Elsevier

Computers in Human Behavior

Volume 98, September 2019, Pages 122-133
Computers in Human Behavior

Full length article
Home robotic devices for older adults: Opportunities and concerns

https://doi.org/10.1016/j.chb.2019.04.002Get rights and content

Highlights

  • Older adults' attitudes and emotional reactions toward robotic devices were evaluated.

  • Specific robot features related to appearance, behavior & function affect acceptance.

  • Older adults' user needs should be addressed in the robot's design.

  • Concrete design guidelines for robotic devices targeting older adults are suggested.

Abstract

Robotic devices for older adults are becoming a reality. New robots are being introduced for the growing subpopulation of healthy older adults, with an emphasis on supporting the positive aspects of aging. In order to inform the design and implementation of such robots, the relevant needs and concerns of this population should be studied, mapped, and translated into recommendations. We present a qualitative study of thirty cognitively-intact older adults, evaluating their attitudes and emotional reactions towards different types of home robotic devices. Interview analysis of participants reactions to videos of six devices uncovered four user needs that can be threatened by the introduction of home robots: the need for independence, the need for control, the fear of being replaced, and the need for authenticity. Furthermore, results reveal that cognitively-intact older adults are willing to adopt robotic devices into their homes, contingent upon their preferences and concerns being addressed. We provide recommendations regarding how researchers and designers of home robots can better address the user needs of healthy older adults by leveraging aspects of the robot's function, speech, appearance, size, proactivity, and mobility.

Introduction

Becoming an older adult comes with both positive and negative changes. Nowadays, many older adults experience “healthy” or “successful” aging (Havighurst, 1963, Rowe and Kahn, 1987). They typically have more time to pursue their own interests and are known to score higher on measures of self-acceptance and positive affect (Hudson et al., 2017, Misselhorn et al., 2013). However, aging also brings about challenges such as sensory, cognitive, and physical decline and is associated with higher levels of loneliness and depression (Adams et al., 2004, Jeste et al., 2013). Global demographic trends show the world population is rapidly aging. Currently, individuals aged 60 and older make up 12.3% of the global population; this number is thought to increase to 22% by 2050 (World Health Organization, 2016). As a result, “being an older adult” is becoming a stage of life that may last several decades, stressing the importance of a successful aging experience.

Technology has the potential to assist with the challenges of aging, as well as with enhancing older adults' everyday wellbeing. As a result, digital products are being specifically designed for older adults, including websites, mobile apps, wearables, and smart home devices (Mast et al., 2010, McCreadie and Tinker, 2005, Scanaill et al., 2006). One such technology is home robotic devices, which is believed to have the potential to support physical, cognitive, and social aspects of the older adult's life (Dario, Guglielmelli, Laschi, & Teti, 1999). Some have suggested home robots as possible support for the shortage of caregivers and health-care providers (Super, 2002), as well as a way to comply with older adults' desire to remain in their own homes and to dispel loneliness (Robinson, MacDonald, & Broadbent, 2014).

Within the human-robot interaction community, researchers have been addressing the challenges faced by older adults in various ways. Most previous studies focused on the decline in cognitive and physical abilities. Recently, due to the growing population of healthy and active older adults, researchers have also begun to focus on successful aging as an additional framework for designing robots for this subpopulation. These studies argue that robotic design criteria should expand to address different aspects of successful aging, such as autonomy and resilience (Lee et al., 2016, Lee and Riek, 2018). In the HCI community, these aspects are commonly referred to as user needs, identified by interviewing or observing relevant users and analyzing their statements regarding their goals, concerns, wishes, and preferences (Kujala, Kauppinen, & Rekola, 2001). HCI researchers commonly use such user needs as design considerations for the creation of new technological products. Healthy older adults and healthy young adults are different populations with different user needs (Lee & Riek, 2018), and different factors influence their attitudes toward robots. As a result, older adults may reject robots that younger adults may find useful. Several reasons may account for these differences. First, many assistive robots are designed under the “deficit model of aging” framework, which focuses on disabilities, and may lead to rejection by healthy older adults who do not want to be associated with the negative aspects of aging (Lee et al., 2016). Second, older adults were found to be more sensitive to robot appearances, a sensitivity that was shown to influence robot acceptance (Riek, 2017). Third, older adults were shown to be less experienced with robotic technologies in comparison to younger adults, this difference accounted for the age-related variance in robot acceptance (Ezer, Fisk, & Rogers, 2009), and may also lead to concerns regarding difficulties in operating the robot (Riek, 2017, Robinson et al., 2014). Lastly, different cost-benefit considerations and higher selectivity is associated with older age and was also shown to influence acceptance (Frennert et al., 2013).

