Artificial intelligence for the prediction of variables in health

Today, thanks to microelectronics, it is possible to find technology that continuously collects data such as motion kinematics or physiological data. This type of technology is commonly referred to as wearable and therefore the scope of this project is to find methodologies based on artificial intelligence for the creation of models that can predict a variable of interest. These predictive models are of interest in sports and in general in human health that will allow to follow up the physical condition of a person, estimate the appearance of an injury or determine variables of interest without the need to invest in high technology, among other advantages. The objectives are: develop a software interface that allows the capture of kinematic, physiologic and performance variables from wearable sensors, conceive a software based on machine learning that allows the prediction of kinematic, physiologic and performance variables from data collected and evaluate the robustness and precision of different machine learning techniques.

Faculty Supervisor:

Frédéric Domingue

Student:

Partner:

Universidad EIA

Discipline:

Computer science

Sector:

Artificial Intelligence; Technology

University:

Université du Québec à Trois-Rivières

Program: