prieto, Bayón, Guirao-Cano, Font-Llagunes and Lobo-Prat.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - exoskeleton
KW - gait analysis
KW - gait assessment
KW - IMU
KW - inertial sensors
KW - rehabilitation
KW - stroke
KW - wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85171864703&partnerID=8YFLogxK
U2 - 10.3389/fbioe.2023.1208561
DO - 10.3389/fbioe.2023.1208561
M3 - Article
AN - SCOPUS:85171864703
SN - 2296-4185
VL - 11
JO - Frontiers in bioengineering and biotechnology
JF - Frontiers in bioengineering and biotechnology
M1 - 1208561
ER -