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Related Concept Videos

Knee Joint01:23

Knee Joint

The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris group...

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Related Experiment Video

Updated: Jul 1, 2026

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
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Bio-inspired auto-adaptive framework for optimized movement of passive knee prosthesis.

Muhammad Asif1, Mohsin Islam Tiwana2, Waqar Shahid Qureshi3

  • 1National University of Sciences and Technology (NUST), Department of Mechatronics Engineering, Islamabad 44000, Pakistan; College of Electrical and Mechanical Engineering of NUST, National Centre of Robotics and Automation (NCRA), Islamabad 44000, Pakistan; University of Engineering and Technology (UET) Taxila, Taxila, Department of Mechatronics Engineering, 47080, Pakistan.

Journal of the Mechanical Behavior of Biomedical Materials
|September 23, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an adaptive bio-inspired framework for passive knee prostheses, improving amputee mobility and fall prevention. The system uses femur power and deep learning for natural walking assistance.

Keywords:
Auto-adaptive frameworkBio-inspiredDamping adjustmentDeep learningFall preventionGait analysisPassive knee prosthesis

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Biomechanics

Background:

  • Amputees face mobility challenges with passive knee prostheses.
  • Existing prostheses often lack adaptive capabilities for natural gait.
  • Restoring function and comfort for amputees is a significant unmet need.

Purpose of the Study:

  • To develop a bio-inspired adaptive framework for passive knee prostheses.
  • To mimic natural walking and compensate for lost limb mobility.
  • To enhance the quality of life for lower-limb amputees.

Main Methods:

  • Developed a framework utilizing femur input power and a damping control mechanism.
  • Integrated sensors for real-time data acquisition.
  • Employed a deep learning architecture for gait phase event classification.

Main Results:

  • Achieved 94.44% classification accuracy for gait phase events using deep learning.
  • Demonstrated optimized movement with reduced hip hikes and less fatigue.
  • Maintained normal knee flexion (64°±6) and achieved 95% fall prevention.

Conclusions:

  • The proposed framework significantly improves passive knee prosthesis functionality and comfort.
  • This innovation offers a promising solution for enhancing amputee mobility and safety.
  • The research contributes to advancing prosthetic technology for better amputee quality of life.