报告人：香港城市大学教授，美国哥伦比亚大学博士 Dr.Chen，
时间: 10:00 AM, 2018 Dec 14
地点: 1066vip威尼斯宝山校区东区机自学院9号楼，604会议室，14号上午10点
报告人简介：Dr. Jiayu CHEN is an assistant professor, academic staff, in College of Science and Engineering of City University of Hong Kong. He obtained his Ph.D. degrees from Columbia University in the United States (美国哥伦比亚大学). He used to work as a research associate in Virginia Tech (2011) and Post Doc researcher in University of Nebraska-Lincoln (2013) before he joined the City University of Hong Kong in August 2013. He served as principal investigators of several research grants from Hong Kong University Grant Council, Hong Kong Environmental Conservation Fund, and Hong Kong Construction Industry Council. He also served as the technical committee member of several panels of Hong Kong Building Department, Development Bureau, and Home Affairs Bureau.  Dr. Chen is an active researcher and published 34 SCI journal papers. His current research mainly focuses on people-centric sensing and wireless sensor networks for construction safety management, building occupancy detection, and facility control.
Seminar Title： Human-centric physiological and mental sensing for safety management in dynamic working conditions
Seminar Abstract：
Construction and other labor-intensive industry suffer from losses due to fatalities and injuries. Since more than 70% of all accidents are related to human activities, detecting and mitigating human-related risks hold the key to improving the safety in labor working environment. Recent studies in the area of neural science and psychology suggest that inattentional blindness is one major cause of unexpected human related accidents. Due to the limitation of human mental workload, laborers are vulnerable to unexpected hazards while focusing on complicated tasks. Therefore, the ability to detect the mental conditions of workers could reduce unexpected injuries. In this seminar, Dr. Jiayu CHEN will share his recent research progress in developing quantitative measurement approach to evaluate hazards through neural time–frequency analysis on human kinematic-mental signals. This research implemented wearable EEG and motion capturing system to proposed a digital monitoring platform for workers' attention and vigilance assessment. Based on the results, theoretical models were constructed for risk assessment during working activities.