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논문 기본 정보

자료유형
학술대회자료
저자정보
Deok-won Lee (Gwangju Institute of Science and Technology) Ahmed Elsharkawy (Gwangju Institute of Science and Technology) Kooksung Jun (Gwangju Institute of Science and Technology) Yun-dong Lee (Gwangju Institute of Science and Technology) SeungJun Kim (Gwangju Institute of Science and Technology) Mun Sang Kim (Gwangju Institute of Science and Technology)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2021
발행연도
2021.10
수록면
2,122 - 2,126 (5page)

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초록· 키워드

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As the number of elderly persons increases, greater attention must be given to how they or their caregivers deal with emergency situations. This paper describes an automated tracking, fall detection, and emergency recovery system for elderly persons, and shows that efficient a Socially Assistive Robot (SAR) can resolve emergency situations and abnormal behaviors for at-risk populations. Our assistant robot uses position data provided by Ultra-WideBand (UWB) wireless network and motion sensor information to detect potentially dangerous situations for elderly persons. In this context, a deep neural network–based double-check method has been developed to detect and confirm fall situation with high accuracy using in-house developed sensory hardware. We then simulated four typical emergency scenarios using SILBOT-3 robot. Interaction scenarios were demonstrated to 28 caregivers, who were then invited to complete a short questionnaire regarding benefits and improvements for our system. Caregivers responded positively to our system’s performance and stated that they would accept an assistant robot that could notify them quickly about a dangerous situation or possibly resolve the situation autonomously.

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Abstract
1. INTRODUCTION
2. EXPERIMENT DESIGN
3. RESULTS AND DISCUSSION
4. CONCLUSION
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