Evidence map›Paper›PMID 40431900›Full record

ArticleSensors (Basel, Switzerland)2025

Self-Supervised WiFi-Based Identity Recognition in Multi-User Smart Environments.

Hamada Rizk, Ahmed Elmogy

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Hamada RizkComputers & Control Engineering Department, Faculty of Engineering, Tanta University, Tanta 31527, Egypt.
Ahmed ElmogyFaculty of Computer Engineering & Sciences, Prince Sattam Ibn Abdelaziz University, Alkharj 16273, Saudi Arabia.ORCID 0000-0001-7282-6598

Funding

Prince Sattam Bin Abdulaziz University 2024/01/28803
6 · The paper itself

Abstract

The deployment of autonomous AI agents in smart environments has accelerated the need for accurate and privacy-preserving human identification. Traditional vision-based solutions, while effective in capturing spatial and contextual information, often face challenges related to high deployment costs, privacy concerns, and susceptibility to environmental variations. To address these limitations, we propose

Indexed as

Artificial IntelligenceWireless TechnologyAlgorithmsHumansPrivacyAI agentsCSIhuman recognitionidentity recognitionself-supervised learningsmart environmentsWiFi sensing

Identifiers

PMID40431900
PMCPMC12115556

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.