The research was carried out in the field of behavioural analysis, with the aim of exploiting and re-elaborating different types of data, offering the basis for new developments and methodologies within a given scenario. The analysis of behaviour has been addressed in 3 real-life scenarios. In Ambient Assisted Living (AAL) has been created an architecture based on the "Zabbix" platform that can monitor and collect data from different smart objects in a home automation house. In this context, the main objective was to exploit the data collected to define and outline anomalous behaviours of elderly people in their homes, implementing an intelligent system based on machine learning algorithms capable of triggering alarms; an innovative aspect of this system is the ability to cross alarms from multiple SOs ensuring a decrease in false positives. The project involved 16 companies and 2 universities. In Industry 4.0, an application has been developed for augmented reality, usable through smart glasses and aimed at training on the job. The project stems from the collaboration of Intermac, a company specialized in stone, glass and metal processing. The goal is to train the operator in carrying out a task by means of 3D animations and informative labels. Considerable attention has been paid to the study of wearable devices' usability because it is one of the main critical aspects of this technology. In the field of E-commerce, a study has been conducted on the navigation data of users who have made purchases within the e-commerce website. These data, suitably filtered and grouped in navigation sessions, allow to train neural networks (Long Short Term Memory networks – usually just called “LSTMs") in the recognition of action sequences. The main objective is to improve the user's purchasing process by proposing products based on the operations carried out on the e-commerce portal. The work presented in this thesis was made possible thanks to the collaboration with the IT company Apra Spa which co-financed the doctorate with a EUREKA scholarship.
La ricerca è stata condotta nell’ambito dell’analisi dei comportamenti, con l’obiettivo di sfruttare e rielaborare differenti tipologie di dati, offrendo le basi per nuovi sviluppi e metodologie all’interno di un determinato scenario. L’analisi del comportamento è stato affrontato in 3 scenari reali: In ambito AAL è stata creata un’architettura basata sulla piattaforma “Zabbix” in grado di monitorare e raccogliere dati provenienti da numerosi smart objects in una casa domotica. In questo ambito l’obiettivo principale è stato quello di sfruttare i dati raccolti per definire e delineare dei comportamenti anomali di persone anziane all’interno della loro abitazione, implementando un sistema intelligente basato su algoritmi di machine learning in grado di far scattare allarmi; un aspetto innovativo di questo sistema è la capacità di incrociare allarmi provenienti da più SOs garantendo una diminuzione di falsi positivi. Il progetto ha coinvolto 16 aziende e 2 università. Nell’ambito Industry 4.0 è stata sviluppata un’applicazione per la realtà aumentata fruibile attraverso smart glasses e finalizzata al training on the job. Il progetto nasce dalla collaborazione dell’azienda Intermac, specializzata nella lavorazione di pietra, vetro e metallo. L’obiettivo è di addestrare l’operatore nello svolgimento di un task mediante animazioni 3d e labels informative. E’ stata posta notevole attenzione allo studio dell’usabilità di dispositivi wearable perché rappresenta una delle principali criticità di questa tecnologia. Nell’ambito E-commerce è stato condotto lo studio sui dati di navigazione degli utenti che hanno effettuato acquisti all’interno dell’e-commerce. Questi dati, opportunamente filtrati e accorpati in sessioni di navigazione, consentono di addestrare reti neurali (LSTM) nel riconoscimento di sequenze di azioni. L’obiettivo principale è quello di migliorare il processo di acquisto dell’utente proponendo prodotti sulla base delle operazioni svolte sul portale e-commerce. Il lavoro presentato in questa tesi è stato reso possibile grazie alla collaborazione con la società informatica Apra Spa che ha cofinanziato il dottorato con una borsa di studio EUREKA.
Data exploitation at different levels for Behaviour Analysis in real-world scenarios / Pollini, Rama. - (2018 Mar 27).
Data exploitation at different levels for Behaviour Analysis in real-world scenarios
POLLINI, RAMA
2018-03-27
Abstract
The research was carried out in the field of behavioural analysis, with the aim of exploiting and re-elaborating different types of data, offering the basis for new developments and methodologies within a given scenario. The analysis of behaviour has been addressed in 3 real-life scenarios. In Ambient Assisted Living (AAL) has been created an architecture based on the "Zabbix" platform that can monitor and collect data from different smart objects in a home automation house. In this context, the main objective was to exploit the data collected to define and outline anomalous behaviours of elderly people in their homes, implementing an intelligent system based on machine learning algorithms capable of triggering alarms; an innovative aspect of this system is the ability to cross alarms from multiple SOs ensuring a decrease in false positives. The project involved 16 companies and 2 universities. In Industry 4.0, an application has been developed for augmented reality, usable through smart glasses and aimed at training on the job. The project stems from the collaboration of Intermac, a company specialized in stone, glass and metal processing. The goal is to train the operator in carrying out a task by means of 3D animations and informative labels. Considerable attention has been paid to the study of wearable devices' usability because it is one of the main critical aspects of this technology. In the field of E-commerce, a study has been conducted on the navigation data of users who have made purchases within the e-commerce website. These data, suitably filtered and grouped in navigation sessions, allow to train neural networks (Long Short Term Memory networks – usually just called “LSTMs") in the recognition of action sequences. The main objective is to improve the user's purchasing process by proposing products based on the operations carried out on the e-commerce portal. The work presented in this thesis was made possible thanks to the collaboration with the IT company Apra Spa which co-financed the doctorate with a EUREKA scholarship.File | Dimensione | Formato | |
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