Nursing and Rehabilitative Care of the Elderly Using Humanoid Robots

Tanioka, Tetsuya

doi:10.2152/jmi.66.19

Cited by 54 publications

(27 citation statements)

References 20 publications

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“…Multiple articles predicted that by using robots to assist with some care activities, nurses may have more time to spend in getting to know their patients' preferences, responding appropriately to their needs, and building stronger therapeutic relationships [35,[45][46][47][48]. In addition, several articles discussed how health professionals, including nurses, have used SARs to gain a deeper understanding of their patients [34,49]. For instance, within LTC settings, SARs have been used to stimulate memories of residents with dementia, allowing health professionals to explore the residents' past experiences, personality, and identity [34,50,51].…”

Section: Research Question 1: Nursing Compassionate Care and Aimentioning

confidence: 99%

Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

Buchanan¹,

Howitt²,

Wilson³

et al. 2020

JMIR Nursing

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Background: Artificial intelligence (AI) is set to transform the health system, yet little research to date has explored its influence on nurses-the largest group of health professionals. Furthermore, there has been little discussion on how AI will influence the experience of person-centered compassionate care for patients, families, and caregivers. Objective: This review aims to summarize the extant literature on the emerging trends in health technologies powered by AI and their implications on the following domains of nursing: administration, clinical practice, policy, and research. This review summarizes the findings from 3 research questions, examining how these emerging trends might influence the roles and functions of nurses and compassionate nursing care over the next 10 years and beyond. Methods: Using an established scoping review methodology, MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Center, Scopus, Web of Science, and ProQuest databases were searched. In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI. Data were charted using structured forms and narratively summarized. Results: A total of 131 articles were retrieved from the scoping review for the 3 research questions that were the focus of this manuscript (118 from database sources and 13 from targeted websites). Emerging AI technologies discussed in the review included predictive analytics, smart homes, virtual health care assistants, and robots. The results indicated that AI has already begun to influence nursing roles, workflows, and the nurse-patient relationship. In general, robots are not viewed as replacements for nurses. There is a consensus that health technologies powered by AI may have the potential to enhance nursing practice. Consequently, nurses must proactively define how person-centered compassionate care will be preserved in the age of AI. Conclusions: Nurses have a shared responsibility to influence decisions related to the integration of AI into the health system and to ensure that this change is introduced in a way that is ethical and aligns with core nursing values such as compassionate care. Furthermore, nurses must advocate for patient and nursing involvement in all aspects of the design, implementation, and evaluation of these technologies.

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Section: Research Question 1: Nursing Compassionate Care and Aimentioning

confidence: 99%

Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

Buchanan¹,

Howitt²,

Wilson³

et al. 2020

JMIR Nursing

View full text Add to dashboard Cite

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“…Based on current and other recent findings (e.g., Henschel & Cross, 2020), we would also argue that measures such as the Godspeed Liking scale are overly specificfocussing on social liking and neglecting other forms of liking in the process. In addition to advising the use of embodied human-robot interaction for probing other cognitive measures of social perception, such as gaze cueing (e.g., Kompatsiari et al, 2018;2019; see also Henschel, Hortensius & Cross, 2020), we also suggest that future studies consider adding more might reveal about their research questions and robotic system of interest. In doing so it is possible to gain new insights regarding measurement techniques and individual differences, and generate research questions which are highly relevant to our future with robotic technologies.…”

Section: Discussionmentioning

confidence: 99%

“…One such robot that is already being introduced to social spheres, and which a number of organisations hope to deploy in such situations, is the "Pepper" robot (Softbank Robotics;Fig 1). This robot has the appearance of a friendly humanoid, and can recognise and speak to individuals, and is already being trialled in hospital and service industry contexts (Foster et al, 2016;Niemelä, Heikkilä & Lammi, 2017; Tanioka, 2019).…”

Section: Companion Robotsmentioning

confidence: 99%

Investigating the effect of heartrate synchrony on prosocial behaviour towards a robot

