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August 9, 2022

Implementation of Fall Detection for The Elderly Home Support using 24-GHz Radar Sensor

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Key result

A 24-GHz radar sensor and CNN achieve ~96% accuracy detecting falls, walking, and vacant states.

Comparison

Fall detection system using 24-GHz radar sensor vs vacant and walking classes

Authors

AAAmran AmranEPElly PurwantiniAWArdik Wijayanto

Discussion

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Overview

May support non-contact fall monitoring development; leaves open prospective validation before clinical adoption.

Structured PICO

P
Population
Experimental setup for fall detection in elderly home support
I
Intervention
Fall detection system using 24-GHz radar sensor mounted on the ceiling, processed with Convolutional Neural Network (CNN) on Arduino Nano 33 BLE Sense
O
Outcome
Accuracy rate of recognizing falling, vacant, and walking classes

A 24-GHz radar sensor combined with a CNN algorithm on an Arduino Nano 33 BLE Sense can detect falls with 96.30% testing accuracy.

Cite This Study

Amran et al. (2022) studied Fall detection for elderly. 24-GHz radar sensor with Convolutional Neural Network (CNN) was evaluated on Accuracy rate of the testing phase. A 24-GHz radar sensor combined with a Convolutional Neural Network algorithm achieved a testing accuracy of 96.30% for detecting falls, walking, and vacant states.

synapsesocial.com/papers/6a16f4b025571367076bca3bhttps://doi.org/10.1109/ies55876.2022.9888595
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Acceleration-Based Low-Cost CW Radar System for Real-Time Elderly Fall Detection2024 · 16 citations
  2. 2A robust fall detection approach based on 4D imaging millimeter-wave radar2024 · 2 citations
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  4. 4FPGA Implementation of a Radar-Based Fall Detection System Using Binarized Convolutional Neural Networks2026
  5. 5A Radar-Based Smart Sensor for Unobtrusive Elderly Monitoring in Ambient Assisted Living Applications2017 · 135 citations