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With the need for geriatric care workers growing faster than can be met, the possibility of socially assistive robots filling this need has garnered increasing attention. This heightened interest in robots as social care workers, however, leads to concerns in detecting possible robot misbehavior. We propose a short questionnaire, based on current elder abuse screening tools, as a method to detect intrusion or misconfiguration in caregiver robots. We focus on misbehavior that can cause psychological or financial harm to the caregiver recipient. We discuss requirements, limitations, and future enhancements.
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Clinical Screening Interview Using a Social Robot for Geriatric Care
Social robots are coming to our homes and have already been used to help humans in a number of ways in geriatric care. This article aims to develop a framework that enables social robots to conduct regular clinical screening interviews in geriatric care, such as cognitive evaluation, falls' risk evaluation, and pain rating. We develop a social robot with essential features to enable clinical screening interviews, including a conversational interface, face tracking, an interaction handler, attention management, robot skills, and cloud service management. Besides, a general clinical screening interview management (GCSIM) model is proposed and implemented. The GCSIM enables social robots to handle various types of clinical questions and answers, evaluate and score responses, engage interviewees during conversations, and generate reports on their well-being. These reports can be used to evaluate the progression of cognitive impairment, risk of falls, pain level, and so on by caregivers or physicians. Such a clinical screening capability allows for early detection and treatment planning in geriatric care. The framework was developed and implemented on our 3-D-printed social robot. It was tested on 30 older adults with different ages, achieved satisfying results, and received their high confidence and trust in the use of this robot for human well-being assessment.
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- PAR ID:
- 10195333
- Date Published:
- Journal Name:
- IEEE Transactions on Automation Science and Engineering
- ISSN:
- 1545-5955
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TASE.2020.2999203
