Progress in Biosensors for Biomedical Engineering Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 153

Editor


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Guest Editor
Electrical and Computer Engineering Department, University of Memphis, Memphis, TN 38152, USA
Interests: biosensors; bio-instrumentation and medical devices; healthcare AI; wearable bioelectronics and IoT; neuro-engineering (fNIRS/EEG); multimodal sensor fusion; digital twins and predictive modeling

Special Issue Information

Dear Colleagues,

Biosensors are transitioning from isolated laboratory tools into active partners in clinical decision-making, shifting medicine away from periodic hospital visits toward continuous, personalized health management. By converting complex physiological, biochemical, and neurological signals into clean, interpretable digital data, these technologies bridge the gap between biological systems and digital medicine.

This Special Issue, entitled "Progress in Biosensors for Biomedical Engineering Applications," highlights the recent interdisciplinary work driving these platforms forward. To keep the scope wide and inviting, we welcome submissions that address both physical sensor hardware and the intelligent computing systems that process their outputs.

On the hardware side, we welcome work on diverse transduction modalities, including optical, electrochemical, and acoustic systems as well as advancements in novel bioreceptors, flexible or stretchable substrates, biocompatible interfaces, and point-of-care microfluidic chips. Concurrently, to reflect the digital health transition, we encourage manuscripts exploring computational methods. This includes applying practical machine learning and deep learning models for biosignal processing, noise/artifact removal, and real-time monitoring. We also welcome work on multimodal sensor fusion, neuro-engineering interfaces (such as EEG and fNIRS), digital twins, and predictive analytics that turn raw data streams into robust clinical insights.

By bringing together materials science, device fabrication, and practical computing, this issue aims to outline the practical future of bio-integrated platforms. We invite academic, clinical, and industrial researchers to share their latest findings, particularly those addressing translation, scale-up, and real-world validation challenges.

Dr. Manob Jyoti Saikia
Guest Editor

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Keywords

  • biosensors and bio-instrumentation
  • wearable and implantable bioelectronics
  • healthcare AI and machine learning
  • biosignal processing (EEG/ECG/fNIRS)
  • multimodal sensor fusion
  • digital twins and predictive healthcare
  • point-of-care diagnostics
  • flexible and biocompatible materials

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Published Papers (1 paper)

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Research

41 pages, 4581 KB  
Article
Multimuscle Surface-EMG Characterization of Upper-Limb Fatigue During Repetitive Haptic Interaction for Health 5.0 Applications
by Mohammad Alja’afreh, Nasser Mustafa and Ali Karime
Bioengineering 2026, 13(9), 982; https://doi.org/10.3390/bioengineering13090982 - 26 Aug 2026
Abstract
Health 5.0 increasingly involves medical robots and haptic systems that sustain physical interaction with patients and clinicians. This proof-of-concept study examined fatigue-related surface electromyography (sEMG) spectral changes during a 400 s repetitive haptic-writing task in 20 adults. Five upper-limb muscles were monitored at [...] Read more.
Health 5.0 increasingly involves medical robots and haptic systems that sustain physical interaction with patients and clinicians. This proof-of-concept study examined fatigue-related surface electromyography (sEMG) spectral changes during a 400 s repetitive haptic-writing task in 20 adults. Five upper-limb muscles were monitored at 1000 Hz, and mean frequency (MNF) and median frequency (MDF) trajectories were summarized by fitted start-to-end spectral decline and combined into arm-level indices. MDF had the higher association with the archived participant-level fatigue-analysis score (r=0.954 versus r=0.783; Δr=0.171; Holm-adjusted p=0.0035). Small-sample influence analysis supported the stability of this within-sample ordering: after omitting each participant in turn, Δr remained positive in 20/20 analyses (range 0.125–0.218), although the exact paired label-swap sensitivity test remained inconclusive (p=0.082). Exploratory leave-one-participant-out calibration produced lower held-out error for MDF (MAE 4.38, RMSE 5.87 percentage points) than for MNF (MAE 9.24, RMSE 12.02 percentage points). The raw seven-category Q2 responses and the archived 0–100 analysis score are reported as separate data products because they are not numerically equivalent under direct linear rescaling. Accordingly, the results identify MDF as the more promising spectral summary for prospective validation in this task, rather than establishing universal superiority or numerical replacement of subjective fatigue. The experiment was a laboratory haptic-writing study and did not test a surgical robot, rehabilitation robot, patient population, or clinical controller; Health 5.0 is therefore presented as a translational motivation rather than a demonstrated application. Full article
(This article belongs to the Special Issue Progress in Biosensors for Biomedical Engineering Applications)
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