Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers

Maxwell Fordjour Antwi-Afari, Heng Li, Yantao Yu, Liulin Kong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

Awkward working postures are the main risk factor for work-related musculoskeletal disorders (WMSDs) causing non-fatal occupational injuries among construction workers. However, it remains a challenge to use existing risk assessment methods for detecting and classifying awkward working postures because these methods are either intrusive or rely on subjective judgment. Therefore, this study developed a novel and non-invasive method to automatically detect and classify awkward working postures based on foot plantar pressure distribution data measured by a wearable insole pressure system. Ten asymptomatic participants performed five different types of awkward working postures (i.e., overhead working, squatting, stooping, semi-squatting, and one-legged kneeling) in a laboratory setting. Four supervised machine learning classifiers (i.e., artificial neural network (ANN), decision tree (DT), K-nearest neighbor (KNN), and support vector machine (SVM)) were used for classification performance using a 0.32 s window size. Cross-validation results showed that the SVM classifier (i.e., the best classifier) obtained a classification performance with an accuracy of 99.70% and a sensitivity of each awkward working posture was above 99.00% at 0.32 s window size. The findings substantiated that it is feasible to use a wearable insole pressure system to identify risk factors for developing WMSDs, and could help safety managers to minimize workers’ exposure to awkward working postures.

Original languageEnglish
Pages (from-to)433-441
Number of pages9
JournalAutomation in Construction
Volume96
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Awkward working postures
  • Construction workers
  • Foot plantar pressure distribution
  • Supervised machine learning classifiers
  • Wearable insole pressure system
  • Work-related musculoskeletal disorders

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

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