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Body Area Networks: Intelligence, Sensing and Communication

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 18 August 2026 | Viewed by 2468

Special Issue Editors

Graduate School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Japan
Interests: signal processing; Internet of Things (IoT); interactive media

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our upcoming Special Issue on “Body Area Networks: Intelligence, Sensing and Communication.”

Body Area Networks (BANs), also known as Wireless Body Area Networks (WBANs), are emerging as key enablers of intelligent, continuous, and non-invasive monitoring systems. With the widespread adoption of wearable and implantable devices, BANs are playing an increasingly vital role in diverse applications such as health monitoring, rehabilitation, sports science, assistive technologies, human–structure co-monitoring systems, and interactive gaming.

This Special Issue aims at highlighting recent advances in intelligent signal processing, sensor and device design, wireless communication, and cross-layer system integration within the BAN framework. We also encourage contributions that explore cutting-edge and exploratory directions, such as the incorporation of Artificial Intelligence (AI), particularly Large Language Models (LLMs), as well as multimodal interaction technologies tailored for BAN-enabled systems.

Of particular interest are interdisciplinary studies exploring wearable AI, human activity understanding, and real-time body interaction in domains such as personalized health, context-aware computing, co-monitoring systems, and embodied game interaction where BANs facilitate the sensing and analysis of both the human body and associated wearable or robotic structures for enhanced monitoring, safety, privacy, and interaction.

Both original research and review articles are welcome. Relevant areas include, but are not limited to, the following:

  • Intelligent health management and signal processing;
  • Wearable and implantable sensor systems for health and motion monitoring;
  • Human activity recognition, gesture tracking, and behavior analysis;
  • Structural health monitoring and human–structure co-monitoring systems;
  • Assistive and accessibility-oriented applications using BANs;
  • LLMs AI for BAN-enhanced systems and user interaction;
  • BAN-based interaction in VR/AR, physical computing, and exergames;
  • Signal fusion from IMU, EMG, EEG, ECG, piezoelectric, and optical sensors;
  • Edge AI and federated learning for on-device BAN applications;
  • Localization and wireless communication;
  • BAN-enabled assistive and accessibility technologies;
  • BAN applications in VR/AR, sports, rehabilitation, and gaming;
  • Standardization, deployment challenges, and scalability issues;
  • Security, privacy, and trust with BAN.

Dr. Xiang Li
Dr. Lei Jing
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 250 words) can be sent to the Editorial Office for assessment.

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. Sensors 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 2600 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

  • body area networks (BANs)
  • wearable sensors
  • signal processing
  • structural health monitoring (SHM)
  • human–structure co-monitoring
  • artificial intelligence (AI)
  • large language models (LLMs)
  • human activity recognition
  • sensor fusion and multimodal data
  • embodied game interaction

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Published Papers (3 papers)