Es¹,

Riddoch²

2020

Preprint

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Robots are being designed to alleviate the burden of social isolation and loneliness, particularly for older adults for whom these issues are more widespread. While good intentions underpin these developments, the reality is that many of these robots are abandoned within a short period of time. To encourage the longer-term use and utility of such robots, researchers are exploring ways to increase robot likability and facilitate attachment. Results from experimental psychology suggest that interpersonal synchrony (the overlap of movement/sensation between two agents) increases the extent to which people like one another. To investigate the possibility that synchrony could facilitate people’s liking towards a robot, we undertook a between-subjects experiment in which participants interacted with a robot programmed to illuminate at the same rate, or 20% slower, than their heart rate. To quantify the impact of cardio-visual synchrony on prosocial attitudes and behaviours toward this robot, participants completed self-report questionnaires, a gaze-cueing task, and were asked to strike the robot with a mallet. Contrary to hypotheses, results revealed no differences in self-reported liking of the robot, gaze cueing effects, or the extent to which participants hesitated to hit the robot between the synchronous and asynchronous groups. The qualitative data collected in semi-structured interviews provided rich insights, though, and call into question the use of the broad “Likability” measurement, and the appropriateness of the ‘hesitance to hit’ paradigm as a measure of attachment to a robotic system.

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“…Robots available nowadays are used for a variety of jobs -e.g. we might have seen a Pepper robot being used for catering, tutoring, elderly care, and more [38]- [40].…”

Section: Related Workmentioning

confidence: 99%

“…3) Context creation: We identified 4 plausible contexts where our robots could work: home, hospital, restaurant, and school. These were chosen because at least some of our robots of interest have already been used in these contexts [38]- [40], and because they represent venues that are currently being explored for robot deployment. To create a contextual illusion, we added some background noise to each of the voices, to immerse participants in the different contexts.…”

Section: A Stimulimentioning

confidence: 99%

How context shapes the appropriateness of a robot’s voice

Torre

Latupeirissa

McGinn

2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Social robots have a recognizable physical appearance, a distinct voice, and interact with users in specific contexts. Previous research has suggested a 'matching hypothesis', which seeks to rationalise how people judge a robot's appropriateness for a task by its appearance. Other research has extended this to cover combinations of robot voices and appearances. In this paper, we examine the missing connection between robot voice, robot appearance, and deployment context. In so doing, we asked participants to match a robot image to a voice within a defined interaction context. We selected widely available social robots, identified task contexts they are used in, and manipulated the voices in terms of gender, naturalness, and accent. We found that the task context mediates the 'matching hypothesis'. People consistently selected a robot based on a vocal feature for a certain context, and a different robot based on the same vocal feature for another context. We suggest that robot voice design should take advantage of current technology that enables the creation and tuning of custom voices. They are a flexible tool to increase perception of appropriateness, which has a positive influence on Human-Robot Interaction. I. INTRODUCTION Spoken communication is the primary form of interaction between a growing number of social robots and their users. However, the overall effort of designing robot voices is considerably less than the amount of work that goes into designing their physical appearance [1]-[4]. Research in Human-Robot Interaction (HRI) could exploit the flexibility afforded by many available Text-to-Speech systems, voice banks, and custom recordings, enabling designers to gain greater control over how robots are perceived. Voices contribute to impression formation of newly-met individuals in human-human interactions [5], as well as shaping how impressions develop over time [6]. Besides linguistic content, voices carry a wide variety of information, ranging from indexical characteristics of the speaker such as gender, age, and place of origin, as well as temporary state alterations, like mood, emotions, or health [7], [8].

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Nursing and Rehabilitative Care of the Elderly Using Humanoid Robots

Cited by 54 publications

References 20 publications

Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

Investigating the effect of heartrate synchrony on prosocial behaviour towards a robot

How context shapes the appropriateness of a robot’s voice

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