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Bioengineering
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Wearable Technologies and Health Informatics: Advancements in Bioengineering
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Wearable Technologies and Health Informatics: Advancements in Bioengineering

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3309

Editors

Dr. Iqram Hussain

E-Mail Website
Guest Editor
Department of Anesthesiology, Weill Cornell Medicine, New York, NY 10065, USA
Interests: IoT; cyber–physical system; big-data; brain–computer interaction; biomedical engineering; health informatics; smart health
Special Issues, Collections and Topics in MDPI journals
Dr. Jahowa Islam

E-Mail Website
Guest Editor
Department of Science, Borough of Manhattan Community College, The City University of New York, New York, NY 10007, USA
Interests: nanomaterials; energy efficiency; electrocatalyst
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This invitation extends to individuals deeply involved in the realms of innovation and progress within the intersection of technology and health. We cordially invite you to contribute your research paper to our esteemed Special Issue centered around "Wearable Technologies and Health Informatics: Advancements in Bioengineering".

This exclusive collection aims to highlight pioneering advancements and breakthrough concepts in various pivotal domains. Your submissions on wearables and health informatics, encompassing bionics, bioelectronics, biomedical engineering, and similarly aligned areas, are eagerly awaited.

Your expertise and contributions in these fields will enrich this Special Issue, providing a comprehensive overview of cutting-edge developments and promising methodologies. Your research has the potential to shape the future landscape of health technology, offering valuable insights and propelling the field toward greater achievements.

We eagerly anticipate your submission, knowing that your work will significantly contribute to the discourse and advancement of biomedical engineering and health informatics. Thank you for considering our invitation, and we eagerly await the opportunity to showcase your innovative contributions within this distinguished Special Issue.

We look forward to receiving your contributions.

Dr. Iqram Hussain
Dr. Jahowa Islam
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

  • wearable sensors
  • health monitoring
  • large language model
  • electronic health record
  • flexible sensors
  • sensors
  • artificial intelligence
  • smart healthcare
  • data privacy
  • machine learning
  • sensor applications
  • energy efficiency
  • health diagnostics
  • wearable signal processing (EEG, ECG, EMG, PPG, and IMU)
  • internet of things (IoT)
  • activity recognition
  • biomedical techniques
  • sleep medicine
  • smart grid
  • data science
  • smart surveillance

