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Related Experiment Video

Updated: May 14, 2026

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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Published on: August 30, 2016

Automated Ergonomic Risk Assessment of Wheelchair Users During Cabinet Interaction Using Vision-Based 3D Pose

Yilin Xu1, Ziqian Yang1, Tao Sun2

  • 1College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
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This study introduces a vision-based system for assessing wheelchair user ergonomics, improving health monitoring in assistive living. The framework accurately quantifies ergonomic risks using advanced AI and computer vision techniques.

Area of Science:

  • Human-Computer Interaction
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Intelligent health management requires continuous perception and real-time interpretation of motion signals in human-centered environments.
  • Automated ergonomic risk assessment is crucial for safe and adaptive assistance, especially for wheelchair users.

Purpose of the Study:

  • To propose a vision-based sensor signal analysis framework for automated ergonomic risk assessment of wheelchair users during cabinet interaction.
  • To enable non-contact, real-time health-state interpretation in assistive living environments through continuous kinematic representations and ergonomic risk scores.

Main Methods:

  • Integration of YOLOv11 for human detection, MHFormer for monocular 3D pose reconstruction, and a fuzzy logic-enhanced Risk Assessment (RULA) model.
Keywords:
3D pose estimationergonomic risk assessmentfuzzy RULAhuman motion analysismonocular visionwheelchair users

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  • Development and evaluation using a dedicated wheelchair cabinet-operation dataset with 30 participants across five scenarios.
  • Transformation of visual sensor signals into kinematic representations and ergonomic risk scores.

    • Main Results:

    • Achieved an average joint-angle estimation RMSE of 7.5°, a 60% improvement over Kinect v2 baseline.
    • Demonstrated 84% risk-classification accuracy with a Cohen's kappa of 0.66, outperforming baseline approaches.
    • Identified higher and more sustained ergonomic risk associated with low revolving-door and low-drawer operations compared to sliding-door interactions.

    Conclusions:

    • Vision-based sensor signal analysis offers an effective solution for intelligent health management and ergonomic monitoring.
    • The proposed framework enables perception-driven assessment in accessible and assistive autonomous living systems.
    • Findings highlight the potential for real-time, non-contact ergonomic risk evaluation in daily living environments.