TY - JOUR
T1 - Identification and classification of construction equipment operators' mental fatigue using wearable eye-tracking technology
AU - Li, Jue
AU - Li, Heng
AU - Umer, Waleed
AU - Wang, Hongwei
AU - Xing, Xuejiao
AU - Zhao, Shukai
AU - Hou, Jun
N1 - Funding Information:
This research study was partially supported by The Hong Kong Polytechnic University , the General Research Fund (GRF) Grant ( BRE/PolyU 152099/18E ) entitled “Proactive Monitoring of Work-Related MSD Risk Factors and Fall Risks of Construction Workers Using Wearable Insoles” and the project ( PolyU 152047/19E ) entitled “In search of a suitable tool for proactive physical fatigue assessment: an invasive to non-invasive approach”. Besides, this work was partially supported by the National Natural Science Foundation of China (Grants 71390524 , 71821001 ).
Publisher Copyright:
© 2019
PY - 2020/1
Y1 - 2020/1
N2 - In the construction industry, the operator's mental fatigue is one of the most important causes of construction equipment-related accidents. Mental fatigue can easily lead to poor performance of construction equipment operations and accidents in the worst case scenario. Hence, it is necessary to propose an objective method that can accurately detect multiple levels of mental fatigue of construction equipment operators. To address such issue, this paper develops a novel method to identify and classify operator's multi-level mental fatigue using wearable eye-tracking technology. For the purpose, six participants were recruited to perform a simulated excavator operation experiment to obtain relevant data. First, a Toeplitz Inverse Covariance-Based Clustering (TICC) method was used to determine the number of levels of mental fatigue using relevant subjective and objective data collected during the experiments. The results revealed the number of mental fatigue levels to be 3 using TICC-based method. Second, four eye movement feature-sets suitable for different construction scenarios were extracted and supervised learning algorithms were used to classify multi-level mental fatigue of the operator. The classification performance analysis of the supervised learning algorithms showed Support Vector Machine (SVM) was the most suitable algorithm to classify mental fatigue in the face of various construction scenarios and subject bias (accuracy between 79.5% and 85.0%). Overall, this study demonstrates the feasibility of applying wearable eye-tracking technology to identify and classify the mental fatigue of construction equipment operators.
AB - In the construction industry, the operator's mental fatigue is one of the most important causes of construction equipment-related accidents. Mental fatigue can easily lead to poor performance of construction equipment operations and accidents in the worst case scenario. Hence, it is necessary to propose an objective method that can accurately detect multiple levels of mental fatigue of construction equipment operators. To address such issue, this paper develops a novel method to identify and classify operator's multi-level mental fatigue using wearable eye-tracking technology. For the purpose, six participants were recruited to perform a simulated excavator operation experiment to obtain relevant data. First, a Toeplitz Inverse Covariance-Based Clustering (TICC) method was used to determine the number of levels of mental fatigue using relevant subjective and objective data collected during the experiments. The results revealed the number of mental fatigue levels to be 3 using TICC-based method. Second, four eye movement feature-sets suitable for different construction scenarios were extracted and supervised learning algorithms were used to classify multi-level mental fatigue of the operator. The classification performance analysis of the supervised learning algorithms showed Support Vector Machine (SVM) was the most suitable algorithm to classify mental fatigue in the face of various construction scenarios and subject bias (accuracy between 79.5% and 85.0%). Overall, this study demonstrates the feasibility of applying wearable eye-tracking technology to identify and classify the mental fatigue of construction equipment operators.
KW - Construction equipment operator
KW - Eye-tracking
KW - Machine learning
KW - Mental fatigue identification and classification
KW - Toeplitz Inverse Covariance-Based Clustering
UR - http://www.scopus.com/inward/record.url?scp=85074137956&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2019.103000
DO - 10.1016/j.autcon.2019.103000
M3 - Article
AN - SCOPUS:85074137956
SN - 0926-5805
VL - 109
JO - Automation in Construction
JF - Automation in Construction
M1 - 103000
ER -