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Related Experiment Video

Updated: Feb 14, 2026

An Instrumented Pull Test to Characterize Postural Responses

An Instrumented Pull Test to Characterize Postural Responses

Published on: April 6, 2019

Pulling force prediction using neural networks.

Rahul Jain^1,2, Makkhan Lal Meena², Manoj Kumar Sain^2,3

¹a University Teaching Department , Rajasthan Technical University Kota , India.

International Journal of Occupational Safety and Ergonomics : JOSE

|February 23, 2018

Summary

This summary is machine-generated.

Artificial neural networks (ANNs) provide a more accurate method for estimating pulling force (PF) compared to traditional regression models. This study demonstrates ANNs as a robust alternative for biomechanics and ergonomics assessments.

Keywords:

handle height industrial ergonomics manual handling neural network pulling force

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Area of Science:

Ergonomics and Human Factors
Biomechanics
Artificial Intelligence

Background:

Pulling force (PF) estimation is crucial in ergonomics and human factors.
Traditional biomechanical models and regression methods are commonly used for PF prediction.
The application of Artificial Neural Networks (ANNs) in work-related biomechanics remains underexplored.

Purpose of the Study:

To compare the predictive capacity of Artificial Neural Networks (ANNs) against regression models for estimating pulling force (PF).
To assess the effectiveness of ANNs in biomechanics and ergonomics task analysis.

Main Methods:

Collected multipositional PF data from 200 subjects across various handle heights and body locations.
Developed and validated both ANN and regression models using training, selection, and validation subsets (75%, 15%, 10%).

Main Results:

ANN models demonstrated significantly higher explained variance in PF predictions.
ANN models exhibited lower root mean square difference values compared to regression models.
The superior performance of ANNs was consistent across three different handle heights.

Conclusions:

Artificial Neural Networks (ANNs) present a precise and robust alternative to regression methods for PF estimation.
ANNs should be recognized as a valuable tool for biomechanics and ergonomics assessments.
The findings support the increased adoption of ANNs in analyzing human factors and work-related tasks.