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Bioengineering
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Wearable Sensors and Measurement Systems for Human Physiology Monitoring
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Wearable Sensors and Measurement Systems for Human Physiology Monitoring

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 21815

Special Issue Editor

Dr. Manob Saikia

E-Mail Website
Guest Editor
Electrical & Computer Engineering Department, The University of Memphis, Memphis, TN 38152, USA
Interests: fNIRS; EEG; wearables; healthcare technologies; bioelectronics; medical device; internet of things; neural engineering; bioinstrumentation; multimodal sensing

Special Issue Information

Dear Colleagues,

Wearable sensor technologies continue to advance, and their use in healthcare is growing rapidly. With ongoing research, their capabilities continue to expand, making them smaller, smarter, more accurate, unobtrusive, comfortable, and inexpensive, as well as capable of continuously measuring and monitoring multiple human physiological parameters simultaneously. The combination of flexible wearable sensing technologies and an integrated measurement system with IoT and AI capabilities can enable unprecedented healthcare, safety, sports, wellness, and daily lifestyle-related applications. However, there are still technological challenges in adopting wearables for reliably making diagnostic decisions and managing chronic disease and preventive care. This Special Issue focuses on wearable sensors and measurement systems for human physiology monitoring, covering challenges and recent advancements. The topics of interest are as follows: bioelectronics and bioinstrumentation; wearable neuroimaging; flexible biosensors and bioelectronicsInternet of Things for healthcare; prototyping and fabrication of novel wearable sensors; health measurement hardware systems; signal processing for wearable sensor systems; sensing and measurement of physiological parameters; biomedical systems for injury prevention and rehabilitation; chronic disease management; and reviews of wearables, emerging applications, and trends.

Dr. Manob Saikia
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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 sensor
  • measurement system
  • bioinstrumentation
  • wearable neuroimaging
  • flexible biosensors
  • bioelectronics
  • Internet of Things in healthcare
  • prototyping
  • signal processing
  • physiological measurement
  • parameters
  • injury prevention
  • rehabilitation
  • wellness monitoring
  • chronic disease

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Published Papers (9 papers)