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

10 pages, 3399 KB  
Article
Practicality of Using Pressure Sensors and Accelerometers to Quantify Hand Orthosis Compliance at Home
by Devi Baruni Devanand, Matthew D. Gardiner and Angela E. Kedgley
Bioengineering 2026, 13(6), 697; https://doi.org/10.3390/bioengineering13060697 - 18 Jun 2026
Viewed by 450
Abstract
Orthosis compliance monitoring provides insights into effective orthosis design and user wear time. Frequently, patient reports of orthosis use are subjective and often result in overestimation of compliance. Therefore, a tool to objectively observe whether patients wear their orthoses as instructed is vital. [...] Read more.
Orthosis compliance monitoring provides insights into effective orthosis design and user wear time. Frequently, patient reports of orthosis use are subjective and often result in overestimation of compliance. Therefore, a tool to objectively observe whether patients wear their orthoses as instructed is vital. This study assessed the real-world practicality of using an objective compliance monitoring device with a hand orthosis. A device consisting of a pressure sensor and accelerometer was tested by ten healthy volunteers who wore a hand orthosis daily and completed a diary of their wear time and activities for a week. Sensor data obtained from the compliance monitoring device were analysed to discern each user’s orthosis wear time. Differences between estimated wear time and actual wear time were insignificant. Pressure-based wear time estimations had a specificity of 99.3 ± 0.7% and a sensitivity of 80.3 ± 19.2%, whilst acceleration-derived estimations had a specificity of 94.5 ± 6.4% and a sensitivity of 73.2 ± 15.8%. This study demonstrated that orthosis compliance can be monitored outside the laboratory, and, furthermore, this device offers insights into the intensity and frequency of a user’s activities and has the future potential to monitor orthosis fit and forces applied to affected joints using pressure. Full article
(This article belongs to the Special Issue Wearable Technologies and Health Informatics: Advancements in Bioengineering)
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16 pages, 770 KB  
Article
Integrated Analysis of Circadian and Sleep Signatures in Depression and Schizophrenia Using Multi-Day Actigraphy
by Rama Krishna Thelagathoti, Ka-Chun Siu, Hesham H. Ali and Rohan M. Fernando
Bioengineering 2026, 13(4), 383; https://doi.org/10.3390/bioengineering13040383 - 26 Mar 2026
Cited by 1 | Viewed by 892
Abstract
Sleep abnormalities and circadian rhythm disruptions are frequently observed in psychiatric disorders such as depression and schizophrenia. However, most previous studies have examined circadian rhythms and sleep separately, limiting understanding of how these processes interact within individuals. This study examined circadian and sleep [...] Read more.
Sleep abnormalities and circadian rhythm disruptions are frequently observed in psychiatric disorders such as depression and schizophrenia. However, most previous studies have examined circadian rhythms and sleep separately, limiting understanding of how these processes interact within individuals. This study examined circadian and sleep characteristics in depression and schizophrenia compared with healthy controls using multi-day wrist actigraphy. Circadian rhythms were assessed using parametric and non-parametric measures of rest–activity patterns, and sleep metrics were derived using a validated actigraphy-based algorithm. Distinct patterns were observed across diagnostic groups. Schizophrenia showed widespread disruption in daily activity patterns, with altered timing and reduced rhythm strength. Sleep was longer but highly fragmented, with frequent awakenings despite increased time in bed. In contrast, depression showed more limited changes, mainly in activity timing and overall activity levels, while sleep and daily patterns remained closer to controls. A key finding was the identification of distinct circadian–sleep profiles for each condition, with global disruption in schizophrenia and more selective alterations in depression. These findings show that combining circadian and sleep measures provides a clearer understanding of psychiatric disorders and may support monitoring and targeted interventions based on daily behavioral rhythms. Full article
(This article belongs to the Special Issue Wearable Technologies and Health Informatics: Advancements in Bioengineering)
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19 pages, 2043 KB  
Article
Domain-Aware Interpretable Machine Learning Model for Predicting Postoperative Hospital Length of Stay from Perioperative Data: A Retrospective Observational Cohort Study
by Iqram Hussain, Joseph R. Scarpa and Richard Boyer
Bioengineering 2026, 13(2), 147; https://doi.org/10.3390/bioengineering13020147 - 27 Jan 2026
Viewed by 1130
Abstract
Background and Objective: Postoperative hospital length of stay (LOS) reflects surgical recovery and resource demand but remains difficult to predict due to heterogeneous perioperative trajectories. We aimed to develop and validate an interpretable machine learning framework that integrates multimodal perioperative data to accurately [...] Read more.
Background and Objective: Postoperative hospital length of stay (LOS) reflects surgical recovery and resource demand but remains difficult to predict due to heterogeneous perioperative trajectories. We aimed to develop and validate an interpretable machine learning framework that integrates multimodal perioperative data to accurately predict LOS and uncover clinically meaningful drivers of prolonged hospitalization. Methods: We studied 97,937 adult surgical cases from a large perioperative registry. Routinely collected perioperative data included patient demographics, comorbid conditions, preoperative laboratory values, intraoperative physiologic summaries, and procedural characteristics. Length of stay was modeled using a supervised regression approach with internal cross-validation and independent holdout evaluation. Model performance was assessed at both the cohort and individual levels, and explanatory analyses were performed to quantify the contribution of clinically defined perioperative domains. Results: The model achieved R2 = 0.61 and MAE ≈ 1.34 days on the holdout set, with nearly identical cross-validation performance (R2 = 0.60, MAE ≈ 1.34 days). Operative duration, diagnostic complexity, intraoperative hemodynamic variability, and preoperative laboratory indices—particularly albumin and hematocrit—emerged as the strongest determinants of postoperative stay. Patients with shorter recoveries typically had brief operations, stable physiology, and normal laboratory profiles, whereas prolonged hospitalization was linked to complex procedures, malignant or respiratory diagnoses, and lower albumin levels. Conclusions: Interpretable machine learning enables accurate and generalizable estimation of postoperative LOS while revealing clinically actionable perioperative domains. Such frameworks may facilitate more efficient perioperative planning, improved allocation of hospital resources, and personalized recovery strategies. Full article
(This article belongs to the Special Issue Wearable Technologies and Health Informatics: Advancements in Bioengineering)
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