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A Neural Networks Approach to Determine Factors Associated With Self-Reported Discomfort in Picking Tasks.

Olfa Haj Mahmoud^1,2, Charles Pontonnier², Georges Dumont²

¹FAURECIA Automotive Seating, Etampes, France.

|October 4, 2021

Summary

A new neural network model estimates self-reported discomfort in picking tasks using fewer variables. This approach maintains accuracy while simplifying data input for ergonomic assessments.

Keywords:

CR10 REBA WMSD kinematics physical risk factors

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Area of Science:

Ergonomics and Human Factors
Artificial Intelligence in Occupational Health
Biomechanical Modeling

Background:

Previous research explored factors influencing self-reported discomfort but found non-linear relationships.
Neural networks have been used to predict discomfort based on posture, age, and anthropometrics, but utilized all available variables.

Purpose of the Study:

To develop a neural network approach for estimating self-reported discomfort in picking tasks.
To identify a minimum set of essential variables for accurate discomfort prediction.
To simplify the input requirements for discomfort estimation models.

Main Methods:

Eleven subjects performed picking tasks with varying masses and durations.
Continuous REBA (Rapid Entire Body Assessment) scores, anthropometric, and environmental data were collected.
A neural network model was trained and tested using a reduced set of input variables.

Main Results:

The initial model using 14 variables achieved a correlation of 0.775 between estimated and experimental data.
Data reduction techniques identified a minimal set of 6 variables.
The reduced variable set yielded comparable accuracy in predicting self-reported discomfort.

Conclusions:

A neural network approach effectively estimates self-reported discomfort using a minimal set of postural, anthropometric, and environmental variables.
This method can aid ergonomists by integrating discomfort prediction into virtual manikin simulations for workstation design.