Bioengineering Innovations for Physiological Monitoring and Human-Centered Health Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 257

Editors


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Guest Editor
Neurosciences Division, CIMA, University of Navarra, 31008 Pamplona, Navarra, Spain
Interests: bioengineering; brain diseases; neurophysiology; neurotechnology; signal analysis; ML/AI; embedded systems; complex systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
Interests: biomechanics; ergonomics; signal processing; rehabilitation; wearable technology; assistive devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on translational bioengineering solutions that leverage physiological signal monitoring, intelligent data analysis, and user-centered technologies to enhance health, mobility, and performance. By integrating advances in biomechanics and advanced sensing, including, but not limited to, wearables, vision-based systems, and artificial intelligence (AI), this Special Issue highlights innovations with real-world impacts across clinical, occupational, and everyday environments. A core theme is monitoring and analysis of physiological signals, such as muscle activity (EMG), heart rate (ECG), respiration, oxygen intake, sleep patterns, and body kinetics and kinematics, to support health monitoring and decision-making. These data streams, when captured through advanced sensing technologies, enable personalized care, early intervention, and functional evaluation in diverse settings. We invite contributions in the following areas:

  • Physiological Signal Monitoring and Wearable Systems
    • Development and application of sensors for real-time tracking of physiological and biomechanical metrics.
    • Multi-modal biomechanical sensing platforms suited for use in clinics, workplaces, homes, or athletic environments.
  • Advanced Signal Processing and AI Integration
    • Use of machine learning, deep learning, and forecasting techniques to enhance data interpretation.
    • Predictive analytics for injury prevention, rehabilitation planning, and performance optimization.
    • Real-time feedback systems for healthy living and training (e.g., gait correction, posture improvement, or motor control training).
  • Accessibility and Low-Cost Technology
    • Affordable, energy-efficient, and easy-to-use systems designed for use in resource-limited settings.
    • Scalable health monitoring solutions for underserved populations or aging communities.
  • Human-Centered Design and Interactive Interfaces
    • Ergonomic considerations in wearable device development, including attachment, comfort, and usability.
    • Evaluation of extended reality systems, including virtual and augmented reality for interactive rehabilitation, skill acquisition, and telehealth.
    • Design of interfaces to support individuals with physical or cognitive impairments.
  • Human Performance in Occupational, Sports, and Clinical Settings
    • Wearables for monitoring biomechanical impacts, workload, fatigue, and recovery.
    • Studies centered on approaches for detecting and reducing the risk of musculoskeletal injury.
    • Data-driven insights to enhance training.
  • Assistive and Rehabilitative Technologies
    • Smart prosthetics, exoskeletons, orthotics, and soft robotics with physiological feedback for adaptive control.
    • Technologies aimed at restoring function, increasing independence, and improving quality of life.

We welcome submissions that emphasize technical innovation, clinical or practical relevance, and inclusive design. Contributions should ideally demonstrate how the proposed bioengineered solutions can be applied to create accessible, scalable, and impactful tools that support a wide range of users, from patients and clinicians to athletes and workers.

Dr. Miguel Valencia
Dr. Pranav Madhav Kuber
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biomechanics
  • human movement science
  • machine learning
  • physiological signal acquisition
  • data science
  • assistive technology

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Published Papers (1 paper)

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Review

27 pages, 953 KB  
Review
Detecting and Improving Human Cognitive State in Real-Time Using Data-Driven Adaptive Systems: A Systematic Review
by Abhineet Rajendra Kulkarni and Pranav Madhav Kuber
Bioengineering 2026, 13(7), 734; https://doi.org/10.3390/bioengineering13070734 (registering DOI) - 24 Jun 2026
Abstract
Changes in human attention, workload, or alertness over time can affect task performance and may even increase the risk of injury. Detecting these changes in real time can be beneficial in improving system performance and safety. We reviewed 27 studies that developed models [...] Read more.
Changes in human attention, workload, or alertness over time can affect task performance and may even increase the risk of injury. Detecting these changes in real time can be beneficial in improving system performance and safety. We reviewed 27 studies that developed models to sense physiological signals, classify one’s cognitive state, and deliver automated intervention. Interventions included providing real-time feedback, adjusting the task’s difficulty, or modifying automation levels across driving, education, rehabilitation, and human–robot collaboration applications. The findings showed that electroencephalography (EEG) sensors were used in 70% of studies, with attention (56%) and mental workload (26%) considered as the most targeted cognitive states. Within-subject classification reached 81.85–95.81% for multi-class tasks in laboratory settings. The most common interventions included neurofeedback display (30%) and task difficulty adjustment (19%), while automation adjustment was less frequent (11%). Only 33% of studies mentioned a latency of 15 milliseconds to 2.5 s, and all systems operated reactively by detecting cognitive states after their onset rather than anticipating them. The provided recommendations focus on the detection of multiple interacting cognitive states and predictive cognitive state trajectories. This review presents key directions for future research and provides a foundation for designing more effective cognitive state adaptive systems. Full article
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