A Convolutional Neural Network Model for Privacy-Sensitive Ultra-Wideband Radar-Based Human Static Posture Classification and Fall Detection

Conference proceedings article

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Publication Details

Author list: Thipprachak, Khirakorn; Tangamchit, Poj; Lerspalungsanti, Sarawut

Publisher: IEEE Computer Society

Publication year: 2023

Volume number: 2023-July

Start page: 443

End page: 447

Number of pages: 5

ISBN: 978-166545245-8

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168871176&doi=10.1109%2fSSP53291.2023.10208028&partnerID=40&md5=0978ac30bc0cb29131ee53d3094158fc

Languages: English-Great Britain (EN-GB)

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Abstract

A reliable fall detection system can enhance the safety of senior citizens by detecting falls in private areas, such as restrooms, where accidents may go unnoticed. This study aimed to create a static human posture recognition system with a possibility of extension for detecting falls in private areas. The system used ultra-wideband (UWB) sensors to detect human body gestures and analyze an individual's posture to determine a laydown posture, which is abnormal in restroom usage. UWB is capable of protecting human privacy because its output contains limited information. This study implemented a convolutional neural network (CNN) model that classified signals from an ultra-wideband sensor in a bathroom into four categories: standing, sitting, lying down, and nobody. This paper proposes a CNN classifier with an overall accuracy of 93%. These results demonstrate the capability of the proposed system to recognize static human posture in private locations. ฉ 2023 IEEE.

Keywords

human static posture classification, ultra-wideband (UWB)