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Updated: Jun 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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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 Kinematic Reconstruction of Human Lower Limbs Using a 3-IMU Wearable Sensor Network, Transformer Model, and

Yang Yu1, Wei Dong1, Hui Dong1

  • 1State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a wearable system with three inertial sensors for precise lower-limb motion tracking. The novel approach uses a Transformer network for accurate gait analysis and rehabilitation in real-world settings.

Area of Science:

  • Biomechanics
  • Wearable Technology
  • Machine Learning

Background:

  • Continuous lower-limb kinematics monitoring is crucial for gait analysis and rehabilitation.
  • Existing methods like optical systems and sparse inertial sensors have limitations in natural environments.
  • Accurate and comfortable motion capture in unconstrained daily scenarios remains a challenge.

Purpose of the Study:

  • To develop a high-precision, minimalist wearable system for continuous lower-limb kinematics monitoring.
  • To reconstruct full lower-limb kinematics without rigid biomechanical assumptions.
  • To enable real-time edge inference for unconstrained human motion capture.

Main Methods:

  • Utilized a minimalist wearable system with three inertial measurement units (IMUs) on the pelvis and shanks.
Keywords:
edge computinghuman motion captureinertial measurement unit (IMU)kinematic reconstructionwearable sensors

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Last Updated: Jun 27, 2026

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  • Applied adaptive channel adjustment based on gradient descent for IMU data preprocessing.
  • Employed a pure Transformer neural network to capture long-range temporal dependencies for kinematics reconstruction.
  • Optimized and deployed the model on an STM32N647 microcontroller for real-time edge inference.

    • Main Results:

    • Achieved a mean absolute error of 2.41° for level walking, outperforming Kalman filter approaches.
    • Demonstrated high tracking robustness during complex movements like squatting and lunging.
    • Real-time edge inference achieved with low latency (approx. 17 ms).

    Conclusions:

    • The proposed edge-computing-enabled framework offers an accurate and comfortable solution for real-time human motion capture.
    • This system overcomes limitations of traditional methods for gait analysis and rehabilitation in daily scenarios.
    • The Transformer-based approach reconstructs kinematics effectively without relying on biomechanical constraints.