Recent Advances in Wearable Bioelectronics in Healthcare/Medical Devices 2026

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biomimetic Design, Constructions and Devices".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 1676

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Guest Editor
Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
Interests: wearable tech; digital health; bioelectronics
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Special Issue Information

Dear Colleagues,

Recent advances in bioelectronics and wearable devices have significantly transformed healthcare, offering innovative solutions for monitoring, diagnosing, and treating various health conditions. Bioelectronic technologies, which integrate biology with electronics, enable precise interactions with biological systems at the molecular and cellular levels. One notable development is flexible, skin-like sensors that monitor vital signs such as heart rate, blood pressure, glucose levels, and even biomarkers for diseases like Parkinson's or Alzheimer’s. These devices leverage advanced materials, such as stretchable polymers and conductive hydrogels, to ensure comfort and durability for long-term wear. Wearable devices, which are powered by artificial intelligence (AI) and machine learning, now analyze real-time data to detect anomalies and predict health risks, enabling early intervention. The integration of wireless communication technologies, including Bluetooth and 5G, facilitates seamless data sharing between patients and healthcare providers, improving telemedicine capabilities. Implantable bioelectronic devices, such as neural interfaces, are also advancing, offering hope for restoring motor function in paralyzed individuals or managing chronic pain through electrical stimulation. Furthermore, energy-harvesting innovations, like sweat-powered or body-heat-based systems, enhance the sustainability of these devices. As bioelectronics and wearable healthcare technologies continue to evolve, they promise a future of personalized, non-invasive, and connected healthcare solutions, fundamentally reshaping patient care.

Dr. Dhruv R. Seshadri
Guest Editor

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Keywords

  • wearable sensors
  • bioelectronics
  • digital therapeutics
  • digital biomarkers
  • remote monitoring
  • human performance
  • epidermal electronics

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Published Papers (1 paper)

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Research

15 pages, 3108 KB  
Article
Prediction of Three-Dimensional Ground Reaction Forces in the Golf Swing Using Wearable Inertial Measurement Units and Biomimetic Deep Learning Models
by Jiayun Li, Ruoyu Wei, Qiantong Xie, Changfa Wu and Yoon Hyuk Kim
Biomimetics 2026, 11(3), 159; https://doi.org/10.3390/biomimetics11030159 - 27 Feb 2026
Viewed by 1183
Abstract
Ground reaction force (GRF) is essential for maintaining dynamic stability and generating power during the golf swing. Traditional GRF assessment relies on force plates, limiting measurement to laboratory environments and restricting evaluation of natural, field-based performance. Recent work has explored wearable inertial measurement [...] Read more.
Ground reaction force (GRF) is essential for maintaining dynamic stability and generating power during the golf swing. Traditional GRF assessment relies on force plates, limiting measurement to laboratory environments and restricting evaluation of natural, field-based performance. Recent work has explored wearable inertial measurement units (IMUs) and data-driven models to estimate GRF during simple locomotor tasks, yet no study has examined whether coupled lower-limb kinematics can predict three-dimensional GRF during complex, high-speed movements such as the golf swing. This study collected bilateral hip, knee, and ankle joint angles from IMUs, along with 3D GRF data, to evaluate five biomimetic deep learning (DL) architectures across seven sensor configurations. The TCN-BiGRU model achieved the highest accuracy (R2 = 0.94 ± 0.02, MRE = 0.044 ± 0.01, NRMSE = 0.064 ± 0.01) among the architectures evaluated in this study, effectively capturing both local and long-range temporal dependencies in human movement. The full bilateral lower-limb configuration yielded the best overall performance, whereas using only the lead leg provided a cost-efficient alternative with minimal loss of accuracy. Among the GRF components, the vertical direction showed the greatest predictive reliability. These findings demonstrate the feasibility and potential of kinematic–force modeling and support the development of wearable, field-ready systems for GRF estimation in dynamic sports environments. Full article
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