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  KEYWORD

Human activity recognition in Parkinson's disease using wearable sensors and artificial intelligence

Parole chiave MACHINE LEARNING, PARKINSON'S DISEASE, PATTERN RECOGNITION, SIGNAL PROCESSING, WEARABLE DEVICES

Riferimenti LUIGI BORZI', GABRIELLA OLMO

Gruppi di ricerca SYSTEM BIOLOGY GROUP - SYSBIO

Tipo tesi EXPERIMENTAL APPLIED

Descrizione The thesis focuses on the labelling and analysis of a large dataset of inertial signals collected from people with Parkinson's disease.
Data recorded from a body-worn smartphone will be labelled to identify common activities of daily living.
Classic machine learning or deep learning algorithms will be optimized and validated to accurately recognize human activities.

Conoscenze richieste - Matlab or Python programming
- Signal processing
- Knowledge of machine and deep learning pipelines


Scadenza validita proposta 21/10/2024      PROPONI LA TUA CANDIDATURA




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