© 2019 by Dr Zilu Liang.  

Research Projects

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PI: Dr. Zilu Liang

Co-PI: Dr. Mario Alberto Chapa-Martell (Silver Egg)

Status: On-going

Where is Stress Processed at Night? A Neuron Imaging Study into Stress-Related Abnormality in Brain Activity During Sleep 

Funded by JSPS Grant-in-Aid (科研費)

Stress is something we can feel subjectively, but how exactly our brain processes stress? Why does stress often cause sleep problems? This study aims to explore the stress-related abnormality in brain activity especially during sleep. This study involves the use of wearable neuron imaging devices and salivary cortisol test. 

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PI: Dr. Zilu Liang

Co-PI: Dr. Mario Alberto Chapa-Martell (Silver Egg)

           Dr. Bernd Ploderer (QUT)

Status: On-going

Developing Accurate Home Sleep Tracking Technology

Funded by JSPS Grant-in-Aid (科研費)

Partnered with QUT, Australia

Sleep-tracking has never been so easy with the rise of consumer wearable devices such as Fitbit and Neuroon, but do you trust what these devices tell you about your sleep? Furthermore, consumer sleep trackers are increasingly used in scientific studies to measure sleep outcomes. It is therefore of practical benefit to ensure that the sleep data collected using these devices are accurate and reliable. This study explores the accuracy issue of consumer sleep trackers both quantitatively and qualitatively. We also aim at developing computing technology that leverages the convenience of consumer sleep-tracking devices for medical-grade accuracy. This study involves the use of consumer wearable wristbands and EEG as well as medical EEG. 

PI: Dr. Bernd Ploderer (QUT)

Co-PI: Dr. Zilu Liang

Status: On-going

SleepBeta: A Mobile App to Improve Sleep Habits Among Students

Partnered with QUT, Australia

Sleep is utmost for physical and mental health, but students usually do not see the importance of having sufficient and good quality of sleep. This study aims to design, develop and validate a mobile application named SleepBeta to raise awareness of sleep health and to promote good sleep hygiene among students. This study involves web app development under the ASP.NET MVC framework as well as the design and conduct of qualitative human-computer interaction study. 

PI: Dr. Zilu Liang

Co-PI: Dr. Gary White (Trinity College Dublin)

Status: On-going

Academic Productivity: How to Define It? How to Measure It? How to Boost It ?

Partnered with Trinity College Dublin, Ireland

Two decades ago Peter Drucker highlighted the differences between knowledge workers and physical workers when it comes to defining and measuring productivity. Students are essentially knowledge workers but in the meantime they have unique characteristics that distinguish them from office workers. How to define and measure academic productivity is an unexplored research area largely because the significance of productivity hasn't been recognized in academia. This study aims to fill in the gap by developing mobile computing technologies to support the tracking, analysis, reflection and improvement of academic productivity. 

PI: Dr. Zilu Liang

Co-PI: Dr. Mario Alberto Chapa-Martell (Silver Egg)

Status: In-preparation

Time and Sequence Matter: Sensing, Modelling and Mining Circadian Rhythm

Highly Innovative

Everyone has an inner clock that governs the functioning of the body at the molecular, cellular, and systems level. Deviation from this inner clock will overdraft the energy cycle of the body and lead to physical and mental diseases in the long run. This study explores the feasibility of leveraging ubiquitous and wearable computing technology for automatic sensing and personalized modelling of circadian rhythm. We will especially focus on the modelling of circadian rhythm debt (i.e. the disparity between the ideal circadian rhythm of a person and  the circadian rhythm entrained by social schedule and lifestyle).

PI: Dr. Zilu Liang

Co-PI: Dr. Mario Alberto Chapa-Martell (Silver Egg)

           Dr. Satoshi Nishimura (産総研)

           Dr. Takuichi Nishimura (産総研)

Status: On-going

LessonSpectrum: Technology Augmented Lesson Planning, Tracking and Reflection

Partnered with AIST

Effective teaching involves well-planned instructional design and well-managed in-class orchestration. Nevertheless, these two components have been routinely considered as two separate domains in educational research. This study aims to bring together these two interleaved components of effective teaching by designing and developing computing technology to support the planning, tracking, analysis and reflection of teaching. 

PI: Dr. Zilu Liang

Co-PI: Ms Oraphan Tatha (University of Tokyo)

Status: On-going

Quantifying, Measuring and Coping with Academic Stress

Partnered with University of Tokyo

University students have millions of things to worry about, but there is a lack of understanding of the main sources of academic stress and how they evolve throughout the course of university life? Are there differences between local and international students in terms of stress trigger and stress coping strategy? This study aims to first establish a psychometric instrument for measuring academic stress, and then use this instrument to probe the dynamics of stress trigger and stress coping among university students.   

PI: Dr. Zilu Liang

Co-PI: Prof. James Bailey (University of Melbourne)

           Prof. Lars Kulik (University of Melbourne)

           Dr. Bernd Ploderer (QUT)

           Dr. Wanyu Liu (IRCAM Centre Pompidou)

           Dr. Yuxuan Li (University of Melbourne)

Status: Completed

SleepExplorer: Exploring Correlations between Personal Sleep Data and Contextual Factors

Funded by Endeavor Research Fellowship

Partnered with University of Melbourne, Australia

Getting enough quality sleep is a key part of a healthy lifestyle. Many people are tracking their sleep through mobile and wearable devices, together with contextual information that may influence sleep quality, like exercise, diet, and stress. However, there is limited support to help people make sense of this wealth of data. This study bridges this gap between sleep-tracking and sense-making through the design of SleepExplorer, a web application that helps individuals understand sleep quality through multi-dimensional sleep structure and explore correlations between sleep data and contextual information. 

PI: Dr. Zilu Liang

Co-PI: Dr. Mario Alberto Chapa-Martell (Silver Egg)

Status: In-preparation

Understanding How We Learn and How We Can Learn Better: In-Class Psycho-Physical Sensing for Personalized Education

Highly Innovative

Prof Sanna Järvelä's "Big Data From the Little Person? Using Multimodal Data to Understand the Regulation of Learning" marked the beginning of a new era where students' in-class psycho-physical reactions to the instructions of a teacher are measured and leveraged to understand self-regulated learning. This exciting new domain of learning analytics demands further research endeavors to address challenges surrounding data collection, information retrieval, data integration, data analytics and human-computer interaction. This study focuses on establishing a multimodal data management platform for in-class psycho-physical sensing and analysis, with a focus on modelling the interpersonal differences in learning.