TY - JOUR
T1 - Federated transfer learning enabled smart work packaging for preserving personal image information of construction worker
AU - Li, Xiao
AU - Chi, Hung lin
AU - Lu, Weisheng
AU - Xue, Fan
AU - Zeng, Jianhuan
AU - Li, Clyde Zhengdao
N1 - Funding Information:
This research was financially supported by the Hong Kong Innovation and Technology Fund (ITF) (No. ITP/029/20LP ) and Hong Kong RGC Postdoctoral Fellowship. It was also partially supported by the National Natural Science Foundation of China (NSFC) (Grant No. 71801159 and No. 52078302 ), the National Natural Science Foundation of Guangdong Province (Grant No. 2021A1515012204 ), Youth Fund of Humanities and Social Sciences Research of the Ministry of Education (Grant No. 18YJCZH090 ), and the funding support from Shenzhen Science and Technology Innovation Commission (Grant No. JCYJ20190808174409266 ).
Publisher Copyright:
© 2021
PY - 2021/8
Y1 - 2021/8
N2 - The rapidly expanding number of IoT-based camera devices makes smart work packaging (SWP) easier to access massive construction workers' personal image information for occupational health and safety (OHS) status monitoring. SWP can then transmit these personal data to the cloud for training the machine learning models and offer safety alerts or health insights. However, there are two urgently important challenges. Firstly, the machine learning model needs to aggregate the SWPs' image data from each construction worker, which may pose a risk to private data leakage without strict privacy and security agreement. In addition, the machine learning models trained on all SWPs' image data may compromise the personalization of image-based OHS status monitoring for each construction worker. To address the above issues, this study proposes a FedSWP framework, the federated transfer learning-enabled SWP for protecting the personal image information of construction workers in OHS management. FedSWP executes the gradient parameters aggregation through federated learning for the image data in each SWP and builds relatively personalized models by transfer learning. Crane operators' facial fatigue monitoring experiments are conducted and have evaluated that FedSWP can achieve accurate and personalized safety alerts and healthcare. This study paves the way for the generalization and extension of FedSWP in many construction OHS applications.
AB - The rapidly expanding number of IoT-based camera devices makes smart work packaging (SWP) easier to access massive construction workers' personal image information for occupational health and safety (OHS) status monitoring. SWP can then transmit these personal data to the cloud for training the machine learning models and offer safety alerts or health insights. However, there are two urgently important challenges. Firstly, the machine learning model needs to aggregate the SWPs' image data from each construction worker, which may pose a risk to private data leakage without strict privacy and security agreement. In addition, the machine learning models trained on all SWPs' image data may compromise the personalization of image-based OHS status monitoring for each construction worker. To address the above issues, this study proposes a FedSWP framework, the federated transfer learning-enabled SWP for protecting the personal image information of construction workers in OHS management. FedSWP executes the gradient parameters aggregation through federated learning for the image data in each SWP and builds relatively personalized models by transfer learning. Crane operators' facial fatigue monitoring experiments are conducted and have evaluated that FedSWP can achieve accurate and personalized safety alerts and healthcare. This study paves the way for the generalization and extension of FedSWP in many construction OHS applications.
KW - Facial fatigue
KW - Federated learning
KW - Image data
KW - Occupational health and safety
KW - Privacy and security
KW - Smart work packaging
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85105694753&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2021.103738
DO - 10.1016/j.autcon.2021.103738
M3 - Journal article
AN - SCOPUS:85105694753
VL - 128
JO - Automation in Construction
JF - Automation in Construction
SN - 0926-5805
M1 - 103738
ER -