Status: On-going
Study Level
Visiting researcher, postdoc, PhD, master, undergraduate, intern, exchange/visiting student
Overview
Chronic metabolic diseases such as diabetes are increasingly prevalent in modern society. While traditional interventions focus dominantly on the control of “what to eat”, a recent study on mice found that restricting the timing of eating prevents obesity and metabolic syndrome, suggesting a potential role of “when to eat” in metabolic regulation [1]. This gives hint to the development of new mobile health technologies that support the management and prevention of metabolic diseases such as diabetes by helping users to track and to adjust mealtime rhythm. However, it is often difficult for people to keep tracking the time of each meal from day to day —a phenomena known as tracking fatigue. This project aims to develop algorithms for automatic detection and modelling of mealtime rhythm based on bio-signals that can be readily measured with wearable devices such as activity trackers, continuous glucose monitoring sensors and hearables.
[1] A Chaix, T Lin, HD Le, et al. (2019) Time-restricted feeding prevents obesity and metabolic syndrome in mice lacking a circadian clock. Cell Metabolism 28:303-319.
Research activities
This research project may involve the following activities:
systematic literature reviews to identify the merits and demerits of existing meal-detection algorithms and systems
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 meal-detection algorithms and systems
model meal activity cycle
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
expertise in activity recognition and mobile health
domain knowledge in glucose metabolism
skills in project management and technical communication.
Skills and experience
As the ideal candidate, you'll have a passion for improving health outcomes and a strong background in machine learning/signal processing and programming using Python or R.
Contributors
Ms. Lauriane Bertrand (exchange student from INP-ENSEEIHT, France)
Mr. Nathan Cleyet-Marrel (exchange student from INP-ENSEEIHT, France)
Dr. Mario Alberto Chapa-Martell (Silver Egg Technology, Japan)
Publications
Conferences proceedings
Bertrand L, Cleyet-Marrel N, Liang Z. (2021) Recognizing eating activities in free-living environment using consumer wearable devices. In Proceedings of the 8th International Symposium on Sensor Science, online.
Bertrand L, Cleyet-Marrel N, Liang Z. (2021) The role of continuous glucose monitoring (CGM) in automatic detection of eating activities. In Proceedings of the IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021), Nara, Japan.
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