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Research

21 pages, 24921 KB  
Article
On-Body and Off-Body Communications: A Comparative Study Between Hardware and Simulations
by Drishti Oza, Alberto Gallegos Ramonet, Masami Yoshida and Taku Noguchi
Sensors 2026, 26(8), 2561; https://doi.org/10.3390/s26082561 - 21 Apr 2026
Viewed by 478
Abstract
The IEEE 802.15.6 standard defines wireless body area networks (WBANs) for communication in, on, and around the human body. However, commercially available hardware platforms that support direct experimental validation of IEEE 802.15.6-oriented WBAN studies remain limited. As a result, much WBAN research still [...] Read more.
The IEEE 802.15.6 standard defines wireless body area networks (WBANs) for communication in, on, and around the human body. However, commercially available hardware platforms that support direct experimental validation of IEEE 802.15.6-oriented WBAN studies remain limited. As a result, much WBAN research still relies on simulations or custom-built transceivers, leaving the practical validity of simulation results uncertain. In this study, we evaluated a configurable radio platform for GMSK-based narrowband WBAN PHY validation in the 420–450 MHz band by comparing theoretical calculations, ns-3 simulation results, and hardware measurements. Evaluations covered both on-body and off-body scenarios at transmit powers from −15 to −25 dBm. Our key findings are as follows: (1) lower transmit power consistently decreases the communication range in both simulated and hardware environments; (2) degradation trends in packet success rate are similar for both environments, supporting simulation credibility; and (3) in the off-body scenario, ns-3 simulations overestimate the communication range by approximately 10 m compared to hardware under identical conditions. The publicly available simulation framework facilitates reproducible WBAN research. Our results confirm that our ns-3 implementation can be used effectively to approximate key GMSK-based WBAN PHY behaviors in realistic conditions while identifying specific differences in range estimates. Full article
(This article belongs to the Special Issue Body Area Networks: Intelligence, Sensing and Communication)
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20 pages, 1314 KB  
Article
Nash Bargaining-Based Hybrid MAC Protocol for Wireless Body Area Networks
by Haoru Su, Jiale Yang, Rong Li and Jian He
Sensors 2026, 26(3), 967; https://doi.org/10.3390/s26030967 - 2 Feb 2026
Cited by 1 | Viewed by 520
Abstract
Wireless Body Area Network (WBAN) is an emerging medical health monitoring technology. However, WBANs encounter critical challenges in balancing reliability, energy efficiency, and Quality of Service (QoS) requirements for life-critical medical data. The design of its Medium Access Control (MAC) protocol has challenges [...] Read more.
Wireless Body Area Network (WBAN) is an emerging medical health monitoring technology. However, WBANs encounter critical challenges in balancing reliability, energy efficiency, and Quality of Service (QoS) requirements for life-critical medical data. The design of its Medium Access Control (MAC) protocol has challenges since dynamic body-shadowing effects and heterogeneous traffic patterns. In this paper, we propose the Nash Bargaining Rate-optimization MAC (NBR-MAC), a hybrid MAC protocol that integrates TDMA-based Guaranteed Time Slots (GTS) with CSMA/CA-based contention access. Unlike traditional schemes, we model the rate allocation as an Asymmetric Nash Bargaining Game, introducing a rigorous disagreement point to guarantee minimum service for critical nodes. The utility function is normalized to resolve dimensional inconsistencies, incorporating sensor priority, buffer status, and channel quality. The Nash Bargaining solution is derived after proving convexity and verifying the axioms. Superframe time slots are allocated based on sensor data priority. Simulation results demonstrate that the proposed protocol enhances transmission success ratio and throughput while reducing packet age and energy consumption under different load conditions. Full article
(This article belongs to the Special Issue Body Area Networks: Intelligence, Sensing and Communication)
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22 pages, 8300 KB  
Article
Sign2Story: A Multimodal Framework for Near-Real-Time Hand Gestures via Smartphone Sensors to AI-Generated Audio-Comics
by Gul Faraz, Lei Jing and Xiang Li
Sensors 2026, 26(2), 596; https://doi.org/10.3390/s26020596 - 15 Jan 2026
Viewed by 820
Abstract
This study presents a multimodal framework that uses smartphone motion sensors and generative AI to create audio comics from live news headlines. The system operates without direct touch or voice input, instead responding to simple hand-wave gestures. The system demonstrates potential as an [...] Read more.
This study presents a multimodal framework that uses smartphone motion sensors and generative AI to create audio comics from live news headlines. The system operates without direct touch or voice input, instead responding to simple hand-wave gestures. The system demonstrates potential as an alternative input method, which may benefit users who find traditional touch or voice interaction challenging. In the experiments, we investigated the generation of comics on based on the latest tech-related news headlines using Really Simple Syndication (RSS) on a simple hand wave gesture. The proposed framework demonstrates extensibility beyond comic generation, as various other tasks utilizing large language models and multimodal AI could be integrated by mapping them to different hand gestures. Our experiments with open-source models like LLaMA, LLaVA, Gemma, and Qwen revealed that LLaVA delivers superior results in generating panel-aligned stories compared to Qwen3-VL, both in terms of inference speed and output quality, relative to the source image. These large language models (LLMs) collectively contribute imaginative and conversational narrative elements that enhance diversity in storytelling within the comic format. Additionally, we implement an AI-in-the-loop mechanism to iteratively improve output quality without human intervention. Finally, AI-generated audio narration is incorporated into the comics to create an immersive, multimodal reading experience. Full article
(This article belongs to the Special Issue Body Area Networks: Intelligence, Sensing and Communication)
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