View Proposal

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Proposer

Marta Vallejo

Title

Multimodal Deep Learning diagnosis applied to clinical assessments in Parkinson’s disease - Collaboration with York University

Goal

Gaining understanding of real research of neurodegenerative diseases using deep/machine learning. A final journal/conference publication if results are of enough quality.

Description

Parkinson’s Disease (PD) is a neurodegenerative disease of high incidence in the ageing population. This project aims at the application of deep learning technologies to a clinical dataset that contains information on patients with prodromal or early-stage PD. By analysing and processing digitalised movement data captured by three standard clinical assessments, the classifier will be expected to characterise bradykinesia, a slowing of movement, which is the fundamental motor feature of PD. The complex nature of bradykinesia makes it difficult to reliably identify it, particularly at the early stages of the disease (Ahlrichs and Lawo, 2013). The types of clinical assessments used in this study are the following: * Finger tapping * Hand pronation-supination * Hand opening-closing * Hand movements measured by accelerometers

Resources

Real datasets used that have been collected in clinical settings.

Background

Interest in the topic and techniques. Good programming skills. Willingness to learn

Url

Difficulty Level

High

Ethical Approval

Full

Number Of Students

2

Supervisor

Marta Vallejo

Keywords

machine learning, deep learning, sensor data

Degrees

Bachelor of Science in Computer Science
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Computing (2 Years)
Master of Science in Data Science
Master of Science in Robotics
Master of Science in Software Engineering
Bachelor of Science in Computing Science
Bachelor of Engineering in Robotics
Bachelor of Science in Statistical Data Science