| KEYWORD |
Assessing the relevance of contextual information for physiological data modeling (wearables)
Thesis in external company 
Thesis abroad
keywords PHYSIOLOGICAL DATA, SMART RINGS, SMARTWATCH, WEARABLES
Reference persons VALENTINA AGOSTINI
External reference persons Dr. Francesca Dalia Faraci, SUPSI (Lugano), Switzerland
Research Groups Biolab: Ingegneria Biomedica
Description Context
Physiological data collected in the wild from wearable sensors (smartwatches, smart rings, …) are often accompanied by contextual information, that can come in many forms: structured and standardized questionnaires, ecological momentary assessments (EMA), events loggers, and so on. Modeling physiological data together with this accompanying information may provide better insights and improved results.
Commercial products, such as the Whoop 4.0, already make us of this feature. Whoop allows users to log their behaviors and events occurring in their life, and show insights on the impact of these events and behaviors on the recovery of the users, thus allowing them to understand if that behavior has a positive or negative effect on them
In our research, we collect data from wearable sensors (mostly Garmin) and we want to explore the possibility of building personalized algorithms linking objective data (i.e., physiological data from wearable sensors) to subjective data (i.e., answers to questionnaires, EMA, ..).
Objective
In this project you will:
1.
Get acquainted with datasets containing both data from wearable sensors and contextual information, such as the Lifesnaps dataset, which is an open dataset composed of data from Fitbit devices, questionnaires, and EMA collected in the wild from ~70 people for one month
2.
Develop an automatic pipeline for the extraction of features from such datasets
3.
Explore the modeling of physiological data taking into consideration the contextual information that is provided together with them
The final aim is to build an algorithm that is able to state the effect of the context on the recovery of the users (assessed with physiological data).
Required skills Prospective candidates should have:
• good level of English
•knowledge of statistics/machine learning
•critical thinking
•basic programming in Python/R
Deadline 16/11/2024
PROPONI LA TUA CANDIDATURA