Collaborative Fall Detection and Response Using Wi-Fi Sensing and Mobile Companion Robot

This paper presents a collaborative fall detection and response system integrating Wi-Fi sensing with robotic assistance. The proposed system leverages channel state information (CSI) disruptions caused by movements to detect falls in non-line-of-sight (NLOS) scenarios, offering non-intrusive monitoring. Besides, a companion robot is utilized to provide assistance capabilities to navigate and respond to incidents autonomously, improving efficiency in providing assistance in various environments. The experimental results demonstrate the effectiveness of the proposed system in detecting falls and responding effectively.

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Publication Details

Published
2024-12-10
DOI
https://doi.org/10.1109/robio64047.2024.10907570
Citations
1
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.25
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article

Collaborative Fall Detection and Response Using Wi-Fi Sensing and Mobile Companion Robot

Hong Yue, Lingxiao Meng, Ziqi Zhao, Max Q.-H. Meng et al.
1 citations
Context-Aware Activity Recognition Systems
0.25
article

Collaborative Fall Detection and Response Using Wi-Fi Sensing and Mobile Companion Robot

Hong Yue, Lingxiao Meng, Ziqi Zhao, Max Q.-H. Meng, Yunwang Chen, Yaozhong Kang
article en
1 citations

Abstract

This paper presents a collaborative fall detection and response system integrating Wi-Fi sensing with robotic assistance. The proposed system leverages channel state information (CSI) disruptions caused by movements to detect falls in non-line-of-sight (NLOS) scenarios, offering non-intrusive monitoring. Besides, a companion robot is utilized to provide assistance capabilities to navigate and respond to incidents autonomously, improving efficiency in providing assistance in various environments. The experimental results demonstrate the effectiveness of the proposed system in detecting falls and responding effectively.

Southern University of Science and Technology (CN)
Openalex Percentile: Top 42%
Context-Aware Activity Recognition Systems
0.25
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Collaborative Fall Detection and Response Using Wi-Fi Sensing and Mobile Companion Robot — Hong Yue, Lingxiao Meng, et al. · (2024) | TGRS Research Map | TGRS