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Cited by 16Muscle activation and electromyography studiesProsthetics and Rehabilitation RoboticsEEG and Brain-Computer Interfaces

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

EMG-based prediction of step direction for a better control of lower limb wearable devices

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Anselmino, Eugenio, Mazzoni, Alberto, Micera, Silvestro·2024·Computer Methods and Programs in Biomedicine
DOI 10.1016/j.cmpb.2024.108305
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Abstract

BACKGROUND AND OBJECTIVES: Lower-limb wearable devices can significantly improve the quality of life of subjects suffering from debilitating conditions, such as amputations, neurodegenerative disorders, and stroke-related impairments. Current control approaches, limited to forward walking, fall short of replicating the complexity of human locomotion in complex environments, such as uneven terrains or crowded places. Here we propose a high-level controller based on two Support Vector Machines exploiting four surface electromyography (EMG) signals of the thigh muscles to detect the onset (Toe-off intention decoder) and the direction (Directional EMG decoder) of the upcoming step. METHODS AND MATERIALS: We validated a preliminary version of the approach by acquiring EMG signals from ten healthy subjects, performing steps in four directions (forward, backward, right, and left), in three different settings (ground-level walking, stairs, and ramps), and in both steady-state and static conditions. Both the Toe-off intention and Directional EMG decoders have been tested with a 5-fold cross-validation repeated five times, using linear and radial-basis-function kernels, and by changing the classification output timing, from 200 ms before to 50 ms after the toe-off. RESULTS: The Toe-off intention decoder reached a median accuracy of 83.34 % (interquartile range (IQR): 6.48) and specificity of 92.72 % (IQR: 3.62) in its radial-basis-function version, while the Directional EMG decoder's median accuracy ranged between 73.92 % (IQR: 5.8), 200 ms before the toe-off, to 92.91 % (IQR: 4.11), 50 ms after the toe-off, with the radial-basis-function kernel implementation. For both the Toe-off intention and Directional EMG decoders the radial-basis-function version achieved better performances than the linear one (Wilcoxon signed rank test, p < 0.05). CONCLUSIONS AND SIGNIFICANCE: The combination of the two decoders proved to be a promising solution to detect the step initiation and classify its direction, paving the way for wearable devices with a broader range of movements and more degrees of freedom, ultimately promoting usability in uncontrolled settings and better reactions to external perturbations. Additionally, the encumbrance of the setup is limited to the thigh of the leg of interest, which simplifies the implementation in compact devices, concurrently limiting the sensors worn by the subject.

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Metadata

Authors
Anselmino, E., Mazzoni, A., & Micera, S.
Affiliations
Scuola Superiore Sant'Anna (IT)
Corresponding author
Anselmino, Eugenio
ORCID
Venue
Computer Methods and Programs in Biomedicine
Publisher
Elsevier BV
Published
2024-09
Volume
254
Pages
108305
ISSN
0169-2607 (ISSN-L)
DOI
10.1016/j.cmpb.2024.108305
OpenAlex
W4399989365
Cited by
15
References
38
Citations by year
2026: 7 · 2025: 6 · 2024: 2
FWCI
1.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Computer Methods and Programs in Biomedicine (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
  • CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Keywords
surface electromyography, support vector machine, thigh muscle, gait control, high-level control, Gaussian kernel, assistive robotics, neuroprosthetics, amputation, stroke rehabilitation, neurodegenerative diseases, external disturbances
MeSH terms
Wearable Electronic Devices, Adult, Algorithms, Electromyography, Female, Humans, Male, Signal Processing, Computer-Assisted, Walking, Muscle, Skeletal, Lower Extremity, Young Adult, Support Vector Machine

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