Human Fall Detection with Ultra-Wideband Radar and Adaptive Weighted Fusion.

Ling Huang, Anfu Zhu, Mengjie Qian, Huifeng An

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

A Two-Stream Method for Human Action Recognition Using Facial Action Cues.Sensors (Basel, Switzerland) · 2024
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ling HuangSchool of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.

Anfu ZhuSchool of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.

Mengjie QianSchool of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.

Huifeng AnSchool of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.

Funding

Lanzhou Municipal Talent Innovation and En- trepreneurship Project Project No. 2022-RC-7This work was supported in part by National Natural Sci- ence Foundation of China Special Program under Grant 62341311

6 · The paper itself

Abstract

To address the challenges in recognizing various types of falls, which often exhibit high similarity and are difficult to distinguish, this paper proposes a human fall classification system based on the SE-Residual Concatenate Network (SE-RCNet) with adaptive weighted fusion. First, we designed the innovative SE-RCNet network, incorporating SE modules after dense and residual connections to automatically recalibrate feature channel weights and suppress irrelevant features. Subsequently, this network was used to train and classify three types of radar images: time-distance images, time-distance images, and distance-distance images. By adaptively fusing the classification results of these three types of radar images, we achieved higher action recognition accuracy. Experimental results indicate that SE-RCNet achieved F1-scores of 94.0%, 94.3%, and 95.4% for the three radar image types on our self-built dataset. After applying the adaptive weighted fusion method, the F1-score further improved to 98.1%.

Indexed as

Accidental FallsRadarAlgorithmsHumansNeural Networks, Computeradaptive weighted fusionfall behavior recognitionUWB radar

Identifiers

PMID39204988

DOI10.3390/s24165294

PMCPMC11359866

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.