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Related Experiment Video

Updated: Sep 11, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Automatic estimation of Hand Activity Level from upper-limb trajectories: a probabilistic regression framework.

Ting-Hung Lin1, Yu Hen Hu1, Robert Radwin2

  • 1Department of Electrical & Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA.

Ergonomics
|August 14, 2025
PubMed
Summary

This study introduces a new video-based method to automatically measure Hand Activity Level (HAL) for assessing repetitive work injury risk. The framework provides objective and reliable HAL scores with confidence measures.

Keywords:
Hand Activity Levelcomputer visionprobabilistic regressionworkplace safety

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Area of Science:

  • Ergonomics and Occupational Health
  • Computer Vision and Machine Learning
  • Biomechanical Engineering

Background:

  • Accurate Hand Activity Level (HAL) measurement is vital for assessing musculoskeletal injury risk in repetitive tasks.
  • Current manual HAL assessments are subjective and not scalable for continuous monitoring.
  • Objective and automated methods are needed for reliable ergonomic risk evaluation.

Purpose of the Study:

  • To develop and validate a probabilistic regression framework for automatic HAL score estimation using video data.
  • To provide confidence measures alongside HAL predictions for quantified uncertainty in ergonomic risk assessment.
  • To enable objective, reliable, and scalable monitoring of hand activity in occupational settings.

Main Methods:

  • Utilized video-based upper-limb pose trajectories as input features.
  • Developed a probabilistic regression framework for HAL score estimation.
  • Incorporated confidence measures to quantify prediction uncertainty.

Main Results:

  • Achieved strong in-domain performance with Root Mean Square Error (RMSE) = 0.24 and Mean Absolute Error (MAE) = 0.17.
  • Demonstrated robust cross-domain generalisation with RMSE = 0.74 and MAE = 0.54.
  • The framework provides accurate and transferable HAL predictions.

Conclusions:

  • The proposed video-based probabilistic regression framework enables objective and reliable automatic estimation of Hand Activity Level (HAL).
  • Quantified uncertainty through confidence measures enhances ergonomic risk assessment.
  • The method shows significant potential for large-scale and continuous monitoring of hand-intensive work, improving occupational safety.