Attention-fused residual transformer CNN for robust lower limb movement recognition
Detecting lower limb movements from surface electromyography (sEMG) signals has received more attention, because of its importance in prosthetic control, robotic applications and medical rehabilitation. sEMG signals offer a non-invasive and accurate method to recognize movement intent. Conventional...
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Taylor & Francis Group
2025-07-01
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| Series: | Automatika |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2025.2513734 |
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| author | A. Anitha D. Jeraldin Auxillia |
| author_facet | A. Anitha D. Jeraldin Auxillia |
| author_sort | A. Anitha |
| collection | DOAJ |
| description | Detecting lower limb movements from surface electromyography (sEMG) signals has received more attention, because of its importance in prosthetic control, robotic applications and medical rehabilitation. sEMG signals offer a non-invasive and accurate method to recognize movement intent. Conventional machine-learning approaches depend on manual feature extraction, which consumes more time and is susceptible to noise interference and class imbalance. To address these challenges, a new framework that combines an Attention-Fused Residual-Transformer Convolutional Neural Network (AF-RT-CNN) is proposed. Data augmentation is applied to create more samples for minority classes, addressing class imbalance problems and improving recognition reliability. The AF-RT-CNN architecture combines residual blocks, attention mechanism and Transformer Encoder aiding robust feature extraction, good generalization capability and pattern recognition. The proposed framework was evaluated across 11 healthy individuals and 11 patients with lower limb impairments, across three distinct categories of lower limb movements sit, stand and gait. The method achieved a remarkable accuracy of 99.35% for the healthy group and 99.54% for the pathological group. When combining both groups, the overall accuracy reached 98.74%. The results indicate the effectiveness of the proposed approach in rehabilitation and assistive technology for lower limb motor control. |
| format | Article |
| id | doaj-art-ce98a8efc6b74cb08aed2f15ef168af0 |
| institution | Kabale University |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-ce98a8efc6b74cb08aed2f15ef168af02025-08-20T03:33:11ZengTaylor & Francis GroupAutomatika0005-11441848-33802025-07-0166352854210.1080/00051144.2025.2513734Attention-fused residual transformer CNN for robust lower limb movement recognitionA. Anitha0D. Jeraldin Auxillia1Department of Electronics and Communication Engineering, St. Xavier’s Catholic College of Engineering, Nagercoil, Tamil Nadu, IndiaDepartment of Electronics and Communication Engineering, St. Xavier’s Catholic College of Engineering, Nagercoil, Tamil Nadu, IndiaDetecting lower limb movements from surface electromyography (sEMG) signals has received more attention, because of its importance in prosthetic control, robotic applications and medical rehabilitation. sEMG signals offer a non-invasive and accurate method to recognize movement intent. Conventional machine-learning approaches depend on manual feature extraction, which consumes more time and is susceptible to noise interference and class imbalance. To address these challenges, a new framework that combines an Attention-Fused Residual-Transformer Convolutional Neural Network (AF-RT-CNN) is proposed. Data augmentation is applied to create more samples for minority classes, addressing class imbalance problems and improving recognition reliability. The AF-RT-CNN architecture combines residual blocks, attention mechanism and Transformer Encoder aiding robust feature extraction, good generalization capability and pattern recognition. The proposed framework was evaluated across 11 healthy individuals and 11 patients with lower limb impairments, across three distinct categories of lower limb movements sit, stand and gait. The method achieved a remarkable accuracy of 99.35% for the healthy group and 99.54% for the pathological group. When combining both groups, the overall accuracy reached 98.74%. The results indicate the effectiveness of the proposed approach in rehabilitation and assistive technology for lower limb motor control.https://www.tandfonline.com/doi/10.1080/00051144.2025.2513734Lower limbsEMGdeep learningResNettransformer |
| spellingShingle | A. Anitha D. Jeraldin Auxillia Attention-fused residual transformer CNN for robust lower limb movement recognition Automatika Lower limb sEMG deep learning ResNet transformer |
| title | Attention-fused residual transformer CNN for robust lower limb movement recognition |
| title_full | Attention-fused residual transformer CNN for robust lower limb movement recognition |
| title_fullStr | Attention-fused residual transformer CNN for robust lower limb movement recognition |
| title_full_unstemmed | Attention-fused residual transformer CNN for robust lower limb movement recognition |
| title_short | Attention-fused residual transformer CNN for robust lower limb movement recognition |
| title_sort | attention fused residual transformer cnn for robust lower limb movement recognition |
| topic | Lower limb sEMG deep learning ResNet transformer |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2025.2513734 |
| work_keys_str_mv | AT aanitha attentionfusedresidualtransformercnnforrobustlowerlimbmovementrecognition AT djeraldinauxillia attentionfusedresidualtransformercnnforrobustlowerlimbmovementrecognition |