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Intelligent Biomedical Systems: The Convergence of Sensors, Signal Processing, and Machine Learning

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1128

Special Issue Editor


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Guest Editor
Department of Metrology and Electronics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, Mickiewicza 30, 30-059 Kraków, Poland
Interests: biomedical signal processing, especially in the context of neurodegenerative disease diagnostics; machine learning applications in healthcare, including voice and facial analysis for patient monitoring; development of mixed reality technologies for early disease detection and health monitoring
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Special Issue Information

Dear Colleagues,

This Special Issue highlights the integration of sensing technologies, advanced signal processing algorithms, and machine learning techniques to drive innovation in healthcare. By combining these fields, we will tackle critical challenges in biomedical science, enhance disease detection, and promote personalized treatment. We invite contributions focusing on novel biosensors, AI-driven diagnostic systems, wearable health monitoring devices, and clinical applications that improve patient outcomes in both clinical and remote settings.

Dr. Daria Hemmerling
Guest Editor

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Keywords

  • biomedical sensors
  • machine learning
  • signal processing
  • wearable devices
  • health monitoring
  • artificial intelligence
  • digital biomarkers
  • telemedicine
  • precision medicine
  • edge computing

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Published Papers (1 paper)

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Research

26 pages, 7667 KB  
Article
GRU-Based Deep Multimodal Fusion of Speech and Head-IMU Signals in Mixed Reality for Parkinson’s Disease Detection
by Daria Hemmerling, Milosz Dudek, Justyna Krzywdziak, Magda Żbik, Wojciech Szecowka, Mateusz Daniol, Marek Wodzinski, Monika Rudzinska-Bar and Magdalena Wojcik-Pedziwiatr
Sensors 2026, 26(1), 269; https://doi.org/10.3390/s26010269 - 1 Jan 2026
Cited by 1 | Viewed by 789
Abstract
Parkinson’s disease (PD) alters both speech and movement, yet most automated assessments still treat these signals separately. We examined whether combining voice with head motion improves discrimination between patients and healthy controls (HC). Synchronous measurements of acoustic and inertial signals were collected using [...] Read more.
Parkinson’s disease (PD) alters both speech and movement, yet most automated assessments still treat these signals separately. We examined whether combining voice with head motion improves discrimination between patients and healthy controls (HC). Synchronous measurements of acoustic and inertial signals were collected using a HoloLens 2 headset. Data were obtained from 165 participants (72 PD/93 HC), following a standardized mixed-reality (MR) protocol. We benchmarked single-modality models against fusion strategies under 5-fold stratified cross-validation. Voice alone was robust (pooled AUC ≈ 0.865), while the inertial channel alone was near chance (AUC ≈ 0.497). Fusion provided a modest but repeatable improvement: gated early-fusion achieved the highest AUC (≈0.875), cross-attention fusion was comparable (≈0.873). Gains were task-dependent. While speech-dominated tasks were already well captured by audio, tasks that embed movement benefited from complementary inertial data. Proposed MR capture proved feasible within a single session and showed that motion acts as a conditional improvement factor rather than a sole predictor. The results outline a practical path to multimodal screening and monitoring for PD, preserving the reliability of acoustic biomarkers while integrating kinematic features when they matter. Full article
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