Producción docente

URI permanente para esta comunidadhttps://dspace.corhuila.edu.co/handle/123456789/43

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Examinando Producción docente por Autor "Rodríguez Serrezuela, Ruthber"

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    PublicaciónAcceso Abierto
    Design and Construction of a Four-Degree Freedom Robot with PID Controller
    Rodríguez Serrezuela, Ruthber (2019-01-01)
  • Miniatura
    PublicaciónAcceso Abierto
    Hybrid Convolutional Vision Transformer for Robust Low-Channel sEMG Hand Gesture Recognition: A Comparative Study with CNNs
    Rodríguez Serrezuela, Ruthber (2025-12-12)
    Hand gesture classification using surface electromyography (sEMG) is fundamental for prosthetic control and human–machine interaction. However, most existing studies focus on high-density recordings or large gesture sets, leaving limited evidence on performance in low-channel, reduced-gesture configurations. This study addresses this gap by comparing a classical convolutional neural network (CNN), inspired by Atzori’s design, with a Convolutional Vision Transformer (CViT) tailored for compact sEMG systems. Two datasets were evaluated: a proprietary Myo-based collection (10 subjects, 8 channels, six gestures) and a subset of NinaPro DB3 (11 transradial amputees, 12 channels, same gestures). Both models were trained using standardized preprocessing, segmentation, and balanced windowing procedures. Results show that the CNN performs robustly on homogeneous signals (Myo:94.2% accuracy) but exhibits increased variability in amputee recordings (NinaPro: 92.0%). In contrast, the CViT consistently matches or surpasses the CNN, reaching 96.6% accuracy on Myo and 94.2% on NinaPro. Statistical analyses confirm significant differences in the Myo dataset. The objective of this work is to determine whether hybrid CNN–ViT architectures provide superior robustness and generalization under low-channel sEMG conditions. Rather than proposing a new architecture, this study delivers the first systematic benchmark of CNN and CViT models across amputee and non-amputee subjects using short windows, heterogeneous signals, and identical protocols, highlighting their suitability for compact prosthetic–control systems.
  • Miniatura
    PublicaciónAcceso Abierto
    Supporting Technology for Frozen Shoulder Rehabilitation: A Randomized Controlled Trial
    Rodríguez Serrezuela, Ruthber (2026-06-07)
    Adhesive capsulitis (AC), commonly known as frozen shoulder, is an enigmatic and poorly defined shoulder disorder characterized by a painful, progressive, and disabling loss of active and passive mobility in the glenohumeral joint across multiple planes. To evaluate the effectiveness of robotic therapy in the rehabilitation of patients diagnosed with AC, a therapeutic intervention study was conducted involving 40 patients treated at the Department of Physical Medicine and Rehabilitation of the General Hospital “Dr. Juan Bruno Zayas Alfonso” in Santiago de Cuba. Patients were randomly assigned to one of two groups: robotic therapy (experimental) and regular therapy (control). The Constant–Murley (CM) scale for pain and shoulder range of motion, as well as the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire, were measured before and after treatment. All patients treated with robotic therapy showed improvement; 70% advanced to pain grade 3 (almost normal), five to grade 2 (slight omalgia), and one subject reached grade 4 (no pain). In the control group, however, only nine patients (45%) advanced to grade 3, and one achieved grade 4, while the rest experienced mild, moderate, or severe pain. At the conclusion of the study, the CM scale indicated excellent or good results in 20 patients, 12 of whom were in the robotic therapy group. In the experimental group, 18 patients (90%) had final DASH scores below 50%.