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<p>This study presents a biotechnical rehabilitation system for improving motor recovery in post-stroke patients with lower extremity paresis. The system integrates virtual reality (VR)–based therapy, real-time muscle fatigue monitoring, and adaptive rehabilitation control to provide personalized treatment. A VR adaptation module modifies rehabilitation content based on patient-specific electrophysiological responses, while biofeedback-based control optimizes neuromuscular engagement. A mathematical model was used to predict rehabilitation outcomes and guide therapy progression. Forty post-stroke patients (aged 30–65&#xa0;years) participated in an experimental study. Patients were assigned to either an experimental group receiving personalized, adaptive VR therapy or a control group receiving standard VR content. Motor function was assessed using the Lower Extremity Functional Scale (LEFS), Functional Gait Scale (FGS), 10-m walk test (10mWT), and 6-min walk test (6minWT). The experimental group showed statistically significant improvements, including an 11% increase in LEFS scores (<i>p</i> &lt; 0.05) and a 10% improvement in functional mobility. These findings indicate that adaptive VR-based rehabilitation combined with real-time muscle fatigue monitoring enhances motor recovery, balance, and functional mobility in post-stroke patients.</p>

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Biotechnical rehabilitation system with virtual reality and feedback for motor recovery

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Sergey Filist,
  • Riad Taha Al-Kasasbeh,
  • Elena Valer’evna Petrunina,
  • Tigran Gevorkyan,
  • Osama M. Al- Habahbeh,
  • Olga Vladimirovna Shatalova,
  • Nikolay A. Korenevskiy,
  • Manafaddin Bashir Namazov,
  • Ahmad Telfah

摘要

This study presents a biotechnical rehabilitation system for improving motor recovery in post-stroke patients with lower extremity paresis. The system integrates virtual reality (VR)–based therapy, real-time muscle fatigue monitoring, and adaptive rehabilitation control to provide personalized treatment. A VR adaptation module modifies rehabilitation content based on patient-specific electrophysiological responses, while biofeedback-based control optimizes neuromuscular engagement. A mathematical model was used to predict rehabilitation outcomes and guide therapy progression. Forty post-stroke patients (aged 30–65 years) participated in an experimental study. Patients were assigned to either an experimental group receiving personalized, adaptive VR therapy or a control group receiving standard VR content. Motor function was assessed using the Lower Extremity Functional Scale (LEFS), Functional Gait Scale (FGS), 10-m walk test (10mWT), and 6-min walk test (6minWT). The experimental group showed statistically significant improvements, including an 11% increase in LEFS scores (p < 0.05) and a 10% improvement in functional mobility. These findings indicate that adaptive VR-based rehabilitation combined with real-time muscle fatigue monitoring enhances motor recovery, balance, and functional mobility in post-stroke patients.