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<p>To address the issues of prolonged rehabilitation and unstable outcomes following total knee arthroplasty, a portable lower limb rehabilitation exoskeleton suitable for seated and supine postures was developed. A musculoskeletal model was constructed, and inverse dynamics analysis of the knee joint was performed to provide mechanical support for actuator design. An inertial measurement unit (IMU)-based joint angle estimation method was proposed by establishing a human-machine pose mapping model and fusing angular velocity integration with gravity projection. The estimation accuracy was evaluated using vision-based motion tracking, and the optimized IMU layout achieved a root-mean-square error (RMSE) of 0.62° ± 0.02°. Furthermore, a dual-strategy control system integrating pressure anomaly detection and angle compensation was introduced, incorporating IMUs, encoders, and pressure sensors to enable real-time human-machine interaction. Experimental results obtained from 10 subjects with varying body profiles and motion speeds (2–4 r/min) demonstrated that the proposed angle compensation algorithm maintained the dynamic tracking deviation within 1.2°, while the pressure monitoring mechanism ensured rapid safety response to contact anomalies. These findings confirm that the proposed system offers high tracking accuracy and control stability, making it suitable for postoperative rehabilitation training.</p>

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Portable lower limb rehabilitation exoskeleton design and human–machine interaction strategies for rehabilitation

  • 期刊论文

  • 发表日期：

    2026年7月9日

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  • Rixi Huang,
  • Ao Lan,
  • Bingwei He,
  • Yue Zhang,
  • Xinyuan Chen

摘要

To address the issues of prolonged rehabilitation and unstable outcomes following total knee arthroplasty, a portable lower limb rehabilitation exoskeleton suitable for seated and supine postures was developed. A musculoskeletal model was constructed, and inverse dynamics analysis of the knee joint was performed to provide mechanical support for actuator design. An inertial measurement unit (IMU)-based joint angle estimation method was proposed by establishing a human-machine pose mapping model and fusing angular velocity integration with gravity projection. The estimation accuracy was evaluated using vision-based motion tracking, and the optimized IMU layout achieved a root-mean-square error (RMSE) of 0.62° ± 0.02°. Furthermore, a dual-strategy control system integrating pressure anomaly detection and angle compensation was introduced, incorporating IMUs, encoders, and pressure sensors to enable real-time human-machine interaction. Experimental results obtained from 10 subjects with varying body profiles and motion speeds (2–4 r/min) demonstrated that the proposed angle compensation algorithm maintained the dynamic tracking deviation within 1.2°, while the pressure monitoring mechanism ensured rapid safety response to contact anomalies. These findings confirm that the proposed system offers high tracking accuracy and control stability, making it suitable for postoperative rehabilitation training.