Collaborative Research Team Project # 22
Advancing Statistical Methods for the Analysis of Complex Biologging Data Collected from Humans and Animals
Technological advancements have revolutionized how we study and monitor the movement, behaviour, and health of humans and animals in their free-living conditions.
Although siloed in their respective disciplines, medical and ecological researchers both collect similar biologging data, and their studies often share common analytical goals and challenges.
Why Biologging Data?
Detecting underlying behaviours is an important goal of analyses of biologging data.
In medical sciences, biologging data are now being used to identify changes in heart rate variability and its association with mood changes in bipolar disorder.
Statistically, this goal can be translated into connecting observations to a finite set of latent states, with the additional challenge that biologging data typically exhibits high temporal dependence, such that observations close in time to one another are likely to be a product of the same underlying latent state.
Research Team’s Goals
- Using labelled data to improve state predictions
- Improving realism through the incorporation of feedback mechanisms
- Accounting for individuality in population inference
- Inference, identifiability, and model validation
People Behind the Project
Vianey Leos Barajas | University of Toronto
Marie Auger-Méthé | University of British Columbia
Joanna Mills Flemming | Dalhousie University
Nancy Heckman | University of British Columbia
Alán Aspuru-Guzik | University of Toronto
Catalina Gomez Salazar | Fisheries and Oceans Canada
Nigel Hussey | University of Windsor
Shelley Lang | Fisheries and Oceans Canada
Marianne Marcoux | Fisheries and Oceans Canada
Juan Morales | Universidad Nacional del Comahue
Abigail Ortiz | University of Toronto
Yannis Papastamatiou | Florida International University
Andrew Trites | The University of British Columbia
Fisheries and Oceans Canada
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Sports Analytics is a Collaborative Research Team project. This program tackles complex problems through a three-year research and training agenda.
CANSSI offers approximately $200,000 for this type of project, which requires a team of faculty, postdocs, and students.