Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion
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In this study, we developed an algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations. Electromyographic (EMG) signals recorded from gluteal and residual thigh muscles and ground reaction forces/moments measured from the prosthetic pylon were used as inputs to a phase-dependent pattern classifier for continuous locomotion-mode identification. The algorithm was evaluated using data collected from five patients with TF amputations. The results showed that neuromuscular-mechanical fusion outperformed methods that used only EMG signals or mechanical information. For continuous performance of one walking mode (i.e., static state), the interface based on neuromuscular-mechanical fusion and a support vector machine (SVM) algorithm produced 99% or higher accuracy in the stance phase and 95% accuracy in the swing phase for locomotion-mode recognition. During mode transitions, the fusion-based SVM method correctly recognized all transitions with a sufficient predication time. These promising results demonstrate the potential of the continuous locomotion-mode classifier based on neuromuscular-mechanical fusion for neural control of prosthetic legs.
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Lightweight classifiers (such as LDA, SVM) can avoid long training times caused by high-dimensional feature spaces and achieve low-latency real-time prediction.
“On the other hand, lightweight classifiers (such as LDA, SVM) can avoid long training times caused by high-dimensional feature spaces and achieve low-latency real-time prediction 140,142,143 , meeting the immediacy requirements of human-computer interaction.”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [143]
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- Authors
- Huang, H., Zhang, F., Hargrove, L. J., Dou, Z., Rogers, D. R., & Englehart, K. B.
- Affiliations
- University of Rhode Island (US)
- Shirley Ryan AbilityLab (US)
- University of New Brunswick (CA)
- Corresponding author
- He Huang
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2011-10
- Volume
- 58
- Issue
- 10
- Pages
- 2867-2875
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- PMC
- PMC3235670
- OpenAlex
- W2167221577
- Cited by
- 542
- References
- 32
- Citations by year
- 2026: 20 · 2025: 40 · 2024: 37 · 2023: 57 · 2022: 56 · 2021: 49 · 2020: 58 · 2019: 48 · 2018: 39 · 2017: 34 · 2016: 23 · 2015: 26 · 2014: 25 · 2013: 24 · 2012: 6
- FWCI
- 10.43
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21HD064968)
- 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
- transfemoral amputation, prosthetic leg, electromyography, ground reaction force, gait phases, stance and swing phases, mode switching, support vector machine, gluteal muscles
- MeSH terms
- Amputees, Artificial Limbs, Electromyography, Humans, Locomotion, Signal Processing, Computer-Assisted, Thigh, Muscle, Skeletal, Support Vector Machine
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