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Related Experiment Video

Updated: May 25, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Design for worksystem safety using employees' perception about safety.

J Maiti1

  • 1Department of Industrial Engineering and Management, Indian Institute of Technology Kharagpur, Kharagpur, Paschimbanga, India. jmaiti@iem.iitkgp.ernet.in

Work (Reading, Mass.)
|February 10, 2012
PubMed
Summary

Workplace injuries in mining are predicted by age, negative affectivity, physical hazards, job dissatisfaction, and safety practices. Organizational factors and employee training significantly impact accident avoidance.

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Last Updated: May 25, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

  • Occupational Safety and Health
  • Ergonomics
  • Sociotechnical Systems

Background:

  • Workplace injuries pose significant risks in mining operations.
  • Leamon's Man-Machine model provides a framework for analyzing work system safety.
  • Sociotechnical systems consider the interaction between people and technology.

Purpose of the Study:

  • To identify predictors of work injuries in mining using a sociotechnical framework.
  • To develop and test hypotheses explaining accident/injury phenomena.
  • To propose safety improvement designs using scaled Mahalanobis distance (MD).

Main Methods:

  • A case-control study design was adopted.
  • Leamon's Man-Machine model was applied within a sociotechnical framework.
  • Scaled Mahalanobis distance (MD) was used to specify safety improvement designs.

Main Results:

  • Age, negative affectivity, physical hazards, job dissatisfaction, and safety practice were significant predictors of work injuries.
  • 36% of injured employees (cases) had MD < 1, suggesting 'unluckiness' potentially due to organizational ineffectiveness.
  • 40% of non-injured employees (controls) had MD > 1, suggesting 'luckiness' potentially due to risky behaviors from lack of training.

Conclusions:

  • Organizational ineffectiveness and employee risk-taking behaviors significantly contribute to work injuries.
  • Tailored safety interventions, including engineering controls, education, and training, are crucial.
  • MD analysis can guide the implementation of specific safety measures based on accident situations.