Anna Spagnolli

Prof. Dr. Anna Spagnolli

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Research Focus & Metrics

Academic Career Length

24Years (2003-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 September 2020 in Journal of Medical Internet Research
Verified
Journal Article
Open access
FMFabio Masina
Fabio Masina
VOValeria Orso
Valeria Orso
PPPatrik Pluchino
Patrik Pluchino
GDGiulia Dainese
Giulia Dainese
SVStefania Volpato
Stefania Volpato
LGLuciano Gamberini
Luciano Gamberini
Background: Voice assistants allow users to control appliances and functions of a smart home by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants’ actual motor, linguistic, and cognitive abilities. Objective: The aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home). Methods: A group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable of accounting for an efficient interaction with the voice assistant was investigated by performing multiple linear regression models. The best model was identified by adopting a selection strategy based on the Akaike information criterion (AIC). Results: For users with disabilities, the effectiveness of interacting with a voice assistant is predicted by the Mini-Mental State Examination (MMSE) and the Robertson Dysarthria Profile (specifically, the ability to repeat sentences), as the best model shows (AIC=130.11). Conclusions: Users with motor, linguistic, and cognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities.

6 December 2014
Verified
Book Chapter
Closed access
PDF file
ASAnna Spagnolli
Anna Spagnolli
EGEnrico Guardigli
Enrico Guardigli
VOValeria Orso
Valeria Orso
AVAlessandra Varotto
Alessandra Varotto
LGLuciano Gamberini
Luciano Gamberini

16 June 2012 in Transportation
Verified
Journal Article
Closed access
PDF file
LGLuciano Gamberini
Luciano Gamberini
ASAnna Spagnolli
Anna Spagnolli
AMAndrea Miotto
Andrea Miotto
EFEva Ferrari
Eva Ferrari
NCNicola Corradi
Nicola Corradi
SFSarah Furlan
Sarah Furlan

31 December 2003 in International Journal of Human-Computer Studies
Verified
Journal Article
Closed access
ASAnna Spagnolli
Anna Spagnolli
DVDiego Varotto
Diego Varotto
GMGiuseppe Mantovani
Giuseppe Mantovani

7 January 2014 in Virtual Reality
Verified
Journal Article
Closed access
PDF file
ASAnna Spagnolli
Anna Spagnolli
CBCheryl Campanella Bracken
Cheryl Campanella Bracken
VOValeria Orso
Valeria Orso

20 October 2011 in Personal and Ubiquitous Computing
Verified
Journal Article
Closed access
PDF file
LGLuciano Gamberini
Luciano Gamberini
ASAnna Spagnolli
Anna Spagnolli
LPLisa Prontu
Lisa Prontu
SFSarah Furlan
Sarah Furlan
FMFrancesco Martino
Francesco Martino
JLJosè Antonio Lozano
Josè Antonio Lozano