Continuous Monitoring: Implantable, Wearable and Remote Sensor Microsystems

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "B1: Biosensors".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 618

Special Issue Editors


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Guest Editor
Department of Precision Engineering, School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-8656, Japan
Interests: tactile sensors; wireless sensing systems

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Guest Editor Assistant
Department of Precision Engineering, School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo, Tokyo 113-8656, Japan
Interests: bioacoustic sensing

Special Issue Information

Dear Colleagues,

The advancement of sensing technologies is transforming how we address diagnostic and environmental challenges. By continuously tracking physiological, ecological, and ambient parameters, these systems enable proactive diagnostics, mitigation, and performance optimization. Leveraging breakthroughs in micro/nanofabrication, flexible substrates, and multimodal sensing, modern platforms deliver exceptional sensitivity, reliability, and long-term stability under dynamic conditions. Advances span from bio-MEMS devices for in vivo diagnostics and fiber-based wearables for motion and biochemical tracking to contactless environmental sensors, showing how the field is expanding at an unprecedented pace.

This Special Issue explores implantable, wearable, and remote microsensors and systems engineered for the uninterrupted monitoring of physiological and environmental metrics. Topics of interest include low-power hardware architectures, biocompatible interfaces, and nonintrusive designs that support seamless, long-term deployment in healthcare and environmental settings. We invite the submission of original research articles presenting novel sensor concepts and designs, system-level integration approaches, and real-world use cases. Even minor innovations can pave the way toward next-generation systems for continuous health management and data-rich environmental analytics.

We look forward to receiving your contributions.

Dr. Jarred Fastier-Wooller
Guest Editor

Dr. Shun Muramatsu
Guest Editor Assistant

Manuscript Submission Information

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Keywords

  • continuous monitoring
  • wearable, flexible, and soft sensors
  • biomedical and implantable sensors
  • remote sensing
  • low power
  • multimodal sensors
  • energy harvesting
  • nonintrusive sensors

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Published Papers (1 paper)

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Research

25 pages, 23200 KB  
Article
A Physics–Data Hybrid Framework Using Uncalibrated Consumer CMOS Vision: Pilot Study on Monocular Automatic TUG Assessment Towards Early Parkinson’s Disease Risk Screening
by Yuxiang Qiu, Xiaodong Sun, Fan Yang, Jarred Fastier-Wooller, Shun Muramatsu, Michitaka Yamamoto and Toshihiro Itoh
Micromachines 2026, 17(5), 523; https://doi.org/10.3390/mi17050523 - 25 Apr 2026
Viewed by 306
Abstract
The Timed Up and Go (TUG) test is a clinical gold standard for assessing elderly mobility, yet its automated deployment in home-monitoring and resource-limited areas is hindered by high hardware costs and expert calibration requirements. This study introduces a Physics–Data Hybrid framework specifically [...] Read more.
The Timed Up and Go (TUG) test is a clinical gold standard for assessing elderly mobility, yet its automated deployment in home-monitoring and resource-limited areas is hindered by high hardware costs and expert calibration requirements. This study introduces a Physics–Data Hybrid framework specifically designed for uncalibrated consumer-grade CMOS cameras, enabling a “plug-and-play” solution for early Parkinson’s disease (PD) risk screening. The proposed pipeline integrates learning-based pose perception with a self-evolving physics model to recover absolute metric-scale motion without manual checkerboard calibration. A noise-adaptive fusion strategy is implemented to reconcile 2D pixel dynamics with 3D kinematic consistency, overcoming the inherent scale ambiguity of monocular vision. Crucially, this framework enables the extraction of high-dimensional spatiotemporal parameters—such as stride length coefficient of variation and mean gait velocity—which provide a finer diagnostic resolution for capturing subtle motor fluctuations than conventional timing-only systems. Results from our pilot study with a cohort of 10 subjects demonstrate that these extracted metric features serve as decisive markers for risk staging simulated by dual-task-induced cognitive-motor-interference, achieving 98% screening accuracy and an overall classification accuracy of 87.32%. This framework provides a robust, low-cost tool for ubiquitous telehealth, potentially supporting early PD risk assessment in underserved populations. Full article
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