Development of an EMG-Based Movement Intention Recognition Platform for Lower-Limb Exoskeletons.
Background/Objectives: Lower-limb exoskeletons require reliable movement recognition mechanisms to support adaptive locomotor assistance and rehabilitation. Electromyographic (EMG) signals provide valuable information on muscle activation and user intention, enabling safe and responsive human–exoske...
| Publicado en: | Prosthesis (2673-1592) Vol. 8; no. 7; pp. 74 - 96 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Jul2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195806958&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195806958 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26731592 MVQF jtl: Prosthesis (2673-1592) issn: 26731592 maglogo: N pubinfo: dt: Jul2026 vid: 8 iid: 7 pid: 97109 pub: MDPI artinfo: ui: 195806958 195806958 195806958 10.3390/prosthesis8070074 195806958 ppf: 74 ppct: 22 formats: tig: atl: Development of an EMG-Based Movement Intention Recognition Platform for Lower-Limb Exoskeletons. aug: au: Sava, Lilia Dunai, Larisa Tirsu, Valentina Dorogan, Andrei Turcanu, Dinu Manin, Nelea Ilev, Alexandru affil: Department of Telecommunications and Electronic Systems, Faculty of Electronics and Telecommunications, Technical University of Moldova, 2004 Chisinau, Moldova sug: subj: Electromyography Movement Lower Extremity Physiology Exoskeleton Devices Artificial Intelligence Convolutional Neural Networks Human Funding Source Kneeling Step Young Adult Conceptual Framework Neuromuscular Control Moldova Male Data Analysis Software Descriptive Statistics Rehabilitation Prostheses and Implants Locomotion Male ab: Background/Objectives: Lower-limb exoskeletons require reliable movement recognition mechanisms to support adaptive locomotor assistance and rehabilitation. Electromyographic (EMG) signals provide valuable information on muscle activation and user intention, enabling safe and responsive human–exoskeleton interaction. This study aims to develop and experimentally validate an EMG-based platform for intelligent lower-limb movement recognition and locomotor assistance applications. Methods: The proposed platform integrates multichannel EMG acquisition, embedded signal processing, and artificial intelligence for movement classification. EMG signals associated with six movement classes (left/right kneeling, stepping, and dash) were acquired from ten healthy male participants aged 19–24 years. Signal preprocessing, normalization, dataset generation, and model training were performed using a dedicated processing framework. Continuous EMG acquisition without threshold-based segmentation was employed to preserve complete neuromuscular information and improve dataset consistency. Movement classification was implemented using a lightweight one-dimensional convolutional neural network (1D-CNN). Model performance was evaluated using Stratified 5-Fold Cross-Validation and Leave-One-Subject-Out (LOSO) protocols. Results: A dataset containing 608 multichannel EMG recordings was generated for training and validation. The proposed 1D-CNN model achieved an accuracy of 92.43 ± 1.69% and a macro F1-score of 0.9093 ± 0.0247 under Stratified 5-Fold Cross-Validation. LOSO evaluation yielded an accuracy of 62.11 ± 23.26%, highlighting the significant impact of inter-subject variability on classification performance. Conclusions: The developed platform provides an effective framework for EMG-based lower-limb movement recognition in intelligent exoskeleton systems. The results demonstrate the feasibility of integrating multichannel EMG sensing and AI-based inference into adaptive locomotor assistance systems while emphasizing the importance of improving subject-independent generalization. The proposed platform also establishes a foundation for future research on multimodal sensing and real-time adaptive exoskeleton control. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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