| KEYWORD |
Area Engineering
Analysing the generalisability of deep learning algorithms for motion analysis
keywords ARTIFICIAL INTELLIGENCE, DEEP LEARNING, WEARABLE DEVICES
Reference persons LUIGI BORZI'
Research Groups DAUIN - GR-24 - reSilient coMputer archItectures and LIfE Sci - SMILIES
Thesis type EXPERIMENTAL APPLIED
Description Numerous deep learning algorithms have been proposed for motion analysis, achieving promising performance.
Currently, their ability to generalise to new scenarios, circumstances, environments and populations remains poorly explored.
This thesis aims to develop deep learning algorithms for motion analysis, analyse their ability to generalise, investigate methods to optimise them, and develop dynamic and robust algorithms that can perform in different circumstances.
Required skills artificial intelligence; deep learning; signal processing
Deadline 19/09/2025
PROPONI LA TUA CANDIDATURA