While often considered a single demographic, older adults are not a homogeneous population and have a variety of needs and desires (Broekens et al., 2009, Lee et al., 2016). As a result, the functions of robotic devices for the elderly also vary widely. One common categorization in this domain is the distinction between physically assistive robotic devices and socially assistive robots (Broekens et al., 2009). Assistive Robotic Devices generally aid in physical activities such as household maintenance, cooking, monitoring health, and similar functions. In addition, these devices can provide help for those who need continuous attention and aid in performing basic functions such as eating, bathing, toileting and getting dressed. Examples of these robots include smart wheelchairs, artificial limbs, PR2 cooking robot, and more. (Broekens et al., 2009, Graf et al., 2004, Mast et al., 2010). In contrast, Socially Assistive Robots are designed as social entities and involve some level of social interaction with the older adult (Broekens et al., 2009). This category is further divided into service robots and companion robots (Broekens et al., 2009). Socially Assistive Service Robots primarily aid in activities such as event reminders, medicine reminders, and offering suggestions for activities (Breazeal, 2003, Mast et al., 2012). Examples include ElliQ, Nursebot, Care-o-bot, and others (Breazeal, 2003). Socially Assistive Companion Robots do not assist in daily activities, rather their entire function is social interaction. These robots are designed to provide companionship for the older adult in order to dispel loneliness and reduce stress (Robinson et al., 2014). Such devices are sometimes designed with a resemblance to animals, toys, or even pets, and include Paro (seal), Huggable (teddy bear), and Aibo (dog; Dautenhahn, 2004).

The challenge of integrating robotic technology into the lives of older adults is still far from trivial. Older adults' attitudes toward robotic devices vary greatly, affecting acceptance rates (Hirsch et al., 2000, Tapus et al., 2007). With recent advancements in the development of home robots, there is an opportunity to study how healthy older adults react to different types of home robots, extending the body of knowledge in the field, and informing designers on the challenges and opportunities relevant to this specific subpopulation. In this paper, we set out to better understand the design factors influencing cognitively-intact older adults' attitudes toward a variety of near-future home robots. We use qualitative methods to analyze older adults’ attitudes and reactions toward six robotic devices, mapping emerging themes and providing design recommendations for designers, researchers, and practitioners.

Section snippets

Related work

Prior literature on older adults' attitudes towards robotic devices typically focused on clinical populations. Recently, studies have been conducted on the population of cognitively-intact older adults, who mostly reside at home (Lee & Riek, 2018), and we set to extend this approach. Previous work studied older adults' general attitudes toward home robots, usually by asking participants to express their opinions when thinking about the topic abstractly, imagining a device, or interacting with a

Methodology

In this study, we comprehensively map attitudes and concerns collected from in-depth interviews of healthy older adults towards a range of robots design factors. We employ qualitative research methods that are considered ideal for exploratory studies (Sofaer, 1999), supporting an inductive process leading to emerging themes without a prior hypothesis. While it is possible to use quantitative methods to measure attitudes, qualitative methods can provide a richer description of complex phenomena (

Theme I: About the robot

The first high-level theme emerged from categories that involved concerns and preferences towards the robotic device. These categories relate to the robot's behavior, appearance, and function (see: Fig. 3). While behavior and appearance involved attitudes regarding specific robot features, function involved the main task the robot can fulfill (assistive or social function). Each category includes quotation groups that emerged from the responses, with some of the groups further divided into

Theme II: about me

The second high-level theme emerged from participants’ responses regarding themselves, their role, and their personal preferences with respect to interaction with a robotic device. The two main categories in this theme are, the User Needs of older adults and their general Openness to a Device (see: Fig. 4).

Discussion and design recommendations

The analysis of cognitively-intact older adults' reactions to the videos of robotic devices revealed common themes that can guide designers, researchers, and practitioners working with robots for this specific population. Our analysis identified robot features and design aspects (“About the Robot”) on the one hand, and user needs (“About Me”) on the other. In this section, we integrate these themes and discuss how older adults are open to home robotic devices only if they are designed to

Limitations

Our study has several limitations. As a qualitative study based on face-to-face interviews, our interviewers could unknowingly influence an interviewee's responses (Opdenakker, 2006). We made our best effort to mitigate this well-known effect, by training the interviewers according to detailed interview protocol and increasing their awareness to this effect (Opdenakker, 2006). In addition, our choice of video study rather than live interaction study to introduce the different types of robotic

Conclusion

Our study indicates that cognitively-intact older adults are willing to accept robotic devices into their home, but have very specific preferences and concerns that must be addressed. Our qualitative analysis revealed four user needs that are at risk of being threatened by design aspects of home robots: the need for independence, the need for control, the fear of being replaced, and the need for authenticity. We presented a set of design recommendations, informing designers how they can address

Declaration of interest

All authors report no financial, personal or other relationships with commercial interests.

Indications of previous presentation

No previous presentation, nor date(s) or location of meeting.

Funding

The study was supported by a grant from The Israel Ministry of Science and Technology (Grant #54178).

Acknowledgments

This research was supported by a grant from the [removed for anonymity]. The authors wish to thank all of our study participants for their insightful comments, ideas, and inspiration.

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