The Hong Kong University of Science and Technology
Department of Computer Science


PhD Thesis Defence


"Probabilistic Activity Recognition from Low-Level Sensors"

By

Miss Jie Yin


Abstract

Activity recognition is gaining increasing interests in the artificial
intelligence (AI) and ubiquitous computing communities due to the
dramatically advancing sensor technology. In this dissertation, we address
the problem of probabilistic activity recognition from low-level sensor
data. The novelty of our work can be seen from two fronts. First, in the
pervasive computing literature, an important focus has been to determine a
user's context from streams of sensor data. Despite much previous work on
computing the locations of users, there has been a lack of study on the
problem of high-level activity recognition. Second, in the AI area,
recognizing complex high-level behavior has traditionally been the focus
of plan recognition. However, most of the work has been restricted to
high-level inferences in a logical framework, and the challenge of dealing
with low-level sensor modeling has so far not been adequately addressed.

In this dissertation, we propose several novel probabilistic algorithms
for activity recognition from low-level sensors. Firstly, we present a
novel clustering algorithm to automatically group user traces into a set
of clusters, each corresponding to a typical class of user activity
patterns. Secondly, we propose a hierarchical activity recognition model,
in which high-level inference about activities is enabled via a
location-based sensor model at the low level. Thirdly, to reduce the
calibration effort for activity recognition and to increase robustness in
recognition quality, we design a segmentation-based activity recognition
model, in which activities can be directly recognized from sequences of
discovered motion patterns. Finally, we also propose a novel approach to
detecting users' abnormal activities from sensor data. We demonstrate the
effectiveness of our proposed algorithms using the data collected in real
wireless environments.


Date:			Friday, 9 June 2006

Time:			10:00a.m.-12:00noon

Venue:			Room 4505
			Lifts 25-26

Chairman:		Prof. Pak Wo Leung (PHYS)

Committee Members:	Prof. Qiang Yang (Supervisor)
			Prof. Dit Yan Yeung
			Prof. Nevin Zhang
			Prof. Hong Xue (BICH)
			Dr. Feng Zhao (Microsoft Research, USA)


**** ALL are Welcome ****