High-level locomotion intent estimation from electromyography and body posture
File(s)
Published version
Author(s)
Hodossy, Balint K
Farina, Dario
Type
Journal Article
Abstract
Objective. Once we learn a reliable gait, we no longer have to consciously contract individual
muscles to walk, or think about the fine-grained low-level control of our joints. Instead, we mainly
make decisions on where we want to end up, at what pace and through which path. Estimating
this high-level (HL) intent may provide the necessary input to wearable robotic devices to adapt
to their user’s needs. We introduce a continuous representation of locomotion goals and investig ate how it may be estimated from muscle signals and body posture. Approach. This study investig ated methods to estimate a representation of HL locomotion intent, the horizontal walking path.
We collected full-body motion capture and bipolar surface electromyography data from 6 subjects
during non-steady-state gait. We trained temporal convolutional networks to causally predict the
walking path directly or parametrically with a critically damped trajectory model, using a mixture
of muscle and body posture signals. Main results. We achieved a mean trajectory estimation accur acy for a 1-second walking path corresponding to r
2 = 0.89 using a multimodal model. We simul taneously provided estimates for current and desired walking velocities as constrained by the walk ing path model, aiding interpretability of the estimator’s output. Significance. Our approach could
provide user interfacing in a subject-independent format for wearable robotic devices. Moreover,
this HL intent representation is flexible and able to be synthesized in virtual environments, where
it can serve as a surrogate for biosignals of simulated intent-driven robotics.
muscles to walk, or think about the fine-grained low-level control of our joints. Instead, we mainly
make decisions on where we want to end up, at what pace and through which path. Estimating
this high-level (HL) intent may provide the necessary input to wearable robotic devices to adapt
to their user’s needs. We introduce a continuous representation of locomotion goals and investig ate how it may be estimated from muscle signals and body posture. Approach. This study investig ated methods to estimate a representation of HL locomotion intent, the horizontal walking path.
We collected full-body motion capture and bipolar surface electromyography data from 6 subjects
during non-steady-state gait. We trained temporal convolutional networks to causally predict the
walking path directly or parametrically with a critically damped trajectory model, using a mixture
of muscle and body posture signals. Main results. We achieved a mean trajectory estimation accur acy for a 1-second walking path corresponding to r
2 = 0.89 using a multimodal model. We simul taneously provided estimates for current and desired walking velocities as constrained by the walk ing path model, aiding interpretability of the estimator’s output. Significance. Our approach could
provide user interfacing in a subject-independent format for wearable robotic devices. Moreover,
this HL intent representation is flexible and able to be synthesized in virtual environments, where
it can serve as a surrogate for biosignals of simulated intent-driven robotics.
Date Issued
2025-12-01
Date Acceptance
2025-12-04
Citation
Journal of Neural Engineering, 2025, 22 (6)
ISSN
1741-2560
Publisher
IOP Publishing
Journal / Book Title
Journal of Neural Engineering
Volume
22
Issue
6
Copyright Statement
© 2025 The Author(s). Published by IOP Publishing Ltd Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41343869
Subjects
AI and machine learning
convolutional networks
electromyography
human-machine interfacing
intent estimation
physics-informed machine learning
Humans
Electromyography
Posture
Male
Adult
Female
Locomotion
Young Adult
Muscle, Skeletal
Walking
Intention
Gait
Wearable Electronic Devices
Publication Status
Published
Coverage Spatial
England
Article Number
066038
Date Publish Online
2025-12-29