Human-Robot Interaction-Based Intention Sharing of Assistant Robot for Elderly People

Yang, Jeong-Yean; Kwon, Oh‐Wook; Lim, Chan-Soon; Kwon, Dong‐Soo

doi:10.1007/978-3-642-33932-5_27

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When reasoning process determines the response with only observed sensing inputs [1], the response at each step attempts to improve the local optimality while goes astray for establishing long-term strategy. In other words, considering the walker system about path planning, the global goal in locomotion is often forgotten by sudden obstacles.…”

Section: Threshold Model For Feedback Reasoningmentioning

confidence: 99%

Feedback-based reasoning process for behavior selection during long-term interaction

Yang

Kwon

2013

2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

Self Cite

View full text Add to dashboard Cite

This paper focused on the effectiveness of the feedback-based reasoning process for managing long-term interaction. We propose the interaction process based on self-reference that a robotic system iteratively evaluates the status and the feedback during turn-based interaction. The result of reasoning process in the previous step is added to input queues, which also generates queries to reasoning to next behaviors. Assuming that reasoning process determines appropriate behaviors, we testified the effects of feedback-based reasoning in two different examples.

show abstract

Section: Threshold Model For Feedback Reasoningmentioning

confidence: 99%

Feedback-based reasoning process for behavior selection during long-term interaction

Yang

Kwon

2013

2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

Self Cite

View full text Add to dashboard Cite

show abstract

Gaze Based Implicit Intention Inference with Historical Information of Visual Attention for Human-Robot Interaction

Nie

2021

Intelligent Robotics and Applications

View full text Add to dashboard Cite

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

et al. 2021

View full text Add to dashboard Cite

Background Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. Objective This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care. Methods Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria. Results The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail. Conclusions The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

show abstract

Human-Robot Interaction-Based Intention Sharing of Assistant Robot for Elderly People

Cited by 3 publications

References 5 publications

Feedback-based reasoning process for behavior selection during long-term interaction

Feedback-based reasoning process for behavior selection during long-term interaction

Gaze Based Implicit Intention Inference with Historical Information of Visual Attention for Human-Robot Interaction

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

Contact Info

Product

Resources

About