Università degli studi di Trento
Measurement technologies to enhance human capabilities via Mixed Reality
Abstract
dc:descriptionIn the relentless pursuit of human progress, technological innovations have always played a key role in shaping our society. In recent years, the convergence of innovative measurement technologies with Mixed Reality (MR) has emerged as a groundbreaking paradigm, offering transformative solutions to enhance individuals across multiple domains. This dissertation focuses on MR-based applications designed and optimized to restore human centrality, fostering advances in healthcare, education, and industry. The primary purpose is to provide end users with the best tools to enhance their perception-action loop in work or education, empowering them to better achieve and control their final goals. Through the synergy between immersive visualization technologies and a framework based on innovative measurement systems, unique environments are created to enhance end users at different levels of the perception-action loop, leading to improved outcomes and overall well-being. Measurement technologies include three-dimensional cameras, wearable sensors, inertial sensors, thermal cameras, and pressure matrices. Many challenges were overcome in this dissertation, such as designing and testing the proper measurement frame and interface, finding new calibration procedures for measurement systems, and developing original data processing techniques in computer vision and machine learning.
Degree
thesis:*- Grantor dc:publisher
- Università degli studi di Trento
- Year dc:date
- 2023
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Luchetti, Alessandro
- Contributors dc:contributor
-
- De Cecco, Mariolino
- Bosetti, Paolo
- Del Prete, Andrea
Subjects
dc:subject × 1Rights
dc:rights- Statement dc:rights
-
- info:eu-repo/semantics/openAccess
- license:Tutti i diritti riservati (All rights reserved)
- license uri:iris.PRI01
- Language dc:language
- eng
Identifiers
dc:identifier.*- OAI identifier oai:identifier
- oai:iris.unitn.it:11572/398049