Biomarkers of Disability and Movement Disorders: Insights from Wearable Devices

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Wearable Biosensors".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 380

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Special Issue Information

Dear Colleagues,

The identification of disability and movement disorder risk factors is essential for implementing preventive interventions and improving a person’s quality of life. Wearable technology has emerged as a promising tool for monitoring functional decline and disorder-related movement alterations through continuous, real-world data collection. By leveraging accelerometers, gyroscopes, and other biosensors, these devices provide objective and real-time assessments, surpassing traditional self-reported measures. Moreover, there is an increasing need to establish which parameters should be monitored in different target populations, such as older adults, stroke survivors, or patients with chronic musculoskeletal diseases.

This Special Issue explores the potential of wearable devices that detect biomarkers of disability and movement disorders, such as changes in movement patterns, posture, and physical activity levels. This collection aims to present work that will enable advancements in wearable technology, the application of wearable technology in healthcare, and future efforts to improve the early detection of disability and movement disorders. By bridging the gap between technology and healthcare, wearable devices hold significant potential for promoting independent living among adults with conditions affecting their fundamental movement skills. We invite authors to contribute research articles and review papers to this Special Issue, providing them with an opportunity to showcase their work, share valuable insights, and highlight recent advancements in the field of biosensors. These contributions will help foster knowledge exchange and drive innovation within the biosensor research community.

Prof. Dr. Andreia S. P. Sousa
Guest Editor

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Keywords

  • wearable biosensors
  • prediction
  • disability
  • movement disorders
  • rehabilitation
  • biomechanics
  • environmental detection
  • biomarkers
  • real-time monitoring

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Published Papers (1 paper)

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Research

26 pages, 6690 KB  
Article
Head-Specific Spatial Spectra of Electroencephalography Explained: A Sphara and BEM Investigation
by Uwe Graichen, Sascha Klee, Patrique Fiedler, Lydia Hofmann and Jens Haueisen
Biosensors 2025, 15(9), 585; https://doi.org/10.3390/bios15090585 - 6 Sep 2025
Viewed by 227
Abstract
Electroencephalography (EEG) is a non-invasive biosensing platform with a spatial-frequency content that is of significant relevance for a multitude of aspects in the neurosciences, ranging from optimal spatial sampling of the EEG to the design of spatial filters and source reconstruction. In the [...] Read more.
Electroencephalography (EEG) is a non-invasive biosensing platform with a spatial-frequency content that is of significant relevance for a multitude of aspects in the neurosciences, ranging from optimal spatial sampling of the EEG to the design of spatial filters and source reconstruction. In the past, simplified spherical head models had to be used for this analysis. We propose a method for spatial frequency analysis in EEG for realistically shaped volume conductors, and we exemplify our method with a five-compartment Boundary Element Method (BEM) model of the head. We employ the recently developed technique for spatial harmonic analysis (Sphara), which allows for spatial Fourier analysis on arbitrarily shaped surfaces in space. We first validate and compare Sphara with the established method for spatial Fourier analysis on spherical surfaces, discrete spherical harmonics, using a spherical volume conductor. We provide uncertainty limits for Sphara. We derive relationships between the signal-to-noise ratio (SNR) and the required spatial sampling of the EEG. Our results demonstrate that conventional 10–20 sampling might misestimate EEG power by up to 50%, and even 64 electrodes might misestimate EEG power by up to 15%. Our results also provide insights into the targeting problem of transcranial electric stimulation. Full article
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