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<p>The significant prevalence of knee injuries in sports and physical activities emphasizes the need for appropriate rehabilitation strategies. This paper describes a unique knee rehabilitation robot designed to facilitate remote rehabilitation, reduce the stress on physical therapists, and promote patient-led rehabilitation. A portable, IOT-enabled knee rehabilitation robot was designed, with customizable speed settings and an Aruco marker-based navigation for accurate leg alignment. The robot has two modes: (1) Persistent Passive Flexion–Extension Mode, for continuous limb flexion and extension; and (2) Intent-Triggered Active Assistance Mode, which uses surface electromyography (sEMG) signals to detect user intent and help movement execution. Six healthy male subjects were chosen to test the device. Usability was assessed using the NASA Task Load Index. The robot reduced muscular activation in healthy individuals, pointing to potential benefits for patients recovering from knee injuries, surgeries, or strokes. Analysis of muscle activity across the monitored muscles at varying speeds confirmed the device's efficacy. Usability tests revealed excellent user experiences, emphasizing the robot's applicability in both hospital and domestic settings.</p>

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Enhancing knee rehabilitation through intent triggered active robotic assistance

  • 期刊论文

  • 发表日期：

    2025年12月15日

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  • Muhammad Umair Ahmad Khan,
  • Hashim Iqbal,
  • Rabbia Muneer,
  • Arsalan Ali,
  • Muhammad Faisal

摘要

The significant prevalence of knee injuries in sports and physical activities emphasizes the need for appropriate rehabilitation strategies. This paper describes a unique knee rehabilitation robot designed to facilitate remote rehabilitation, reduce the stress on physical therapists, and promote patient-led rehabilitation. A portable, IOT-enabled knee rehabilitation robot was designed, with customizable speed settings and an Aruco marker-based navigation for accurate leg alignment. The robot has two modes: (1) Persistent Passive Flexion–Extension Mode, for continuous limb flexion and extension; and (2) Intent-Triggered Active Assistance Mode, which uses surface electromyography (sEMG) signals to detect user intent and help movement execution. Six healthy male subjects were chosen to test the device. Usability was assessed using the NASA Task Load Index. The robot reduced muscular activation in healthy individuals, pointing to potential benefits for patients recovering from knee injuries, surgeries, or strokes. Analysis of muscle activity across the monitored muscles at varying speeds confirmed the device's efficacy. Usability tests revealed excellent user experiences, emphasizing the robot's applicability in both hospital and domestic settings.