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Cited by 21Prosthetics and Rehabilitation RoboticsMuscle activation and electromyography studiesHuman Motion and Animation

The DOI matches its Crossref record; 28 of the 29 references it cites were found in a registry or an index.

Robust Sensor Fusion and Biomimetic Control of a Lower-Limb Exoskeleton With Multimodal Sensors

Arceo, Juan Carlos, Yu, Lingzhou, Bai, Shaoping·2025·IEEE Transactions on Automation Science and Engineering
DOI 10.1109/tase.2024.3421318
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Abstract

This article presents a systematic approach to robustly control a lower-limb exoskeleton in real-time using multimodal sensors. The control adopts two sensor bands that combine an array of force-sensitive resistors (FSR) and an inertial measurement unit (IMU) to measure both force myography (FMG) signals and limb motion. A robust sensor fusion algorithm that combines FMG and IMU signals with artificial neural networks is developed to accurately estimate the wearer’s hip and knee rotation angles. Moreover, a mathematical model of the lower-limb exoskeleton is calibrated and validated with real-time experimental data. Finally, a model-based controller is designed to track position references generated from the network through linear matrix inequalities. The biomimetic control algorithm is tested in simulation and a physical setup to show the effectiveness of the novel control method.Note to Practitioners—This work addresses the challenge of real-time control of lower-limb exoskeletons. Our approach to address the trajectory generation problem involves emulating the movements of a healthy limb. This is achieved through a robust sensor fusion algorithm to integrate data from two multimodal sensors, namely, FMG and IMU sensors, enabling a reliable estimation of the hip and knee’s angular positions. Signals are processed and neural networks are trained with exoskeleton encoder measurements as targets. Physical tests show both the accuracy and robustness of the control method. Potential applications of the new method include real-time gait analysis, control of upper limb exoskeletons, and study of the induction of neural plasticity.

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Crossref · 10.1109/tase.2024.3421318

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28 of 28 references verified0 citation errors · 0 not checkable

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Show all 29 references

Research works printed but not linked: 3

  1. Haykin, 1998, Neural Networks: A Comprehensive Foundation
  2. Heaton, 2008, Introduction to Neural Networks With Java
  3. 10.1007/978-1-4612-1768-8_11 · DOI

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Metadata

Authors
Arceo, J. C., Yu, L., & Bai, S.
Affiliations
Aalborg University (DK)
ORCID
Venue
IEEE Transactions on Automation Science and Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2025
Volume
22
Pages
5425-5435
ISSN
  • 1545-5955 (ISSN-L)
  • 1558-3783
DOI
10.1109/tase.2024.3421318
OpenAlex
W4400314290
Cited by
19
References
29
Citations by year
2026: 8 · 2025: 10
FWCI
2.09
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • IEEE Transactions on Automation Science and Engineering (publishedVersion)
  • VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
Funders
ALEXO project through Frode V
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Human Motion and Animation
Keywords
lower limb exoskeleton, sensor fusion, force myography, inertial measurement unit, multimodal sensors, biomimetic control, linear matrix inequalities, model-based control, trajectory tracking, gait analysis, neuroplasticity

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