• WiMTI: A multitask learning mo...

*Result*: 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

*Further Information*

*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.*