Human Activity Recognition via Score Level Fusion of Wi-Fi CSI Signals.

Gunsik Lim, Beomseok Oh, Donghyun Kim, Kar-Ann Toh

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Deep-Learning-Based Baseline Evaluation of Public WiFi CSI Datasets for Contactless RF-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2026
Article

4 · The record

Corrections and comments

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.

Gunsik LimSchool of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.

Beomseok OhDepartment of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

Donghyun KimSchool of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.ORCID 0000-0003-1960-0527

Kar-Ann TohSchool of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.

Funding

National Research Foundation of Korea NRF-2021R1A2C1093425 and NRF-2022R1A4A2000748

6 · The paper itself

Abstract

Wi-Fi signals are ubiquitous and provide a convenient, covert, and non-invasive means of recognizing human activity, which is particularly useful for healthcare monitoring. In this study, we investigate a score-level fusion structure for human activity recognition using the Wi-Fi channel state information (CSI) signals. The raw CSI signals undergo an important preprocessing stage before being classified using conventional classifiers at the first level. The output scores of two conventional classifiers are then fused via an analytic network that does not require iterative search for learning. Our experimental results show that the fusion provides good generalization and a shorter learning processing time compared with state-of-the-art networks.

Indexed as

Human ActivitiesLearningHumansRecognition, Psychologyhuman activity recognitionscore-level fusionWi-Fi CSI signals

Identifiers

PMID37631828

DOI10.3390/s23167292

PMCPMC10459961

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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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.