Data Pipeline of Artificial Intelligence Applications

The overarching goal of this project is to research the feasibility of a low-cost, general purpose data pipeline that can learn and adapt to large, varied and dynamic data sets. Specifically, we will utilize a large sample of 3D gait and motion data captured from a cohort of research participants with dementia or mild cognitive impairment during an intervention study. Main objectives of the proposed research project are to explore the feasibility of a low-cost data pipeline that is capable of learning and adapting to varied and dynamic data through a survey of the state-of-the-art of the field. Additionally, we aim to develop a novel and innovative, low-cost pipeline using state-of-the-art machine learning algorithms to perform automated mobility and balance analysis in dementia or mild cognitive impairment.

Faculty Supervisor:

Stephen Czarnuch

Student:

Isaac Adejuwon

Partner:

Metricsflow

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

Program: