Browsing by Author "Gamberini, Luciano"

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Designing for Mixed Reality Urban Exploration
(2021) Andolina, Salvatore; Hsieh, Yi Ta; Kalkofen, Denis; Nurminen, Antti; Cabral, Diogo; Spagnolli, Anna; Gamberini, Luciano; Morrison, Ann; Schmalstieg, Dieter; Jacucci, Giulio
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This paper introduces a design framework for mixed reality urban exploration (MRUE), based on a concrete implementation in a historical city. The framework integrates different modalities, such as virtual reality (VR), augmented reality (AR), and haptics-audio interfaces, as well as advanced features such as personalized recommendations, social exploration, and itinerary management. It permits to address a number of concerns regarding information overload, safety, and quality of the experience, which are not sufficiently tackled in traditional non-integrated approaches. This study presents an integrated mobile platform built on top of this framework and reflects on the lessons learned.
Digital Me
(2017) Sjöberg, Mats; Chen, Hung-Han; Floreen, Patrik; Koskela, Markus; Kuikkaniemi, Kai; Lehtiniemi, Tuukka; Peltonen, Jaakko
A4 Artikkeli konferenssijulkaisussa
Our lives are getting increasingly digital; much of our personal interactions are digitally mediated. A side effect of this is a growing digital footprint, as every action is logged and stored. This data can be very powerful, e.g., a person’s actions can be predicted, and deeply personal information mined. Hence, the question of who controls the digital footprint is becoming a pressing technological and social issue. We believe that the solution lies in human-centric personal data, i.e., the individuals themselves should control their own data. We claim that in order for human-centric data management to work, the individual must be supported in understanding their data. This paper introduces a personal data storage system Digital Me (DiMe). We describe the design and implementation of DiMe, and how we use state-of-the-art machine learning for visualisation and interactive modelling of the personal data. We outline several applications that can be built on top of DiMe.
Integrating neurophysiologic relevance feedback in intent modeling for information retrieval
(2019-03-12) Jacucci, Giulio; Barral, Oswald; Daee, Pedram; Wenzel, Markus; Serim, Baris; Ruotsalo, Tuukka; Pluchino, Patrik; Freeman, Jonathan; Gamberini, Luciano; Kaski, Samuel; Blankertz, Benjamin
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).