The present PhD research explores the integration of vision devices and intelligent systems to monitor and enhance human well-being in healthcare and manufacturing contexts, starting from the standards proposed in Industry 4.0 and aiming to follow the principles of the novel Industry 5.0. The Microsoft Azure Kinect, a state-of-the-art depth sensor, has been selected as a key instrument for data collection, and innovative camera calibration methods have been developed to ensure the accuracy and reliability of the gathered data. The main goal of the present study is to evaluate the efectiveness of machine learning and deep learning models for mobility assessment and action segmentation, to determine their suitability for human monitoring.

Vision devices and intelligent systems for monitoring the well-being of humans in healthcare and manufacturing

Romeo, Laura
2024

Abstract

The present PhD research explores the integration of vision devices and intelligent systems to monitor and enhance human well-being in healthcare and manufacturing contexts, starting from the standards proposed in Industry 4.0 and aiming to follow the principles of the novel Industry 5.0. The Microsoft Azure Kinect, a state-of-the-art depth sensor, has been selected as a key instrument for data collection, and innovative camera calibration methods have been developed to ensure the accuracy and reliability of the gathered data. The main goal of the present study is to evaluate the efectiveness of machine learning and deep learning models for mobility assessment and action segmentation, to determine their suitability for human monitoring.
2024
Inglese
Perri, Anna Gina
Ciminelli, Caterina
Politecnico di Bari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/64925
Il codice NBN di questa tesi è URN:NBN:IT:POLIBA-64925