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

Abstract

Objective

A neural networks approach has been proposed to handle various inputs such as postural, anthropometric and environmental variables in order to estimate self-reported discomfort in picking tasks. An input reduction method has been proposed, reducing the input variables to the minimum data required to estimate self-reported discomfort with similar accuracy as the neural network fed with all variables.

Background

Previous works have attempted to explore the relationship between several factors and self-reported discomfort using observational methods. The results showed that this relationship was not a simple linear relationship. Another study used neural networks to model the function returning reported discomfort according to static posture, age, and anthropometrics variables. The results demonstrated the model’s ability to predict reported discomfort. But all the available variables were used to design the neural network.

Method

Eleven subjects carried-out picking tasks with various masses (0, 1, 3 kg) and imposed duration (5, 10, or 15 s). Continuous REBA score, anthropometric and environmental data were computed, and subjects’ discomfort were collected. The data set of this work consisted in the computed continuous REBA score, anthropometric, environmental data and collected subjects’ discomfort.

Results

The results showed that the correlation between the estimated and experimental tested data was equal to 0.775 when using all the 14 available variables. After data reduction, only 6 variables were left, with a very close performance when predicting discomfort.

Conclusion

A neural network approach has been proposed to estimate self-reported discomfort according to a minimum set of postural, anthropometric and environmental variables in picking tasks.

Application

This method has the potential to support ergonomists in workstation designing processes, by adding discomfort prediction to virtual manikins’ behaviors in simulation tools.