Statistical machine learning algorithm for early detection of infection using data from consumer wearables.

Prof. Siobhan Banks & Dr. Linda Grosser

Organization

University of South Australia, Australia.

Team

Prof. Siobhan Banks

Dr. Linda Grosser

Project Description & Objectives

Apply statistical machine learning to validate unified study design and analysis approaches to generate an algorithm that can be applied to data from off-the-shelf, consumer wearables for early detection of a modelled immune response that precedes active infection.

Data Collection Process

Participants collected and wore the Garmin Venu Sq 2 for 14 days. Participants filled out a daily questionnaire about their subjective state, activities, food, health etc.

On day 11 participants received a vaccination. Participants returned device to the lab on day-14.

Fitrockr Utilization

Fitrockr was used to obtain the raw data for the health variables of interest collected by the Garmin device. Additionally, it assisted in monitoring participants to ensure the device was synced daily.

Wearable Used

Garmin Venu Sq 2

Number of Participants

106

Duration

5 months

Metrics Collected

Steps

Heart Rate

BBI

HRV

Skin Temperature