Device-free Activity Recognition Leveraging on Internet of Things
Significant growth in the aging population presents many challenges, which calls an urgent need to develop innovative technologies that help the elderly live independently and safely in their own homes. In this work, we propose a system using low-cost RFID tags, which enables device-free, unobtrusive monitoring of elderly people by leveraging machine learning algorithms and the Internet of Things (IoT) technology. We propose several machine learning algorithms to enable device-free localization and activity recognition by learning RSSI of RFID tags.
This work appears in ICDM 2015.
Tao Gu
Professor
IEEE Fellow
AAIA Fellow
ACM Distinguished Member
Email: firstname dot lastname AT mq.edu.au
Phone: +61-2-9850-4357
Address: Room 267, 4 Research Park Drive, North Ryde, NSW 2109, Australia
My research interests include Internet of Things, Ubiquitous Computing, Mobile Computing, Embedded AI, Wireless Sensor Networks, and Big Data Analytics.