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Visual-Inertial Fusion Framework for Isolating Seated Human-Body Vibration in Dynamic Vehicular Environments.

Nova Eka Budiyanta1,2,3, Azizur Rahman4, Chi-Tsun Cheng5

  • 1Biomedical Engineering Department, School of Engineering, STEM College, RMIT University, Melbourne, VIC 3000, Australia.

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Summary
This summary is machine-generated.

This study introduces a visual-inertial fusion framework to accurately measure seated whole-body vibration (WBV) and active postural compensation in vehicles. The system effectively separates body motion from camera jitter for better analysis.

Keywords:
in-vehicle human posture dynamicvisual–inertial sensor fusionwhole-body vibration

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Area of Science:

  • Biomechanics
  • Human-Computer Interaction
  • Automotive Engineering

Background:

  • Accurate assessment of discomfort, fatigue, and postural control in vehicle occupants requires understanding whole-body vibration (WBV) transmission and active compensation.
  • Existing methods often use single-modality sensing, failing to decompose complex motion components like platform vibration, passive transmission, active compensation, and camera jitter.

Purpose of the Study:

  • To propose and validate a visual-inertial fusion framework for isolating seated human body vibration in dynamic vehicular environments.
  • To differentiate between passive vibration transmission and active postural compensation.
  • To separate true body motion from camera jitter in real-world monitoring systems.

Main Methods:

  • A visual-inertial fusion framework integrating Inertial Measurement Units (IMUs) and RGB-D data was developed.
  • The system synchronized three IMUs with RGB-D landmarks to separate seat, human body, and camera accelerations.
  • Body vibration velocity was derived from differential acceleration, incorporating band-pass filtering and spectral integration. Postural Compensation Index metrics were calculated using 3D landmarks.

Main Results:

  • The framework successfully distinguished passive ride phases from actively compensated phases.
  • Camera jitter was effectively separated from genuine human body motion.
  • Anisotropic postural strategies were revealed, with torso vibration dominated by anteroposterior components (40%) and head motion by lateral sway (>50%).
  • Anthropometric evaluations showed shoulder width errors within ±10-20 mm.

Conclusions:

  • The proposed framework provides a structured basis for analyzing vibration and posture in in-vehicle monitoring.
  • It enables a more accurate assessment of occupant comfort, fatigue, and postural control by decomposing complex motion dynamics.
  • The system's ability to isolate specific motion components offers advancements for driver monitoring and vehicle ride analysis.