TY - GEN
T1 - Recognition of construction workers' physical fatigue based on gait patterns driven from three-axis accelerometer embedded in a smartphone
AU - Fardhosseini, Mohammad Sadra
AU - Habibnezhad, Mahmoud
AU - Jebelli, Houtan
AU - Migliaccio, Giovanni
AU - Lee, Hyun Woo
AU - Puckett, Jay
N1 - Publisher Copyright:
© 2020 American Society of Civil Engineers.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85086991161&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086991161&partnerID=8YFLogxK
U2 - 10.1061/9780784482872.049
DO - 10.1061/9780784482872.049
M3 - Conference contribution
AN - SCOPUS:85086991161
T3 - Construction Research Congress 2020: Safety, Workforce, and Education - Selected Papers from the Construction Research Congress 2020
SP - 453
EP - 462
BT - Construction Research Congress 2020
A2 - El Asmar, Mounir
A2 - Grau, David
A2 - Tang, Pingbo
PB - American Society of Civil Engineers (ASCE)
T2 - Construction Research Congress 2020: Safety, Workforce, and Education
Y2 - 8 March 2020 through 10 March 2020
ER -