南开大学机器人与信息自动化研究所 天津市智能机器人技术重点实验室
Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics
2015年秋季先进机器人与MEMS技术系列学术讲座
Seminar Series：Advanced Robotics & MEMS

报告人： Prof Francis Tay Eng Hock
时间：2015年11月20日（周五）上午10:00-11:30

地点：津南校区信息东楼计控学院225室

题目：MEMSWear
Mission Critical Wearable Embedded Systems for Elderly Care

摘要：The goal of the project is to build a wearable system (MEMSWear) by creating reliable, light weight, and smart systems which can predict and prevent falls among the elderly and minimize injuries where a fall is inevitable. Post-fall vital signs monitoring system is also developed to assess subject’s vital signs condition after fall. In this seminar, a wearable faint fall safety system capable of fall onset detection, injury minimization and airbag deployment for cushioning will be introduced. New technologies in post-fall vital signs monitoring will be discussed. Besides this project, a brief introduction on applying support vector machine (SVM) in rehabilitation area, e.g., to classify chronic lower back pain patients and to identify the range of the healthy people, will be conducted.

报告人简介：
Francis Eng Hock Tay is an Associate Professor with the Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore. He received the Ph.D. degree from the Massachusetts Institute of Technology (MIT), Cambridge, in 1995. Dr. Tay was the founding director of the Microsystems Technology Initiative (MSTI), and had established the Microsystems Technology Specialization. He had also served as the Technical Advisor, in the Micro and Nano Systems Laboratory, Institute of Materials Research Engineering (IMRE). His research areas including MEMS, Biotechnology, Nanotechnology, Wearable Devices, Fall Onset Detection, Vital Signs Monitoring, Body Sensor Network, Rehabilitation and Scoliosis. He is also the Principal Investigator for several projects under Agency for Science, Technology and Research (A*STAR) and one project under Qatar National Research Fund (QNRF).