Poster: Towards Privacy Preserving Patient State Classification in Psychiatric Seclusion Room using mmWave Radar

Continuous monitoring of patients in psychiatric seclusion rooms is essential yet challenging, particularly with staff shortages that can delay responses to sudden changes in patient conditions. To this end, we propose PsiMo, a remote patient state monitoring system using mmWave Frequency Modulated Continuous Wave (FMCW) radar. Unlike existing vision-based or wearable systems, PsiMo captures patient movements without compromising privacy or risking potential self-harm. Our system continuously monitors patient's state of motion to alert medical staff in the event of abnormal conditions, such as agitation. Our preliminary evaluation shows PsiMo achieves 97.0% accuracy in patient state classification, demonstrating its potential for effective, non-contact monitoring.

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Publication Details

Published
2024-11-04
DOI
https://doi.org/10.1145/3666025.3699418
Primary Topic
Biometric Identification and Security
Type
article
Field-Weighted Citation Impact
0.00

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article

Poster: Towards Privacy Preserving Patient State Classification in Psychiatric Seclusion Room using mmWave Radar

Dongjin Seo, Jun Young Han, Jonghyuk Yun, Joon Son et al.
Biometric Identification and Security
article

Poster: Towards Privacy Preserving Patient State Classification in Psychiatric Seclusion Room using mmWave Radar

Dongjin Seo, Jun Young Han, Jonghyuk Yun, Joon Son, Seongjin Wang
article en

Abstract

Continuous monitoring of patients in psychiatric seclusion rooms is essential yet challenging, particularly with staff shortages that can delay responses to sudden changes in patient conditions. To this end, we propose PsiMo, a remote patient state monitoring system using mmWave Frequency Modulated Continuous Wave (FMCW) radar. Unlike existing vision-based or wearable systems, PsiMo captures patient movements without compromising privacy or risking potential self-harm. Our system continuously monitors patient's state of motion to alert medical staff in the event of abnormal conditions, such as agitation. Our preliminary evaluation shows PsiMo achieves 97.0% accuracy in patient state classification, demonstrating its potential for effective, non-contact monitoring.

Korea Advanced Institute of Science and Technology (KR), Yonsei University (KR), Ulsan National Institute of Science and Technology (KR)
Korea Health Industry Development Institute
Gender equality
Openalex Percentile: Top 79%
Biometric Identification and Security
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