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Research article summary:

A neuro-fuzzy model for estimating electromyographical activity of trunk muscles due to manual lifting.

Abstract Extract:
The main objective of this study was to develop a hybrid neuro-fuzzy system for estimating the magnitude of EMG responses of 10 trunk muscles based on two lifting task variables (trunk velocity and trunk moment) as model inputs. The input and output ... (Full abstract text below)

Published 2003Jan in Journal: Ergonomics (Language : eng)

Full Pubmed Extract

This information was retrieved, real-time, on your behalf from the public area of the Pubmed website:

1. Ergonomics. 2003 Jan;46(1-3):285-309

A neuro-fuzzy model for estimating electromyographical activity of trunk muscles due to manual lifting.

Lee W, Karwowski W, Marras WS, Rodrick D

Department of Industrial Management, Kumoh National University of Technology,188 Shin-Pyung Dong, Kumi, South-Korea 730-701.

The main objective of this study was to develop a hybrid neuro-fuzzy system for estimating the magnitude of EMG responses of 10 trunk muscles based on two lifting task variables (trunk velocity and trunk moment) as model inputs. The input and output variables were represented using the fuzzy membership functions. The initial fuzzy rules were generated by the neural network using true EMG data. Two different laboratory-derived EMG data sets were used for model development and validation, respectively. The mean absolute error (MAE) between the actual and model-estimated normalized EMG values was calculated. Across all muscles, the average value of MAE was 8.43% (SD=2.87%) of the normalized EMG data. The larger absolute errors occurred in the left side of the trunk, which exhibited higher levels of muscular activity. Overall, the developed model was capable of estimating the normalized EMG values with average value of the mean absolute differences of 6.4%. It was hypothesized that model performance could be improved by increasing the number of inputs, including additional task variables as well as the subjects' characteristics.

PMID : 12554412 [PubMed - Indexed for MEDLINE]


This information is obtained from the National Library of Medicine (NLM). Abstract text and other information may be subject to copyright. Type "NLM copyright" into Google for more information.

Full Author Information

First NameLastNameInitials
WookgeeLeeW
WaldemarKarwowskiW
William SMarrasWS
DavidRodrickD

Affiliation: Department of Industrial Management, Kumoh National University of Technology,188 Shin-Pyung Dong, Kumi, South-Korea 730-701.

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MESH categories and related page links

This article was linked to the MESH categories shown on the left below. The links on the right are related Memletics pages.

Category links from this article:

  • Biomechanics
  • Computer Simulation
  • Electromyography
  • Fuzzy Logic
  • Human Engineering
  • Humans
  • Lifting
  • Lumbosacral Region - physiology
  • Models, Neurological
  • Muscle Contraction - physiology
  • Neural Networks (Computer)
  • Risk Factors
  • Task Performance and Analysis
  • Time Factors
   

Related Memletics topics:

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