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Research

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14 pages, 2718 KiB  
Article
An Explainable Fusion of ECG and SpO2-Based Models for Real-Time Sleep Apnea Detection
by Tanmoy Paul, Omiya Hassan, Christina S. McCrae, Syed Kamrul Islam and Abu Saleh Mohammad Mosa
Bioengineering 2025, 12(4), 382; https://doi.org/10.3390/bioengineering12040382 - 3 Apr 2025
Viewed by 366
Abstract
Obstructive sleep apnea (OSA) is a common disorder characterized by disrupted breathing during sleep, leading to serious health consequences such as daytime fatigue, hypertension, metabolic issues, and cardiovascular disease. Polysomnography (PSG) is the standard diagnostic method but is costly and uncomfortable for patients, [...] Read more.
Obstructive sleep apnea (OSA) is a common disorder characterized by disrupted breathing during sleep, leading to serious health consequences such as daytime fatigue, hypertension, metabolic issues, and cardiovascular disease. Polysomnography (PSG) is the standard diagnostic method but is costly and uncomfortable for patients, which has led to interest in artificial intelligence (AI) for automated OSA detection. To develop an explainable AI model that utilizes electrocardiogram (ECG) and blood oxygen saturation (SpO2) data for real-time apnea detection, providing visual explanations to enhance interpretability and support clinical decisions. It emphasizes giving visual explanations to show how specific segments of the signal contribute to the AI’s conclusions. Furthermore, it explores the combination of individual models to improve detection accuracy. The fusion of individual models demonstrates an enhanced performance in detection accuracy. Visual explanations for AI decisions highlight the importance of certain signal features, making the model’s operations transparent to healthcare providers. The proposed AI model addresses the crucial need for transparent and interpretable AI in healthcare. By providing real-time, explainable OSA detection, this approach represents a significant advancement in the field, potentially improving patient care and aiding in the early identification and management of OSA. Full article
(This article belongs to the Special Issue Wearable Sensors and Measurement Systems for Human Physiology Monitoring)
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21 pages, 4073 KiB  
Article
Development of Self-Powered Energy-Harvesting Electronic Module and Signal-Processing Framework for Wearable Healthcare Applications
by Jegan Rajendran, Nimi Wilson Sukumari, P. Subha Hency Jose, Manikandan Rajendran and Manob Jyoti Saikia
Bioengineering 2024, 11(12), 1252; https://doi.org/10.3390/bioengineering11121252 - 11 Dec 2024
Viewed by 1903
Abstract
A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients’ health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and [...] Read more.
A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients’ health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel. The new architecture and techniques offered by the developed method include an analog front-end unit, a signal processing unit, and a battery management unit for the acquiring and processing of real-time ECG signals. The dynamic multi-level wavelet packet decomposition framework has been used and applied to an ECG signal to extract the desired features by removing overlapped and repeated samples from an ECG signal. Further, a random forest with deep decision tree (RFDDT) architecture has been designed for offline ECG signal classification, and experimental results provide the highest accuracy of 99.72%. One assesses the custom-developed sensor by comparing its data with those of conventional biosensors. The onboard energy-harvesting and battery management circuits are designed with a BQ25505 microprocessor with the support of silicon photodiodes and solar cells which detect the ambient light variations and provide a maximum of 4.2 V supply to enable the continuous operation of an entire module. The measurements conducted on each unit of the proposed method demonstrate that the proposed signal-processing method significantly reduces the overlapping samples from the raw ECG data and the timing requirement criteria for personalized and wearable health monitoring. Also, it improves temporal requirements for ECG data processing while achieving excellent classification performance at a low computing cost. Full article
(This article belongs to the Special Issue Wearable Sensors and Measurement Systems for Human Physiology Monitoring)
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16 pages, 524 KiB  
Article
Dyadic and Individual Variation in 24-Hour Heart Rates of Cancer Patients and Their Caregivers
by Rajnish Kumar, Junhan Fu, Bengie L. Ortiz, Xiao Cao, Kerby Shedden and Sung Won Choi
Bioengineering 2024, 11(1), 95; https://doi.org/10.3390/bioengineering11010095 - 18 Jan 2024
Cited by 3 | Viewed by 2002
Abstract
Background: Twenty-four-hour heart rate (HR) integrates multiple physiological and psychological systems related to health and well-being, and can be continuously monitored in high temporal resolution over several days with wearable HR monitors. Using HR data from two independent datasets of cancer patients and [...] Read more.
Background: Twenty-four-hour heart rate (HR) integrates multiple physiological and psychological systems related to health and well-being, and can be continuously monitored in high temporal resolution over several days with wearable HR monitors. Using HR data from two independent datasets of cancer patients and their caregivers, we aimed to identify dyadic and individual patterns of 24 h HR variation and assess their relationship to demographic, environmental, psychological, and clinical variables of interest. Methods: a novel regularized approach to high-dimensional canonical correlation analysis (CCA) was used to identify factors reflecting dyadic and individual variation in the 24 h (circadian) HR trajectories of 430 people in 215 dyads, then regression analysis was used to relate these patterns to explanatory variables. Results: Four distinct factors of dyadic covariation in circadian HR were found, contributing approximately 7% to overall circadian HR variation. These factors, along with non-dyadic factors reflecting individual variation exhibited diverse and statistically robust patterns of association with explanatory variables of interest. Conclusions: Both dyadic and individual anomalies are present in the 24 h HR patterns of cancer patients and their caregivers. These patterns are largely synchronous, and their presence robustly associates with multiple explanatory variables. One notable finding is that higher mood scores in cancer patients correspond to an earlier HR nadir in the morning and higher HR during the afternoon. Full article
(This article belongs to the Special Issue Wearable Sensors and Measurement Systems for Human Physiology Monitoring)
