Friday, 29 May 2015
Royal Academy of Engineering, Prize Winner
At the recent regional event of the prestigious Royal Academy of Engineering, one of the PhD students, Sandipan Pal was awarded runners-up prize for his poster based on his PhD project “Video-based Lifestyle Monitoring for Assisted Living”. This collaborative project brings together the expertise of computer vision research within the Department of Electronic and Electrical Engineering (EEE) to addresses the technical challenges involved in the creation and application of video-based lifestyle monitoring system by health service researchers within the School of Health and Related Research (ScHARR). This project is part of the PIPIN (Promoting Independence through Personalized Interactive Technologies) network, funded by The University of Sheffield cross-cutting research network scheme (click here for more information).
Sandipan’s poster was one of the six finalists chosen from within the Faculty of Engineering to represent the University at this event. At the event there were posters by early career researchers from other UK universities. The poster was judged by the Fellows of the Academy. Sandipan received his award from Dame Ann Dowling, DBE FRS FREng, President of the Royal Academy of Engineering.
A rapidly ageing world envisages the use of technology for promoting independent living for the elderly. It is widely accepted that the amount of daily activities undertaken is representative of ones’ health. Current passive home monitoring technologies are mostly passive sensor-based. Often an obtrusive, complicated sensor network monitors a single or multiple parameters of daily living of an individual. On the other hand, with the reduction of camera prices and the evolution of computer vision techniques, there is a growing interest of using video-based systems for different application domains. This project explores the idea of using a camera instead of passive sensors to monitor the day to day activity levels of an individual within a home environment. Using computer vision and machine learning techniques, a personalized profile of an individual would be created which would be indicative of the daily activities undertaken and representative of the health status of an individual within the home environment thereby promoting healthy and independent living.