| Journal of Biomedical Systems and Engineering

Published Online: 30 September 2026.

J. Biomed. Syst. Eng., 2026, 1(1), 26805 | Volume 1 Issue 1 (September 2026) | DOI: https://doi.org/10.64189/bse.26805

This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)

Advancing Healthcare Technology: From Edge-AI

Diagnostics to Metagenomic Intelligence and

Biomaterial Sensors

Akash Kumar Bhoi

Department of Electronics and Telecommunication Engineering, Symbiosis Institute of Technology, Pune Campus, Symbiosis

International (Deemed University), Pune, Maharashtra, 412115, India

*Email: eic.jbse@gr-journals.com (Akash Kumar Bhoi)

The intrinsic convergence of computational intelligence, material science, and molecular biology underpins

modern healthcare engineering. At the nexus of this multidisciplinary interaction lies the urgent need for

scalable, real-time diagnostic tools that can operating seamlessly from the planetary scale of metagenomic

tracking to the micro-scale of wearable, edge-deployed physical sensors. Accelerating the translation of clinical

workflows requires bridging the gap between algorithmic models and physical engineering realities.

Addressing these complex healthcare demands necessitates robust software optimization frameworks, highly

sensitive biomaterial substrates, assistive accessibility pipelines, and reference-free deep learning models that

transcend classical alignment bottlenecks.

In response to these critical needs, we introduce the inaugural issue of the Journal of Biomedical Systems and

Engineering - a peer-reviewed, open-access forum dedicated to advancing research at the intersection of

medicine, hardware engineering, data science, and molecular diagnostics. Embracing the paradigm of

translational technology, the journal serves as a multidisciplinary platform for high-impact original research,

comprehensive reviews, and computational proof-of-concepts. Our inaugural issue features four contributions

that collectively span the continuum from individual edge-hardware optimization and localized point-of-care

biosensors to real-time assistive frameworks and deep-learning metagenomic architectures:

Odiba et al. provide a comprehensive review of the integration of Deep Learning (DL) with metagenomic

sequencing for advanced microbial species differentiation.

[1]

The authors evaluate four foundational neural

architecture families - convolutional, recurrent, transformer-based genomic language, and graph neural

networks - across clinical tasks including taxonomic classification, genome-resolved binning, and microbiome-

phenotype association.

[2,3]

The review contrasts these DL methodologies against conventional alignment-based

limitations, specifically highlighting hurdles such as data leakage, domain shift across platforms, and database

reference dependency. Ultimately, they outline the strategic necessity of reference-free, multi-modal

foundation models to achieve biologically robust microbial identification. Basak and Sahu execute a

comprehensive compression-aware ablation study aimed at continuous vital-sign anomaly detection on

wearable and bedside Internet of Medical Things (IoMT) hardware.

[4]

Utilizing a synthetic multi-variate vital-

sign dataset mapped against National Early Warning Score 2 (NEWS2) thresholds, the authors systematically

benchmark a baseline CNN-LSTM network against structured channel pruning, partial dynamic post-training

8-bit integer quantization, and knowledge distillation to a 1,662-parameter GRU student.

[5]

While the baseline

achieved an exceptional F1-score of 0.955, knowledge distillation yielded a massive 40.8-fold reduction in

multiply-accumulate operations while preserving a high F1-score of 0.912. Crucially, the authors separate their

software-sandbox metrics from a rigorous, self-contained implementation protocol designed to guide future

physical edge-hardware deployment and clinical verification. Shifting focus toward molecular-level real-time

monitoring, Kumar et al. present the numerical design and optimization of a highly sensitive, multilayer Surface

Plasmon Resonance (SPR) sensor for label-free urine glucose detection.

[6]

The architecture integrates a calcium

fluoride (CaF₂) prism coupled with a copper plasmonic layer, silicon dioxide (SiO₂), blue phosphorus (BlueP),

and a 2D Franckeite mineral heterostructure to maximize the interaction of the evanescent field with the

analyte.

[7,8]

The optimized configuration demonstrates an extraordinary maximum angular sensitivity of

385.55°/RIU and a peak Figure of Merit (FoM) of 82.31/RIU. This computational proof-of-concept establishes

a vital theoretical baseline for subsequent physical prototyping and clinical translation using real patient urine

samples. Moving from biochemical assays to human-computer interactions, Siddiqui et al. develop a real-time

assistive pipeline combining Indian Sign Language (ISL) alphabet recognition with temporal stabilization.

[9]

accommodate users experiencing involuntary hand movements, such as Parkinson's disease tremors, the

system tracks 21 three-dimensional skeletal joints via a webcam using MediaPipe Hands.

[10]

These landmarks

are mapped to a convolutional neural network (CNN) that achieves a macro F1-score of 99.27% across a 13,000-

image dataset. Operating at an inference rate of ~9.23 frames per second, the prototype integrates a strict 20-

frame consecutive prediction rule to smooth shaky-motion artifacts, delivering real-time text interpretation

and augmented reality (AR) style feedback. Collectively, these inaugural contributions demonstrate that

safeguarding human health relies on a continuous synthesis of precise software optimization, sensitive material

substrates, and scalable machine learning frameworks. They also delineate recurrent translational challenges -

such as domain shift across clinical environments, laboratory-to-field hardware constraints, and the need for

physical experimental validation - which we warmly encourage future contributors to address in upcoming

issues.

