Context-awareness sensing with RFID

PhD Thesis Proposal Defence


Title: "Context-awareness sensing with RFID"

by

Mr. Longfei SHANGGUAN


Abstract:

Radio Frequency IDentification (RFID) is a rapidly growing technology that has 
the potential to make great economic impacts on many industries. While RFID is 
a relatively old technology, more recent advancements in chip manufacturing 
technology are making RFID practical for new applications and settings, 
particularly object localization, human activity sensing and consumer item 
level tagging. These advancements have the potential to revolutionize 
supply-chain management, inventory control, and human computer interaction. In 
this thesis, we focus on developing new algorithms for fine-grained object 
localization and human activity sensing based on passive RFIDs.

Firstly, we propose a new method for luggage order tracking based on the 
signals features backscattered from passive tags. In many logistics 
applications, luggage attached with tags are placed on moving conveyor belts 
for processing. It is important to figure out the order of goods on the belts 
so that further actions like sorting can be accurately taken on proper goods. 
Due to arbitrary goods placement or the irregularity of wireless signal 
propagation, neither of the order of tag identification nor the received signal 
strength provides sufficient evidence on their relative positions on the belts. 
In this study, we establish a probabilistic model for recognizing the transient 
critical region and propose the OTrack protocol to continuously monitor the 
order of tags. we evaluate the accuracy and effectiveness through a one-month 
experiment conducted through a working conveyor at Beijing Capital 
International Airport.

Secondly, we propose a new method for RFID based relative object localization. 
Although many schemes for object localization using Radio Frequency 
Identification (RFID) tags have been proposed, they mostly focus on absolute 
object localization and are not suitable for relative object localization 
because of large error margins and the special hardware that they require. We 
propose an approach called Spatial-Temporal Phase Profiling (STPP) to RFID 
based relative object localization. The basic idea of STPP is that by moving a 
reader over a set of tags, for each tag, the reader obtains a sequence of RF 
phase values, which we call a phase profile. By analyzing the spatial-temporal 
dynamics in the phase profiles, STPP can calculate the spatial ordering among 
the tags. STPP requires neither dedicated infrastructure nor special hardware. 
We implemented STPP and evaluated its performance in two real-world 
applications: locating misplaced books in a library and sorting baggage in an 
airport.

Thirdly, we propose a new sensing scheme for free-weight exercise monitoring 
based on passive RFIDs. Regular free-weight exercise helps to strengthen the 
body's natural movements and stabilize muscles that are important to strength, 
balance, and posture of human beings. Prior works have exploited wearable 
sensors or RF signal changes (e.g., WiFi and Blue-tooth) for activity sensing, 
recognition and counting etc. However, none of them have incorporate three key 
factors necessary for a practical free-weight exercise monitoring system: 
recognizing free-weight activities on site, assessing their qualities, and 
providing useful feedbacks to the bodybuilder promptly. Our FEMO system 
responds to these needs, providing an integrated free-weight exercise 
monitoring that incorporates all the essential functionalities mentioned above. 
We implement FEMO with COTS RFID systems and conduct a two-weeks experiment. 
The preliminary result from 15 volunteers demonstrates that FEMO can be applied 
to a variety of free-weight activities and users.


Date:			Thursday, 15 January 2015

Time:                   3:00pm - 5:00pm

Venue:                  Room 3494
                         lifts 25/26

Committee Members:	Dr. Ke Yi (Supervisor)
 			Dr. Kai Chen (Chairperson)
 			Dr. Lin Gu
 			Dr. Qiong Luo


**** ALL are Welcome ****