| KEYWORD |
Automatic recognition of motor fluctuations in Parkinson's disease using wearable sensors and machine learning
keywords MACHINE LEARNING, PARKINSON'S DISEASE, SIGNAL PROCESSING, WEARABLE DEVICES
Reference persons LUIGI BORZI'
Research Groups DAUIN - GR-24 - reSilient coMputer archItectures and LIfE Sci - SMILIES
Thesis type EXPERIMENTAL APPLIED
Description Parkinson's disease is the second most common neurodegenerative disease. Various motor symptoms occur as the disease progresses. Currently available pharmacological treatments can reduce symptoms and improve quality of life. At the same time, patients experience motor fluctuations, going from periods of increased therapy efficacy and thus excellent symptom control, to times when the therapy effect ends and symptoms reappear.
This thesis aims to automatically recognise motor fluctuations using wearable sensors.
Inertial data from a large sample of patients were recorded using a combination of wearable sensors placed in different body segments.
Data were collected in the laboratory, during specific motor tests, both in the presence and absence of therapy.
The student will analyse the available data, process the signals recorded by the different sensors and develop artificial intelligence algorithms.
Required skills signal processing; machine learning; biomechanics
Deadline 19/09/2025
PROPONI LA TUA CANDIDATURA