Hospital Receptionist Robot v2: Design for Enhancing Verbal Interaction with Social Skills

Ahn, Ho Seok; Yep, Wesley; Lim, JongYoon; Ahn, Byeong Kyu; Johanson, Deborah L.; Hwang, Eui Jun; Lee, Min Ho; Broadbent, Elizabeth; MacDonald, Bruce A.

doi:10.1109/ro-man46459.2019.8956300

Cited by 19 publications

(12 citation statements)

References 33 publications

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“…The development of social robots includes several algorithms as well as different implementations, such as face recognition, speech recognition, cognitive and decisionmaking modules [29,39,40,42,44,60,61,66], emotional modules [47,50], and ergonomics [43], which must also be considered fundamental. Achieving new levels of conversational modeling and knowledge and providing intelligent interactive platforms that can interact with users is a promising field [75].…”

Section: Discussionmentioning

confidence: 99%

“…Robots have been used to quantify significant harm levels in autistic children, by professional caregivers of the elderly, to accompany the elderly while walking, to help persons with motor impairment (i.e., quadriplegia), to monitor and correct during rehabilitation for head, neck, and back pain [16,36], and as a mediator in the interaction with the physician or nurse who performed the treatment [18]. Additionally, social robots can be rehabilitation therapists at home [20] or do administrative tasks, such as reception [29,51] in hospitals [22,60]. They have also been used for edutainment purposes [30,70,80].…”

Section: Discussionmentioning

confidence: 99%

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Social Robots in Hospitals: A Systematic Review

2021

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Hospital environments are facing new challenges this century. One of the most important is the quality of services to patients. Social robots are gaining prominence due to the advantages they offer; in particular, several of their main uses have proven beneficial during the pandemic. This study aims to shed light on the current status of the design of social robots and their interaction with patients. To this end, a systematic review was conducted using WoS and MEDLINE, and the results were exhaustive analyzed. The authors found that most of the initiatives and projects serve the elderly and children, and specifically, that they helped these groups fight diseases such as dementia, autism spectrum disorder (ASD), cancer, and diabetes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Social Robots in Hospitals: A Systematic Review

2021

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“…Autonomous systems respond to real world conditions, make decisions, and perform actions with minimal or no interaction with a human ( 19 ). They be encountered in a clinical setting (autonomous implanted devices), in support functions to provide assistance 1 (carrying things around in a facility), or to automate non-physical work, such as a digital receptionist handling patient check-in ( 20 ).…”

Section: Classification Of Ai and Robotic Systems In Medicinementioning

confidence: 99%

A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems

Denecke

Baudoin²

2022

Front. Med.

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Health care is shifting toward become proactive according to the concept of P5 medicine–a predictive, personalized, preventive, participatory and precision discipline. This patient-centered care heavily leverages the latest technologies of artificial intelligence (AI) and robotics that support diagnosis, decision making and treatment. In this paper, we present the role of AI and robotic systems in this evolution, including example use cases. We categorize systems along multiple dimensions such as the type of system, the degree of autonomy, the care setting where the systems are applied, and the application area. These technologies have already achieved notable results in the prediction of sepsis or cardiovascular risk, the monitoring of vital parameters in intensive care units, or in the form of home care robots. Still, while much research is conducted around AI and robotics in health care, adoption in real world care settings is still limited. To remove adoption barriers, we need to address issues such as safety, security, privacy and ethical principles; detect and eliminate bias that could result in harmful or unfair clinical decisions; and build trust in and societal acceptance of AI.

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“…Several robots become our state of the arts of this research: Sophia, the social Humanoid Robot and rst Citizen Robot in the world, developed by Hanson Robotics [4], Edutainment Robot, the close domain question answering robot, with answer base that constructed from the encyclopedia, developed by Oh HJ et al [5], Hospital Receptionist Robot, developed by Ahn HS et al [6], that can express emotion, friendliness and can recognize the face. DialogFlow used by the Hospital Receptionist Robot for the conversation decision-making method.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Question Answering System for Intelligent Humanoid Robot

Budiharto

Andreas

Gunawan

2020

Preprint

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Background- The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action. Findings- The question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF). Conclusions- Our evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM / F1 score than using the CNN encoder.

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Hospital Receptionist Robot v2: Design for Enhancing Verbal Interaction with Social Skills

Cited by 19 publications

References 33 publications

Social Robots in Hospitals: A Systematic Review

Social Robots in Hospitals: A Systematic Review

A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems

Deep Learning-Based Question Answering System for Intelligent Humanoid Robot

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