Dr Wee Hong Ong


Lecturer, Faculty of Science



Ong Wee Hong is a lecturer in Computer Science program in the Universiti Brunei Darussalam (UBD). He joined the UBD in 2007. Before joining the UBD, he taught in the Jefri Bolkiah College of Engineering from 1998 to 2007. Ong Wee Hong received the B.Eng. in Communication and Control Engineering from the University of Manchester, Institute of Science and Technology in 1997. He received the M.Sc. in Computing Science from the Imperial College London in 2004. In 2014, he received the Doctor of Engineering ( PhD ) in Electrical and Information Systems from the University of Tokyo, Japan.


Personal robots, ambient intelligence


Unsupervised human activities recognition

Human Activity Recognition (HAR) is an important component in assistive technologies, however, we have not seen wide adoption of HAR technologies in our homes. Two main hurdles to the wide adoption of HAR technologies in our homes are the expensive infrastructure requirement and the use of supervised learning in the HAR technologies. Many HAR researches have been carried out assuming an environment embedded with sensors. In addition, the majority of HAR technologies use supervised approaches, where there are labeled data to train the expert system. In reality, our natural living environment are not embedded with sensors. Labeled data are not available in our natural living environment. We are developing a framework for autonomous HAR suitable in our natural living environment, i.e. the sensor-less homes. The framework uses unsupervised learning approach to enable a robot, acting as a mobile sensor hub, to autonomously collect data and learn the different human activities without requiring manual (human) labeling of the data.

Applications Invited
MSc, PhD

Public-cloud based smart devices for Internet of Things (IoT)

Smart devices in an IoT system, such as the smart home, either connect through their own proprietary server running their server side applications, or they connect through the user home network where a center device is running the necessary server side applications. The second approach is not flexible and not user friendly to setup. There is increasing number of systems that take the first approach of having their own cloud server. However, not all inventors are capable of hosting their cloud server to cater for large volume of users. This restricts the development of large scale IoT systems to large companies. There is also the concern of being tied to a proprietary service. To address this issue and to allow amateur inventors to build large scale IoT systems, we are developing a new form of connectivity for IoT systems by exploiting public-cloud services widely used by general public such as DropBox and Google Drive.

Applications Invited
MSc, PhD

Self-driving car

Self-driving car technologies are growing and maturing. It will be part of the future transport system. We are initiating our venture into this domain inline with our interest in robot navigation. In this project, we will build a prototype self-driving car and use it to conduct research works in the various aspects of self-driving car.

Applications Invited

Scopus Publications

Scopus Publications


1. Ong, W.-H., Palafox, L. & Koseki, T. Autonomous Learning and Recognition of Human Action based on An Incremental Approach of Clustering. IEEJ Trans. Electron. Inf. Syst. 135, 1136–1141 (2015)
2. Ong, W.-H., Palafox, L. & Koseki, T. An Incremental Approach of Clustering for Human Activity Discovery. IEEJ Trans. Electron. Inf. Syst. 134, 1724–1730 (2014)
3. Ong, W. H., Koseki, T. & Palafox, L. Unsupervised Human Activity Detection with Skeleton Data from RGB-D Sensor. in 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks 30–35 (IEEE, 2013)
4. Chin, K. C.-Y. & Buhari, S. M. Impact of LEGO sensors in remote controlled robot. in 2008 IEEE International Conference on Robotics and Biomimetics 1777–1782 (IEEE, 2009)