• WiMTI: A multitask learning mo...

Treffer: WiMTI: A multitask learning model for WiFi-based identity and posture recognition.

Title:
WiMTI: A multitask learning model for WiFi-based identity and posture recognition.
Authors:
Zhou Z; DUT School of Software Technology & DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China. Electronic address: zhouzhiyi2020@mail.dlut.edu.cn., Lu X; DUT School of Software Technology & DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China. Electronic address: luxx@mail.dlut.edu.cn., Lu B; DUT School of Software Technology & DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China. Electronic address: bingxian.lu@dlut.edu.cn., Chen Y; DUT School of Software Technology & DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China. Electronic address: chenyuan@mail.dlut.edu.cn., Wen H; DUT School of Software Technology & DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China. Electronic address: haoranwen@mail.dlut.edu.cn., Wang L; DUT School of Software Technology & DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian, Liaoning, China. Electronic address: lei.wang@dlut.edu.cn.
Source:
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Mar; Vol. 195, pp. 108242. Date of Electronic Publication: 2025 Oct 22.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York : Pergamon Press, [c1988-
MeSH Terms:
Posture*/physiology , Pattern Recognition, Automated*/methods , Machine Learning* , Neural Networks, Computer*, Humans ; Algorithms
Contributed Indexing:
Keywords: Channel state information; Identity and posture recognition; Multitask learning; WiFi-based sensing
Entry Date(s):
Date Created: 20251029 Date Completed: 20260124 Latest Revision: 20260128
Update Code:
20260130
DOI:
10.1016/j.neunet.2025.108242
PMID:
41161203
Database:
MEDLINE

Weitere Informationen

WiFi-based identity recognition has attracted considerable attention due to its nonintrusive nature and ability to preserve privacy, unlike traditional camera-based or wearable-sensor-based methods. However, variations in human posture can severely disrupt the stability of WiFi signal characteristics, notably reducing the accuracy and reliability of WiFi-based identity recognition systems. Therefore, we propose WiMTI, a novel multitask learning (MTL) model designed for simultaneous identity and posture recognition. By explicitly capturing posture-related features through MTL, WiMTI effectively reduces the negative impact of posture variations on identity recognition. Specifically, WiMTI employs dynamic cross-stitch units for adaptive feature fusion and integrates fractal dimension analysis to enhance feature representation. Experimental results show that WiMTI achieves state-of-the-art average accuracies of 98.25% for identity recognition and 92.83% for posture recognition. These results demonstrate WiMTI's robustness and effectiveness, making it suitable for practical applications such as access control and caregiving.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.