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Use of a Foot-Induced Digitally Controlled Resistance Device for Functional Magnetic Resonance Imaging Evaluation in Patients with Foot Paresis
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Surface EMG-Validated Multi-DoF Wheelchair-Based Rehabilitation Device.

Jagan P1, Madhav Rao1

  • 1Electronics & Communication Department, International Institute of Information Technology-Bangalore, Bengaluru 560100, India.

Bioengineering (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an IoT-enabled robotic system for limb rehabilitation, utilizing electromyography (EMG) signals to personalize patient recovery. The assistive device supports both upper and lower limbs for individuals with paralysis.

Keywords:
Degrees of Freedom (DoF)actuatorsjoint anglepost-strokerehabilitationsurface EMGupper and lower limb

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Internet of Things (IoT)

Background:

  • Rehabilitation is crucial for motor function recovery after neurological injury.
  • Current robotic systems often focus on single limb types or have limited motion capabilities.
  • Therapist-assisted rehabilitation can be resource-intensive and may not suit all recovery phases.

Purpose of the Study:

  • To engineer an integrated, IoT-enabled robotic system for both upper and lower limb rehabilitation.
  • To incorporate electromyography (EMG) signals for enhanced movement characterization and personalization.
  • To provide a versatile assistive solution for patients with hemiplegia, paraplegia, and tetraplegia.

Main Methods:

  • Development of a wheelchair-based robotic assistive system for simultaneous upper and lower limb movement.
  • Integration of a novel electromyography (EMG) signal processing module for movement analysis.
  • Implementation of an IoT framework for remote monitoring and control, enabling home-based rehabilitation.

Main Results:

  • Successful development and prototyping of the dual-limb assistive robotic system.
  • Demonstration of EMG signal utilization for characterizing diverse muscle movements.
  • Validation of the system's potential for personalized and accessible rehabilitation.

Conclusions:

  • The developed system offers a novel, integrated approach to robotic limb rehabilitation.
  • IoT enablement and EMG integration enhance personalization and remote accessibility.
  • This technology holds promise for improving recovery outcomes for patients with various forms of paralysis.