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      Maximizing the Benefits of Participatory Design for Human–Robot Interaction Research With Older Adults

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          Abstract

          Objective

          We reviewed human–robot interaction (HRI) participatory design (PD) research with older adults. The goal was to identify methods used, determine their value for design of robots with older adults, and provide guidance for best practices.

          Background

          Assistive robots may promote aging-in-place and quality of life for older adults. However, the robots must be designed to meet older adults’ specific needs and preferences. PD and other user-centered methods may be used to engage older adults in the robot development process to accommodate their needs and preferences and to assure usability of emergent assistive robots.

          Method

          This targeted review of HRI PD studies with older adults draws on a detailed review of 26 articles. Our assessment focused on the HRI methods and their utility for use with older adults who have a range of needs and capabilities.

          Results

          Our review highlighted the importance of using mixed methods and including multiple stakeholders throughout the design process. These approaches can encourage mutual learning (to improve design by developers and to increase acceptance by users). We identified key phases used in HRI PD workshops (e.g., initial interview phase, series of focus groups phase, and presentation phase). These approaches can provide inspiration for future efforts.

          Conclusion

          HRI PD strategies can support designers in developing assistive robots that meet older adults’ needs, capabilities, and preferences to promote acceptance. More HRI research is needed to understand potential implications for aging-in-place. PD methods provide a promising approach.

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          Most cited references34

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          Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction

          A critical construct related to human-robot interaction (HRI) is autonomy, which varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots may interact with one another. Thus, there is a need to understand HRI by identifying variables that influence – and are influenced by – robot autonomy. Our overarching goal is to develop a framework for levels of robot autonomy in HRI. To reach this goal, the framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, the framework proposes a process for determining a robot’s autonomy level, by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as guidelines to determine autonomy, categorize the LORA along a qualitative taxonomy, and consider which HRI variables (e.g., acceptance, situation awareness, reliability) may be influenced by the LORA.
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            Human–Robot Interaction

            The current status of human-robot interaction (HRI) is reviewed, and key current research challenges for the human factors community are described.
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              Participatory Design in Gerontechnology: A Systematic Literature Review

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Human Factors: The Journal of the Human Factors and Ergonomics Society
                Hum Factors
                SAGE Publications
                0018-7208
                1547-8181
                August 30 2021
                : 001872082110374
                Affiliations
                [1 ] University of Illinois Urbana-Champaign, USA
                Article
                10.1177/00187208211037465
                34461761
                a15aeec5-1800-44df-abbb-0cbd804162ae
                © 2021

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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