​Developing Accurate Home Sleep Tracking Technology


Study Level

Visiting researcher, postdoc, PhD, master, undergraduate, intern, exchange/visiting student




Overview

Getting enough quality sleep is a key part of a healthy lifestyle. 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.


Research Activities

This research project contains the following two tracks


Track 1: developing new sleep staging algorithms with consumer wearables

This track may involve the following research activities:

  • systematic literature reviews to identify the merits and demerits of existing sleep-tracking technology

  • design data collection protocol and conduct longitudinal data collection experiment

  • retrieve and preprocess physiological data collected using a variety of wearable and mobile devices

  • design and evaluate new sleep staging algorithms

  • design sleep data visualization and perform usability evaluations

  • co-author research papers and give presentations in academic conferences


Track 2: human-computer interaction in sleep tracking

This track may involve the following research activities:

  • systematic literature reviews to identify design challenges and opportunities in sleep tracking

  • interviews, surveys, and co-design workshops with people with sleep disorders such as insomnia and sleep apnea

  • design intuitive and user-friendly sleep data visualization and perform user evaluations

  • design and develop new sleep-tracking technology to support good sleep hygiene

  • field trials to investigate the impact on behavioral change of sleep tracking technology

  • co-author research papers and give presentations in academic conferences


Outcomes

Upon conclusion of this research, you will gain:

  • experience in designing and conducting data collection experiments with human subjects

  • skills in data engineering and data science

  • experience in human-computer interaction and user-centered design

  • expertise in mobile health

  • domain knowledge in sleep science

  • skills in project management and technical communication


Skills and Experience

As the ideal candidate, you'll have a passion for improving sleep health and a strong background in (for track 1) machine learning and programming using Python/R or (for track 2) human computer interaction or user-centered design.


Contributors

  • Prof. Bernd Ploderer (Queensland University of Technology, Australia)

  • Dr. Mario Alberto Chapa-Martell (Silver Egg Technology, Japan)

  • Prof. James Bailey (University of Melbourne, Australia)

  • Prof. Lars Kulik (University of Melbourne, Australia)

  • Dr. Wanyu Liu (IRCAM Centre Pompidou, France)

  • Dr. Yuxuan Li (University of Melbourne, Australia)


Selected Publications

#sleepTracking #HCI #quantifiedSelf #smartWatches #machineLearning #Fitbit #credibility

© 2020 by Dr Zilu Liang.