The Editorial Office remains dedicated to a rigorous, transparent, and timely peer-review process adhering to

the highest standards of scientific and ethical integrity. We enthusiastically invite biomedical engineers,

computer scientists, material researchers, and clinical technologists to contribute their work and assist in

establishing the Journal of Biomedical Systems and Engineering as a leading global venue for integrative health

technology.

Conflict of Interest

The author is a co-author of one article featured in this issue (Ref. 6); its peer review and editorial handling

were carried out independently of the author.

Artificial Intelligence (AI) Use Disclosure

The author declare that artificial intelligence (AI)-assisted tools were used only for language refinement,

grammar improvement, and manuscript structuring purposes during the preparation of this work. All content

and interpretations were reviewed and verified by the author.

References

[1]

D. Odiba, F. C. Terna, O. G. Uyi, S. Anzaku, O. O. Orole, Integrating Metagenomics and Deep Learning

for Accurate Microbial Species Differentiation, Journal of Biomedical Systems and Engineering, 2026,

1, 26803, doi: 10.64189/bse.26804.

[2]

R. J. Wright, A. M. Comeau, M. G. I. Langille, From defaults to databases: Parameter and database choice

dramatically impact the performance of metagenomic taxonomic classification tools, Microbial

Genomics, 2023, 9, 000949, doi: 10.1099/mgen.0.000949.

[3]

A. Escobar-Zepeda, E. E. Godoy-Lozano, L. Raggi, L. Segovia, E. Merino, R. M. Gutiérrez-Rios, K. Juarez,

A. F. Licea-Navarro, L. Pardo-Lopez, A. Sanchez-Flores, Analysis of sequencing strategies and tools for

taxonomic annotation: Defining standards for progressive metagenomics, Scientific Reports, 2018, 8,

12034, doi: 10.1038/s41598-018-30515-5.

[4]

P. Basak, S. Sahu, Compression-aware vital-sign anomaly detection: A synthetic data ablation of

pruning, quantization, and knowledge distillation, with a protocol for clinical and edge-hardware

extension, Journal of Biomedical Systems and Engineering, 2026, 1, 26801, doi: 10.64189/bse.26801.

[5]

T. Liang, J. Glossner, L. Wang, S. Shi, X. Zhang, Pruning and quantization for deep neural network

acceleration: A survey, Neurocomputing, 2021, 461, 370–403, doi: 10.1016/j.neucom.2021.07.045.

[6]

R. Kumar, L. Garia, B. Brahma, A. K. Bhoi, Enhanced surface plasmon resonance sensor employing

blue phosphorus and franckeite for urine glucose detection, Journal of Biomedical Systems and

Engineering, 2026, 1, 26802, doi: 10.64189/bse.26802.

[7]

N. Mudgal, A. Saharia, A. Agarwal, J. Ali, P. Yupapin, G. Singh, Modeling of Highly Sensitive Surface

Plasmon Resonance (SPR) Sensor for Urine Glucose Detection, Optical and Quantum Electronics,

2020, 52, 307, doi: 10.1007/s11082-020-02427-0.

[8]

A. Shalabney, I. Abdulhalim, Electromagnetic Fields Distribution in Multilayer Thin Film Structures

and the Origin of Sensitivity Enhancement in Surface Plasmon Resonance Sensors, Sensors and

Actuators A: Physical, 2010, 159, 24–32, doi: 10.1016/j.sna.2010.02.005.

[9]

F. Siddiqui, I. Ansari, N. Kazi, J. Khatri, Real-Time ISL to text interpretation and Parkinson’s tremor

correction with augmented reality support, Journal of Biomedical Systems and Engineering, 2026, 1,

26803, doi: 10.64189/bse.26803.

[10]

Z. Deng, Y. Leng, J. Hu, Z. Lin, X. Li, Q. Gao, SML: A Skeleton-based multi-feature learning method for

sign language recognition, Knowledge-Based Systems. 2024, 301, 112288, doi:

10.1016/j.knosys.2024.112288.

Publisher Note: The views, statements, and data in all publications solely belong to the authors and

contributors. GR Scholastic is not responsible for any injury resulting from the ideas, methods, or products

mentioned. GR Scholastic remains neutral regarding jurisdictional claims in published maps and institutional

affiliations.

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which

permits the non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format,

as long as appropriate credit to the original author(s) and the source is given by providing a link to the Creative

Commons License and changes need to be indicated if there are any. The images or other third-party material

in this article are included in the article's Creative Commons License, unless indicated otherwise in a credit line

to the material. If material is not included in the article's Creative Commons License and your intended use is

not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly

from the copyright holder. To view a copy of this License, visit: https://creativecommons.org/licenses/by-

nc/4.0/