Socially Assistive Robots as Mental Health Interventions for Children: A Scoping Review

Kabacińska, Katarzyna; Prescott, Tony J.; Robillard, Julie M.

doi:10.1007/s12369-020-00679-0

Cited by 65 publications

(45 citation statements)

References 68 publications

(121 reference statements)

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“…A study of the use of the Sony Aibo robot dog in a residential care home found a positive impact on the experience of loneliness similar to that generated by interaction with a real dog ( Banks et al., 2008 ). Recent work on social robots as interventions for mental health also indicates the potential for the affective components of human-robot interaction to generate emotional comfort and to scaffold feelings of self-worth ( Ostrowski et al., 2019 ; Kabacińska et al., 2020 ). The effectiveness of robots as social companions can also be improved by adapting their cognitive architectures and capabilities to suit specific populations, such as people living with dementia ( Perugia et al., 2020 ).…”

Section: Potential Benefitsmentioning

confidence: 99%

“…The interactive and dynamic features of social robots, and their ability to understand and respond to human emotion, make them candidate solutions for application domains that require social engagement and comfort. In healthcare, social robots are being investigated and implemented as tools to assist patients by means of emotional support across the lifespan, from pediatric populations ( Kabacińska et al., 2020 ) to older adults ( Broekens et al., 2009 ; Robinson et al., 2014 ; Prescott and Caleb-Solly, 2017 ; Pu et al., 2018 ; Papadopoulos et al., 2020 ). In education, social robots are being used as tutors or co-learners to promote cognitive and affective outcomes including social skills development ( Belpaeme et al., 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Are friends electric? The benefits and risks of human-robot relationships

Prescott

Robillard

2021

iScience

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Section: Potential Benefitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Are friends electric? The benefits and risks of human-robot relationships

Prescott

Robillard

2021

iScience

Self Cite

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“…Sensor and device network technologies were applied in 11 studies. Apart from the multi-device systems that were used with wearable technologies ([ 14 , 39 , 44 , 61 ]), a large proportion of the solutions were distributed or multi-agent systems that were developed mainly based on a client/server architecture. The client devices could interact with each other online or offline, and their status could also reflect on the server device.…”

Section: Resultsmentioning

confidence: 99%

“…Among different kinds of Socially Assistive Systems, robotic systems include a large number of studies, such as Socially Assistive Robots for older adults ([ 29 , 30 , 38 ]) and children [ 39 ]. Some existed review papers have reported such field, so we did not include robotic systems in our systematic review.…”

Section: Methodsmentioning

confidence: 99%

Investigating socially assistive systems from system design and evaluation: a systematic review

Qiu

Kang

et al. 2021

Univ Access Inf Soc

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Purpose The development of assistive technologies that support people in social interactions has attracted increased attention in HCI. This paper presents a systematic review of studies of Socially Assistive Systems targeted at older adults and people with disabilities. The purpose is threefold: (1) Characterizing related assistive systems with a special focus on the system design, primarily including HCI technologies used and user-involvement approach taken; (2) Examining their ways of system evaluation; (3) Reflecting on insights for future design research. Methods A systematic literature search was conducted using the keywords “social interactions” and “assistive technologies” within the following databases: Scopus, Web of Science, ACM, Science Direct, PubMed, and IEEE Xplore. Results Sixty-five papers met the inclusion criteria and were further analyzed. Our results showed that there were 11 types of HCI technologies that supported social interactions for target users. The most common was cognitive and meaning understanding technologies, often applied with wearable devices for compensating users’ sensory loss; 33.85% of studies involved end-users and stakeholders in the design phase; Four types of evaluation methods were identified. The majority of studies adopted laboratory experiments to measure user-system interaction and system validation. Proxy users were used in system evaluation, especially in initial experiments; 42.46% of evaluations were conducted in field settings, primarily including the participants’ own homes and institutions. Conclusion We contribute an overview of Socially Assistive Systems that support social interactions for older adults and people with disabilities, as well as illustrate emerging technologies and research opportunities for future work.

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“…There is growing interest in developing SAS, and every year many new studies are published. As we have not identified systematic reviews that focused on assistive technologies for support social interactions other than robotic systems ([ 21 , 22 , 23 , 24 ]), there is a need to categorize the current research studies. Hence, we have outlined the research methods used to study SASs, identify the outcomes, inform design practice, and guide further research.…”

Section: Introductionmentioning

confidence: 99%

A Review of Data Gathering Methods for Evaluating Socially Assistive Systems

Qiu

Kang

et al. 2021

Sensors

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Social interactions significantly impact the quality of life for people with special needs (e.g., older adults with dementia and children with autism). They may suffer loneliness and social isolation more often than people without disabilities. There is a growing demand for technologies to satisfy the social needs of such user groups. However, evaluating these systems can be challenging due to the extra difficulty of gathering data from people with special needs (e.g., communication barriers involving older adults with dementia and children with autism). Thus, in this systematic review, we focus on studying data gathering methods for evaluating socially assistive systems (SAS). Six academic databases (i.e., Scopus, Web of Science, ACM, Science Direct, PubMed, and IEEE Xplore) were searched, covering articles published from January 2000 to July 2021. A total of 65 articles met the inclusion criteria for this systematic review. The results showed that existing SASs most often targeted people with visual impairments, older adults, and children with autism. For instance, a common type of SASs aimed to help blind people perceive social signals (e.g., facial expressions). SASs were most commonly assessed with interviews, questionnaires, and observation data. Around half of the interview studies only involved target users, while the other half also included secondary users or stakeholders. Questionnaires were mostly used with older adults and people with visual impairments to measure their social interaction, emotional state, and system usability. A great majority of observational studies were carried out with users in special age groups, especially older adults and children with autism. We thereby contribute an overview of how different data gathering methods were used with various target users of SASs. Relevant insights are extracted to inform future development and research.

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Socially Assistive Robots as Mental Health Interventions for Children: A Scoping Review

Abstract: This is a repository copy of Socially assistive robots as mental health interventions for children : a scoping review.

Cited by 65 publications

References 68 publications

Are friends electric? The benefits and risks of human-robot relationships

Are friends electric? The benefits and risks of human-robot relationships

Investigating socially assistive systems from system design and evaluation: a systematic review

A Review of Data Gathering Methods for Evaluating Socially Assistive Systems

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