Abstract

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.

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