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Electronics
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Advances in Wearable Computing: Connectivity, Security, and Applications
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Advances in Wearable Computing: Connectivity, Security, and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 1122

Special Issue Editors

Dr. Kamen Ivanov

E-Mail Website
Guest Editor
Institute of Electronics, Bulgarian Academy of Sciences, 72 Tsarigradsko Chaussee Blvd., 1784 Sofia, Bulgaria
Interests: body sensor networks; wearable computing; biomedical electronics and signal processing
Dr. Qimeng Li

E-Mail Website
Guest Editor
Institute of High-Performance Computing and Networks, National Research Council, Via P. Bucci, 7-11C, 87036 Rende, Italy
Interests: body sensor networks; Internet of Things; wearable computing

Special Issue Information

Dear Colleagues,

Wearable devices are increasingly vital for promoting health and reshaping health culture. By providing enhanced access to personal physical activity and physiological metrics, these devices empower individuals to take greater control of their well-being. This trend has driven interest in multimodal health monitoring, the quantified self, and personalized healthcare.

As wearable technology advances, innovations are focused on making these devices more practical, user-friendly, and seamlessly integrated into daily life. This evolution emphasizes the importance of user acceptance, reliability, effectiveness, accessibility, and the secure processing of personal information associated with wearable systems.

In the realm of medical wearable sensor data analytics, artificial intelligence is taking on an increasingly pivotal role, with large language models being at the forefront of this progress. The development of advanced models with high capabilities in processing diverse biomedical data relies on inputs from thousands of personal wearable devices, with these models typically operating in the cloud. Conversely, edge devices provide users with greater autonomy but necessitate optimized, highly personalized recognition models. Additionally, diverse wearables can dynamically interconnect and collaborate to achieve optimal analysis results, along with enhancing the overall efficiency and reliability of monitoring.

In this rapidly advancing landscape, key aspects of biomedical data processing, such as feature extraction, multimodal sensor data fusion, model and data optimization, and secure data handling, remain essential for successful practical deployment.

This Special Issue will showcase the latest scientific advancements in the dynamic field of wearable devices. We invite submissions on, but not limited to, the following aspects:

  • Physical and physiological activity signal processing in wearables;
  • Multimodal sensor fusion;
  • Artificial intelligence for wearable devices;
  • Large language models for wearable biomedical data processing;
  • Quantified self, personalized healthcare, and health parameter monitoring through wearables;
  • Biometric applications based on data from wearable sensors;
  • Connectivity and security in body sensor networks;
  • User acceptance of wearable systems.

We welcome all types of manuscripts, including original, applied, fundamental, translational, and clinical studies; review articles; and methodological papers.

Dr. Kamen Ivanov
Dr. Qimeng Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • physiological signal processing
  • gait analysis
  • activity recognition
  • sports performance evaluation
  • identity recognition
  • wearable computing
  • body sensor networks
  • multimodal sensor data fusion
  • artificial neural networks
  • user acceptance

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

16 pages, 937 KB  
Article
Analysis of Sensor Location and Time–Frequency Feature Contributions in IMU-Based Gait Identity Recognition
by Fangyu Liu, Hao Wang, Xiang Li and Fangmin Sun
Electronics 2025, 14(19), 3905; https://doi.org/10.3390/electronics14193905 - 30 Sep 2025
Viewed by 146
Abstract
Inertial measurement unit (IMU)-based gait biometrics have attracted increasing attention for unobtrusive identity recognition. While recent studies often fuse signals from multiple sensor positions and time–frequency features, the actual contribution of each sensor location and signal modality remains insufficiently explored. In this work, [...] Read more.
Inertial measurement unit (IMU)-based gait biometrics have attracted increasing attention for unobtrusive identity recognition. While recent studies often fuse signals from multiple sensor positions and time–frequency features, the actual contribution of each sensor location and signal modality remains insufficiently explored. In this work, we present a comprehensive quantitative analysis of the role of different IMU placements and feature domains in gait-based identity recognition. IMU data were collected from three body positions (shank, waist, and wrist) and processed to extract both time-domain and frequency-domain features. An attention-gated fusion network was employed to weight each signal branch adaptively, enabling interpretable assessment of their discriminative power. Experimental results show that shank IMU dominates recognition accuracy, while waist and wrist sensors primarily provide auxiliary information. Similarly, the contribution of time-domain features to classification performance is the greatest, while frequency-domain features offer complementary robustness. These findings illustrate the importance of sensor and feature selection in designing efficient, scalable IMU-based identity recognition systems for wearable applications. Full article
(This article belongs to the Special Issue Advances in Wearable Computing: Connectivity, Security, and Applications)
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14 pages, 839 KB  
Article
MMFA: Masked Multi-Layer Feature Aggregation for Speaker Verification Using WavLM
by Uijong Lee and Seok-Pil Lee
Electronics 2025, 14(19), 3857; https://doi.org/10.3390/electronics14193857 - 29 Sep 2025
Viewed by 322
Abstract
Speaker verification (SV) is a core technology for security and personalized services, and its importance has been growing with the spread of wearables such as smartwatches, earbuds, and AR/VR headsets, where privacy-preserving on-device operation under limited compute and power budgets is required. Recently, [...] Read more.
Speaker verification (SV) is a core technology for security and personalized services, and its importance has been growing with the spread of wearables such as smartwatches, earbuds, and AR/VR headsets, where privacy-preserving on-device operation under limited compute and power budgets is required. Recently, self-supervised learning (SSL) models such as WavLM and wav2vec 2.0 have been widely adopted as front ends that provide multi-layer speech representations without labeled data. Lower layers contain fine-grained acoustic information, whereas higher layers capture phonetic and contextual features. However, conventional SV systems typically use only the final layer or a single-step temporal attention over a simple weighted sum of layers, implicitly assuming that frame importance is shared across layers and thus failing to fully exploit the hierarchical diversity of SSL embeddings. We argue that frame relevance is layer dependent, as the frames most critical for speaker identity differ across layers. To address this, we propose Masked Multi-layer Feature Aggregation (MMFA), which first applies independent frame-wise attention within each layer, then performs learnable layer-wise weighting to suppress irrelevant frames such as silence and noise while effectively combining complementary information across layers. On VoxCeleb1, MMFA achieves consistent improvements over strong baselines in both EER and minDCF, and attention-map analysis confirms distinct selection patterns across layers, validating MMFA as a robust SV approach even in short-utterance and noisy conditions. Full article
(This article belongs to the Special Issue Advances in Wearable Computing: Connectivity, Security, and Applications)
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