Interferenze. Riattivazione dell'ex carcere Sant'Agata a Bergamo

LAUREA MAGISTRALEIl progetto “Interferenze” nasce dalla necessità del comune di Bergamo di riqualificare e quindi di dare una nuova identità all’area dell’ex carcere di Sant’Agata. Partendo dal recupero dell’architettura storica, l’intento è quello di creare un nuovo palcoscenico per le arti performative, che porti alla riattivazione di una delle strutture più interessanti del centro storico. Tramite questo progetto si vuole trasformare uno spazio abbandonato, all’interno delle mura della città vecchia, in un polo catalizzatore. La volontà di partenza è quella di destinare gli spazi a centri di ricerca e di produzione nel campo teatrale, rimanendo legati alla tradizione artistica della città. In un’epoca dove la tecnologia ed i nuovi media sono alla base della nostra società, il mezzo per raggiungere questo obiettivo sembra proprio essere il digitale. L’idea nasce dal concetto di interferenza, con questo termine si indica il momento in cui un segnale di disturbo altera la percezione di un segnale trasmesso, trasformandolo in un segnale distorto; allo stesso modo questo progetto vuole indagare come il teatro tradizionale interagisce con le nuove tecnologie, considerandole come l’agente disturbante. Il teatro applicato a queste nuove frontiere digitali rimane però ancora oggi un enigma da risolvere, un panorama in cui tanti sono stati i tentativi di definire questa disciplina artistica emergente, attraverso numerosi spettacoli innovativi ed eterogenei, ma che facilmente tendono a sconfinare in altri campi performativi di appartenenza. Per fare chiarezza su questo tema la mia indagine si è concentrata sulla suddivisione del teatro in tutte le sue componenti e l’analisi caso per caso, cercando di capire come abbia risposto a questo sviluppo tecnologico. Scenografie, attori e costumi digitali sono le tre macrocategorie prese in considerazione per cercare di definire la trasformazione del teatro tradizionale dopo la “rivoluzione digitale” avvenuta negli ultimi decenni. Il risultato di questa ricerca si concretizza in una mostra interattiva in cui il penitenziario diventa uno spazio adibito al racconto dello scenario digitale teatrale. Le varie celle aiutano a scoprire gli approfondimenti monotematici lungo il percorso. La mostra si trasforma in un’esperienza immersiva, enfatizzata dalla realtà virtuale, che amplifica non solo la capacità di coinvolgimento dei visitatori, ma anche la quantità di informazioni esposte. Al centro di questo edificio, infine, ho progettato un’architettura parassita che, al suo interno, si declina sia come padiglione espositivo, grazie alle sue pareti dinamiche, sia come scenario perfetto per la produzione di spettacoli teatrali digitali

AI-Based Early Change Detection in Smart Living Environments

In the smart environments we live today, a great variety of heterogeneous sensors are being increasingly deployed with the aim of providing more and more value-added services. This huge availability of sensor data, together with emerging Artificial Intelligence (AI) methods for Big Data analytics, can yield a wide array of actionable insights to help older adults continue to live independently with minimal support of caregivers. In this regard, there is a growing demand for technological solutions able to monitor human activities and vital signs in order to early detect abnormal conditions, avoiding the caregivers’ daily check of the care recipient. The aim of this study is to compare state-of-the-art machine and deep learning techniques suitable for detecting early changes in human behavior. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, and vital signs. The achieved results demonstrate the superiority of unsupervised deep-learning techniques over traditional supervised/semi-supervised ones in terms of detection accuracy and lead-time of prediction.</jats:p

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

Predicting change from multivariate time series has relevant applications ranging from the medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aimed to predict changes in one’s behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a temporal convolutional network, and the behavioral state was predicted through bidirectional long short-term memory auto-encoder, operating jointly. From the comparison with the state of the art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.</jats:p

AI-Based Early Change Detection in Smart Living Environments

In the smart environments we live today, a great variety of heterogeneous sensors are being increasingly deployed with the aim of providing more and more value-added services. This huge availability of sensor data, together with emerging Artificial Intelligence (AI) methods for Big Data analytics, can yield a wide array of actionable insights to help older adults continue to live independently with minimal support of caregivers. In this regard, there is a growing demand for technological solutions able to monitor human activities and vital signs in order to early detect abnormal conditions, avoiding the caregivers&rsquo; daily check of the care recipient. The aim of this study is to compare state-of-the-art machine and deep learning techniques suitable for detecting early changes in human behavior. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, and vital signs. The achieved results demonstrate the superiority of unsupervised deep-learning techniques over traditional supervised/semi-supervised ones in terms of detection accuracy and lead-time of prediction

Context-Aware AAL Services through a 3D Sensor-Based Platform

The main goal of Ambient Assisted Living solutions is to provide assistive technologies and services in smart environments allowing elderly people to have high quality of life. Since 3D sensing technologies are increasingly investigated as monitoring solution able to outperform traditional approaches, in this work a noninvasive monitoring platform based on 3D sensors is presented providing a wide-range solution suitable in several assisted living scenarios. Detector nodes are managed by low-power embedded PCs in order to process 3D streams and extract postural features related to person’s activities. The feature level of details is tuned in accordance with the current context in order to save bandwidth and computational resources. The platform architecture is conceived as a modular system suitable to be integrated into third-party middleware to provide monitoring functionalities in several scenarios. The event detection capabilities were validated by using both synthetic and real datasets collected in controlled and real-home environments. Results show the soundness of the presented solution to adapt to different application requirements, by correctly detecting events related to four relevant AAL services