A focus group study on the design considerations and impressions of a socially assistive robot for long-term care

Louie, Wing-Yue Geoffrey; Li, Jacob; Vaquero, Tiago Stegun; Nejat, Goldie

doi:10.1109/roman.2014.6926259

Cited by 29 publications

(26 citation statements)

References 20 publications

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“…Brian 2.1 was developed to engage older adults in eating activity and a cognitive stimulation activity, and had the capability of adapting its behavior based on the state of the activities as well as user’s body language (attentive or distracted). These researchers also developed a robotic system Tangy [23][24] for use in long-term care facilities to provide telepresence and group-based cognitive intervention. Robotic coach systems were also developed for stroke rehabilitation, autism intervention, and weight loss [21][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Robotic Coach Architecture for Elder Care (ROCARE) Based on Multi-User Engagement Models

Fan

Bian

Zheng

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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The aging population with its concomitant medical conditions, physical and cognitive impairments, at a time of strained resources, establishes the urgent need to explore advanced technologies that may enhance function and quality of life. Recently, robotic technology, especially socially assistive robotics has been investigated to address the physical, cognitive, and social needs of older adults. Most system to date have predominantly focused on one-on-one human robot interaction (HRI). In this paper, we present a multi-user engagement-based robotic coach system architecture (ROCARE). ROCARE is capable of administering both one-on-one and multi-user HRI, providing implicit and explicit channels of communication, and individualized activity management for long-term engagement. Two preliminary feasibility studies, a one-on-one interaction and a triadic interaction with two humans and a robot, were conducted and the results indicated potential usefulness and acceptance by older adults, with and without cognitive impairment.

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Section: Introductionmentioning

confidence: 99%

A Robotic Coach Architecture for Elder Care (ROCARE) Based on Multi-User Engagement Models

Fan

Bian

Zheng

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Specific data gathering methods which can be employed in pursuit of the above include focus groups and workshops (e.g. [19,22,24]), interviews [22], surveys [17] and observations (e.g. [6,12,30]).…”

Section: Methods For Mutual Shapingmentioning

confidence: 99%

Mutual Shaping in the Design of Socially Assistive Robots: A Case Study on Social Robots for Therapy

Winkle

Caleb-Solly

Turton

et al. 2019

Int J of Soc Robotics

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This paper offers a case study in undertaking a mutual shaping approach to the design of socially assistive robots. We consider the use of social robots in therapy, and we present our results regarding this application, but the approach is generalisable. Our methodology combines elements of user-centered and participatory design with a focus on mutual learning. We present it in full alongside a more general guide for application to other areas. This approach led to valuable results concerning mutual shaping effects and societal factors regarding the use of such robots early in the design process. We also measured a significant shift in participant robot acceptance pre-/post-study, demonstrating that our approach led to the two-way sharing and shaping of knowledge, ideas and acceptance.

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“…Our long-term project is the deployment of intelligent mobile robots in retirement homes to engage residents in stimulating recreational activities (Louie, Han, & Nejat, 2013;Louie, Li, Vaquero, & Nejat, 2014;Louie, Vaquero, Nejat, & Beck, 2014;Li, Louie, Despond, & Nejat, 2016). We have designed the robot known as Tangy to: 1) navigate using a laser range finder and 3D depth sensors, 2) detect users with 2D cameras, and 3) interact with users through speech, gestures, and a touch screen.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…We define the main elements of the proposed problem: the environment in which the residents (users) and robots interact, the constraints, the goal and preferences. The constraints for the telepresence sessions and Bingo activities were obtained from meetings with directors, healthcare professionals, and residents from Toronto area retirement homes (Louie, Li, Vaquero, & Nejat, 2014. The parameters and preferences used herein can be changed as needed without a large impact on the models proposed in this paper.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…To address the lack of human resources, human-robot interaction (HRI) and robot companionship have been proposed and shown to have positive results on the human psychological state (Banks, Willoughby, & Banks, 2008;Turkle, 2006). To that end, we have undertaken a long-term study on the deployment of intelligent humanlike mobile robots in retirement homes to assist and interact with elderly residents (Booth, Tran, Nejat, & Beck, 2016;Louie, Vaquero, Nejat, & Beck, 2014;Louie, Li, Vaquero, & Nejat, 2014Mohamed & Nejat, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Robots in Retirement Homes: Applying Off-the-Shelf Planning and Scheduling to a Team of Assistive Robots

Tran¹,

Vaquero²,

Nejat³

et al. 2017

jair

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This paper investigates three different technologies for solving a planning and scheduling problem of deploying multiple robots in a retirement home environment to assist elderly residents. The models proposed make use of standard techniques and solvers developed in AI planning and scheduling, with two primary motivations. First, to find a planning and scheduling solution that we can deploy in our real-world application. Second, to evaluate planning and scheduling technology in terms of the "model-and-solve" functionality that forms a major research goal in both domain-independent planning and constraint programming. Seven variations of our application are studied using the following three technologies: PDDL-based planning, time-line planning and scheduling, and constraint-based scheduling. The variations address specific aspects of the problem that we believe can impact the performance of the technologies while also representing reasonable abstractions of the real world application. We evaluate the capabilities of each technology and conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application. PDDL-based planning is able to find mostly low quality solutions while the timeline approach was unable to model the full problem without alterations to the solver code, thus moving away from the modeland-solve paradigm. It would be misleading to conclude that constraint programming is "better" than PDDL-based planning in a general sense, both because we have examined a single application and because the approaches make different assumptions about the knowledge one is allowed to embed in a model. Nonetheless, we believe our investigation is valuable for AI planning and scheduling researchers as it highlights these different modelling assumptions and provides insight into avenues for the application of AI planning and scheduling for similar robotics problems. In particular, as constraint programming has not been widely applied to robot planning and scheduling in the literature, our results suggest significant untapped potential in doing so.

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A focus group study on the design considerations and impressions of a socially assistive robot for long-term care

Cited by 29 publications

References 20 publications

A Robotic Coach Architecture for Elder Care (ROCARE) Based on Multi-User Engagement Models

A Robotic Coach Architecture for Elder Care (ROCARE) Based on Multi-User Engagement Models

Mutual Shaping in the Design of Socially Assistive Robots: A Case Study on Social Robots for Therapy

Robots in Retirement Homes: Applying Off-the-Shelf Planning and Scheduling to a Team of Assistive Robots

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