Learning with Limited Data in Sensor-based Human Behavior Recognition

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

PhD Thesis Defence

Title: "Learning with Limited Data in Sensor-based Human Behavior Recognition"


Mr. Wenchen Zheng


Human behavior recognition from sensor observations is an important topic in 
both artificial intelligence and mobile computing. It is also a difficult task 
as the sensor and behavior data are usually noisy and limited. In this thesis, 
we first introduce the three major problems in human behavior recognition, 
including location estimation, activity recognition and mobile recommendation. 
Solving these three problems helps to answer the typical questions in human 
behavior recognition, such as where a user is, what s/he is doing and whether 
s/he will be interested in doing something at somewhere. In our attempt to 
solve these problems, we find that in practice the biggest challenge comes from 
the data sparsity. Such data sparsity can be because we have limited labeled 
data for new contexts in localization, or limited sensor data for users / 
activities in activity recognition, or limited activity data for mobile 
recommendation. In order to address these challenges, we propose learning 
methods which can effectively incorporate domain-dependent auxiliary data in 
training and thus greatly relieve the sparsity problem. We conduct empirical 
studies with real-world data sets, and demonstrate the effectiveness of our 
algorithms over the competing baselines.

Date:			Wednesday, 3 August 2011

Time:			2:00pm – 4:00pm

Venue:			Room 3584
 			Lifts 27/28

Chairman:		Prof. Vincent Lau (ECE)

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Prof. Dik-Lun Lee
 			Prof. Raymond Wong
                         Prof. Fugee Tsung (IELM)
                         Prof. Jian-Nong Cao (Computing, PolyU)

**** ALL are Welcome ****