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Advanced Implantable, Wearable, and Bio-Integrated Antenna Systems for Next-Generation Healthcare and Wireless Applications

A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Bioelectronics".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 928

Editors


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Guest Editor
Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
Interests: dielectric resonator antenna; MIMO antenna; UWB antenna; implantable antenna; bio-electromagnetics; wearable antenna; millimeter-wave antenna; sub-6 Hz antenna

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Guest Editor
Department of Industrial Engineering and Management, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, Gheorghe Marinescu 38, 540142 Târgu Mureș, Romania
Interests: biomaterials; Ti alloys; antenna material
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Special Issue Information

Dear Colleagues,

The rapid progress of bio-electromagnetics, advanced materials, artificial intelligence (AI), and next-generation wireless technologies is accelerating the development of advanced implantable, wearable, and bio-integrated antenna systems for healthcare and emerging wireless applications. These antennas serve as the electromagnetic core of modern medical devices, supporting continuous physiological monitoring, neural interfacing, targeted therapies, wireless power transfer, and intelligent closed-loop treatment systems. As healthcare shifts toward personalized, minimally invasive, and connected models, there is a growing need for compact, energy-efficient, and biologically compatible antenna solutions. In this context, AI-driven design and optimization are emerging as powerful tools to enhance antenna performance, adaptability, and system intelligence.

Establishing devices that can operate inside or on the human body presents significant challenges. Biological tissues are lossy and electromagnetically complex, leading to detuning, impedance mismatch, absorption losses, and strict safety constraints such as Specific Absorption Rate (SAR). Applications including smart prosthetics, implantable biosensors, capsule endoscopy, microwave imaging, hyperthermia-based cancer therapy, and neuromodulation require broadband or multiband functionality, mechanical flexibility, conformal geometries, and long-term stability under deformation. Machine learning and data-driven techniques are increasingly being explored to model complex tissue interactions, predict performance, and enable adaptive antenna behavior in dynamic biological environments.

Advances in high-permittivity and magneto-dielectric materials, metamaterial-inspired miniaturization, textile and stretchable conductors, additive manufacturing, 3D printing, and bioresorbable electronics are expanding design possibilities, and coupled with multiphysics modeling, realistic anatomical phantoms, and AI-driven optimization, these innovations enable safer, more efficient, and reliable systems. This Special Issue welcomes original research and comprehensive reviews addressing modeling, miniaturization, fabrication, safety-aware design, experimental validation, and system-level integration of antenna technologies for next-generation healthcare and wireless platforms, with particular emphasis on the integration of artificial intelligence and data-driven design methodologies.

Dr. Sumer Singh Singhwal
Prof. Dr. Ildiko Peter
Guest Editors

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Keywords

  • implantable antennas
  • wearable antennas
  • bio-integrated antenna systems
  • bio-electromagnetics
  • in-body and on-body communications
  • MedRadio/MICS/ISM bands
  • Internet of Medical Things (IoMT)
  • antenna miniaturization
  • Specific Absorption Rate (SAR) optimization
  • flexible and stretchable antennas
  • textile and conformal antennas
  • metamaterial-inspired antennas
  • high-permittivity and magneto-dielectric materials
  • wireless power transfer for implants
  • energy-harvesting biomedical devices
  • reconfigurable and tunable antennas
  • microwave imaging and sensing
  • hyperthermia and microwave cancer therapy
  • additive manufacturing and 3D printing
  • multiphysics modeling and AI-based optimization
  • 5G/6G biomedical applications
  • artificial intelligence in electromagnetics

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Published Papers (2 papers)

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Research

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15 pages, 13174 KB  
Article
An Analytical Model of Inertial Gait Parameters for the Development of Robotic Exoskeletons for Lower-Limb Rehabilitation
by Hyun K. Kim, Jungyoon Kim and Jaehyun Park
Electronics 2026, 15(13), 2851; https://doi.org/10.3390/electronics15132851 - 30 Jun 2026
Viewed by 293
Abstract
Robotic lower-limb exoskeletons are an increasingly important tool in the rehabilitation of patients with motor impairments, and their effectiveness depends on how faithfully the device reproduces the natural gait pattern. Inertial measurement units (IMUs) are widely used to acquire body-worn kinematic data for [...] Read more.
Robotic lower-limb exoskeletons are an increasingly important tool in the rehabilitation of patients with motor impairments, and their effectiveness depends on how faithfully the device reproduces the natural gait pattern. Inertial measurement units (IMUs) are widely used to acquire body-worn kinematic data for gait monitoring, but compact, interpretable models linking IMU-derived hip- and knee-flexion features to gait phase under exoskeleton-assisted conditions are still lacking. We collected gait data from two independent experiments: Experiment 1, 20 healthy adults (10 M, 10 F; 22.2 ± 1.9 years) walking freely on level ground, stairs and a ramp with seven Noraxon IMUs; and Experiment 2, six healthy adults (4 M, 2 F; 31.0 ± 8.9 years) walking with and without the Exowalk (HR-02) over-ground exoskeleton with five IMUs. Eight bilateral hip- and knee-flexion features were extracted, and a binary logistic-regression model with stance/swing as the dependent variable was fitted on Experiment 1 and externally cross-validated on Experiment 2. The model classified gait phases with an accuracy of 90.83% (sensitivity 87.50%, specificity 92.50%, positive predictive value 85.37%) on Experiment 1. External validation retained 91.7% accuracy during free walking but dropped to 41.7% under Exowalk-assisted walking, indicating that the device alters the inertial signature of gait. The findings identify swing-phase hip flexion and the minimum swing-phase knee flexion as the kinematic descriptors most predictive of gait phase, and provide quantitative design and control targets for next-generation IMU-instrumented lower-limb rehabilitation exoskeletons. Full article
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Review

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38 pages, 4229 KB  
Review
Global Perspectives on AI-Based Digital Twins in Smart Rehabilitation and Physiotherapy: Convergence of IoMT, Multiphysics Modeling, and Wireless Bio-Integrated Sensing
by Emilia Mikołajewska, Jolanta Masiak, Ewelina Panas, Urszula Rogalla-Ładniak and Dariusz Mikołajewski
Electronics 2026, 15(17), 3795; https://doi.org/10.3390/electronics15173795 - 24 Aug 2026
Viewed by 273
Abstract
Artificial intelligence (AI)-based digital twins (DTs) are emerging as a groundbreaking paradigm in rehabilitation and physiotherapy, enabling the creation of dynamic virtual representations of patients for continuous monitoring, prognostic assessment and personalised therapeutic interventions. This article presents a global, interdisciplinary review of AI-based [...] Read more.
Artificial intelligence (AI)-based digital twins (DTs) are emerging as a groundbreaking paradigm in rehabilitation and physiotherapy, enabling the creation of dynamic virtual representations of patients for continuous monitoring, prognostic assessment and personalised therapeutic interventions. This article presents a global, interdisciplinary review of AI-based DT technologies in rehabilitation settings utilising the Internet of Medical Things (IoMT), with particular emphasis on the integration of wearable and implantable sensor systems in next-generation wireless healthcare applications. The article analyses how multimodal wearable sensors, implantable devices and smart wireless communication networks can support the acquisition of real-time biomechanical and physiological data for adaptive rehabilitation. By combining perspectives from biomedical engineering, physiotherapy, computational intelligence and wireless healthcare systems, this article highlights the emerging opportunities and challenges associated with the creation of scalable digital twin ecosystems for precision rehabilitation. The proposed vision contributes to the development of smart, connected and personalized rehabilitation infrastructures, in line with future paradigms of healthcare and wireless communication. Full article
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