Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study
BackgroundVoice 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 doo...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2020-09-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | http://www.jmir.org/2020/9/e18431/ |
| _version_ | 1818402473521446912 |
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| author | Masina, Fabio Orso, Valeria Pluchino, Patrik Dainese, Giulia Volpato, Stefania Nelini, Cristian Mapelli, Daniela Spagnolli, Anna Gamberini, Luciano |
| author_facet | Masina, Fabio Orso, Valeria Pluchino, Patrik Dainese, Giulia Volpato, Stefania Nelini, Cristian Mapelli, Daniela Spagnolli, Anna Gamberini, Luciano |
| author_sort | Masina, Fabio |
| collection | DOAJ |
| description | BackgroundVoice 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.
ObjectiveThe 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).
MethodsA 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).
ResultsFor 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).
ConclusionsUsers 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. |
| first_indexed | 2024-12-14T08:08:55Z |
| format | Article |
| id | doaj.art-081a3f725bf34df58c8061314af68445 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-12-14T08:08:55Z |
| publishDate | 2020-09-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-081a3f725bf34df58c8061314af684452022-12-21T23:10:06ZengJMIR PublicationsJournal of Medical Internet Research1438-88712020-09-01229e1843110.2196/18431Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods StudyMasina, FabioOrso, ValeriaPluchino, PatrikDainese, GiuliaVolpato, StefaniaNelini, CristianMapelli, DanielaSpagnolli, AnnaGamberini, LucianoBackgroundVoice 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. ObjectiveThe 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). MethodsA 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). ResultsFor 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). ConclusionsUsers 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.http://www.jmir.org/2020/9/e18431/ |
| spellingShingle | Masina, Fabio Orso, Valeria Pluchino, Patrik Dainese, Giulia Volpato, Stefania Nelini, Cristian Mapelli, Daniela Spagnolli, Anna Gamberini, Luciano Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study Journal of Medical Internet Research |
| title | Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study |
| title_full | Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study |
| title_fullStr | Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study |
| title_full_unstemmed | Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study |
| title_short | Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study |
| title_sort | investigating the accessibility of voice assistants with impaired users mixed methods study |
| url | http://www.jmir.org/2020/9/e18431/ |
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