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Related Experiment Video

Updated: Feb 27, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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Real-Time Estimation of User Adaptation During Hip Exosuit-Assisted Walking Using Wearable Inertial Measurement Unit

Cheonkyu Park1, Alireza Nasizadeh1, Kiho Lee1

  • 1School of Mechanical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.

Biomimetics (Basel, Switzerland)
|February 26, 2026
PubMed
Summary

This study developed a real-time method using wearable sensors to estimate user adaptation to hip exosuits. The system successfully tracked adaptation by analyzing gait variability, paving the way for personalized robotic assistance.

Keywords:
exosuitsgait variabilityreal-time monitoringuser adaptationwearable robots

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Area of Science:

  • Biomechanics
  • Robotics
  • Human-Computer Interaction

Background:

  • Wearable robots, like hip exosuits, can enhance walking efficiency.
  • The effectiveness of these devices hinges on the user's ability to adapt to the provided assistance.
  • Quantifying this adaptation in real-time is crucial for optimizing performance and user experience.

Purpose of the Study:

  • To introduce a novel framework for estimating user adaptation to wearable robotic assistance in real-time.
  • To utilize only wearable sensor data for adaptation estimation, excluding metabolic measurements during operation.
  • To validate the framework's ability to track adaptation during exoskeleton-assisted walking.

Main Methods:

  • Five healthy adults walked on a treadmill wearing a soft hip exosuit for six days.
  • Thigh-mounted inertial measurement units captured gait data, from which step-frequency, hip-flexion, and hip-extension variability were extracted.
  • A Long Short-Term Memory (LSTM) model estimated adaptation levels based on these kinematic variability features.

Main Results:

  • User adaptation was confirmed by a significant decrease in metabolic cost (9.0 ± 5.6%) over six days.
  • Kinematic variability metrics (step-frequency, max hip flexion, max hip extension) significantly decreased, reflecting user adaptation.
  • The LSTM model achieved 59.2% prediction accuracy within ±10 percentage points of metabolic cost-based adaptation using leave-one-subject-out validation.

Conclusions:

  • Real-time estimation of user adaptation to exosuit assistance is feasible using wearable sensor kinematic data.
  • Gait variability metrics effectively capture user adaptation to robotic assistance.
  • This framework supports adaptive control and personalized training protocols for wearable robots.