Recognition of construction workers' physical fatigue based on gait patterns driven from three-axis accelerometer embedded in a smartphone

Mohammad Sadra Fardhosseini, Mahmoud Habibnezhad, Houtan Jebelli, Giovanni Migliaccio, Hyun Woo Lee, Jay Puckett

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The construction industry is among the most hazardous industries in the United States, associated with a high number of accidents. Workers' fatigue has been recognized as one of the four major causes of fatal incidents in this industry. Therefore, early identification of workers' fatigue in a project could support accident prevention. To this end, the objective of the present study is to develop a framework to detect workers' fatigue by examining their gait patterns measured by a three-axis accelerometer embedded in a smartphone. The application of accelerometer sensors in a smartphone is useful because it can record gait-pattern data at the construction site (not just limited just to a controlled environment). To achieve this objective, five construction workers were asked to participate in this study by recording their gait patterns before and after a fatigue-inducing exercise. Related time features were extracted and selected to train the classifier. Finally, supervised-learning algorithms [e.g., linear and nonlinear support vector machines (SVM)] were adopted to detect workers' fatigue in different working conditions. The study results indicate that workers' fatigue was detected at an accuracy of 87.93% and 82.75% using the linear and nonlinear SVMs, respectively. It is expected that these findings will provide useful guidelines for early prediction of physical fatigue and therefore enable project managers to make informed decisions in improving worker safety.

Original languageEnglish (US)
Title of host publicationConstruction Research Congress 2020
Subtitle of host publicationSafety, Workforce, and Education - Selected Papers from the Construction Research Congress 2020
EditorsMounir El Asmar, David Grau, Pingbo Tang
PublisherAmerican Society of Civil Engineers (ASCE)
Pages453-462
Number of pages10
ISBN (Electronic)9780784482872
DOIs
StatePublished - 2020
EventConstruction Research Congress 2020: Safety, Workforce, and Education - Tempe, United States
Duration: Mar 8 2020Mar 10 2020

Publication series

NameConstruction Research Congress 2020: Safety, Workforce, and Education - Selected Papers from the Construction Research Congress 2020

Conference

ConferenceConstruction Research Congress 2020: Safety, Workforce, and Education
CountryUnited States
CityTempe
Period3/8/203/10/20

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction

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