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24 pages, 15417 KiB  
Article
Evaluation of Morlet Wavelet Analysis for Artifact Detection in Low-Frequency Commercial Near-Infrared Spectroscopy Systems
by Tobias Bergmann, Logan Froese, Alwyn Gomez, Amanjyot Singh Sainbhi, Nuray Vakitbilir, Abrar Islam, Kevin Stein, Izzy Marquez, Fiorella Amenta, Kevin Park, Younis Ibrahim and Frederick A. Zeiler
Bioengineering 2024, 11(1), 33; https://doi.org/10.3390/bioengineering11010033 - 27 Dec 2023
Cited by 2 | Viewed by 2216
Abstract
Regional cerebral oxygen saturation (rSO2), a method of cerebral tissue oxygenation measurement, is recorded using non-invasive near-infrared Spectroscopy (NIRS) devices. A major limitation is that recorded signals often contain artifacts. Manually removing these artifacts is both resource and time consuming. The [...] Read more.
Regional cerebral oxygen saturation (rSO2), a method of cerebral tissue oxygenation measurement, is recorded using non-invasive near-infrared Spectroscopy (NIRS) devices. A major limitation is that recorded signals often contain artifacts. Manually removing these artifacts is both resource and time consuming. The objective was to evaluate the applicability of using wavelet analysis as an automated method for simple signal loss artifact clearance of rSO2 signals obtained from commercially available devices. A retrospective observational study using existing populations (healthy control (HC), elective spinal surgery patients (SP), and traumatic brain injury patients (TBI)) was conducted. Arterial blood pressure (ABP) and rSO2 data were collected in all patients. Wavelet analysis was determined to be successful in removing simple signal loss artifacts using wavelet coefficients and coherence to detect signal loss artifacts in rSO2 signals. The removal success rates in HC, SP, and TBI populations were 100%, 99.8%, and 99.7%, respectively (though it had limited precision in determining the exact point in time). Thus, wavelet analysis may prove to be useful in a layered approach NIRS signal artifact tool utilizing higher-frequency data; however, future work is needed. Full article
(This article belongs to the Special Issue Wearable Sensors and Measurement Systems for Human Physiology Monitoring)
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14 pages, 4497 KiB  
Article
Design and Implementation of Analog-Digital Hybrid Beamformers for Low-Complexity Ultrasound Systems: A Feasibility Study
by Heechul Yoon, Junseung Kim, Kunkyu Lee and Tai-Kyong Song
Bioengineering 2024, 11(1), 8; https://doi.org/10.3390/bioengineering11010008 - 21 Dec 2023
Cited by 1 | Viewed by 2249
Abstract
Low-complexity ultrasound systems are increasingly desired for both wearable, point-of-care ultrasound and high-end massive-channel ultrasound for 3-D matrix imaging. However, the imaging capabilities, including spatial resolution and contrast, could suffer as low complexity systems are pursued, which remains as an unresolved tradeoff. To [...] Read more.
Low-complexity ultrasound systems are increasingly desired for both wearable, point-of-care ultrasound and high-end massive-channel ultrasound for 3-D matrix imaging. However, the imaging capabilities, including spatial resolution and contrast, could suffer as low complexity systems are pursued, which remains as an unresolved tradeoff. To mitigate this limitation, this study revisits the general structures of analog and digital beamformers and introduces a hybrid approach, referred to as analog-digital hybrid beamforming, to implement efficient ultrasound systems. The suggested hybrid beamforming takes two stages sequentially, where the first analog stage partially beamforms M-channel RF signals to N sum-out data (i.e., M-to-N beamforming), and the second digital stage beamforms N partial sums to single final beamformed data (i.e., N-to-1 beamforming). Our approach was systematically designed and implemented with only four major integrated circuits, which was capable of driving full 64-channel transmission and reception. The developed system was demonstrated with a customized 64-channel 1-D phased array using a commercial tissue mimicking phantom. From the phantom imaging results, signal-to-noise ratio, contrast-to-noise ratio, and full beam width at half maximum values were quantitatively evaluated. The demonstrated results indicate that the analog-digital hybrid beamforming can be applied to any type of array for sophisticated 3-D imaging and tiny wearable ultrasound applications. Full article
(This article belongs to the Special Issue Wearable Sensors and Measurement Systems for Human Physiology Monitoring)
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33 pages, 21482 KiB  
Article
Wearable Prophylaxis Tool for AI-Driven Identification of Early Warning Patterns of Pressure Ulcers
by Lorenz Gruenerbel, Ferdinand Heinrich, Jonathan Böhlhoff-Martin, Lynn Röper, Hans-Günther Machens, Arthur Gruenerbel, Moritz Schillinger, Andreas Kist, Franz Wenninger, Martin Richter and Leonard Steinbacher
Bioengineering 2023, 10(10), 1125; https://doi.org/10.3390/bioengineering10101125 - 25 Sep 2023
Cited by 5 | Viewed by 3689
Abstract
As today’s society ages, age-related diseases become more frequent. One very common but yet preventable disease is the development of pressure ulcers (PUs). PUs can occur if tissue is exposed to a long-lasting pressure load, e.g., lying on tissue without turning. The cure [...] Read more.
As today’s society ages, age-related diseases become more frequent. One very common but yet preventable disease is the development of pressure ulcers (PUs). PUs can occur if tissue is exposed to a long-lasting pressure load, e.g., lying on tissue without turning. The cure of PUs requires intensive care, especially for the elderly or people with preexisting conditions whose tissue needs longer healing times. The consequences are heavy suffering for the patient and extreme costs for the health care system. To avoid these consequences, our objective is to develop a pressure ulcer prophylaxis device. For that, we built a new sensor system able to monitor the pressure load and tissue vital signs in immediate local proximity at patient’s predilection sites. In the clinical study, we found several indicators showing correlations between tissue perfusion and the risk of PU development, including strongly reduced SpO2 levels in body tissue prior to a diagnosed PU. Finally, we propose a prophylaxis system that allows for the prediction of PU developments in early stages before they become visible. This work is the first step in generating an effective system to warn patients or caregivers about developing PUs and taking appropriate preventative measures. Widespread application could reduce patient suffering and lead to substantial cost savings. Full article
(This article belongs to the Special Issue Wearable Sensors and Measurement Systems for Human Physiology Monitoring)
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19 pages, 2385 KiB  
Article
Temporal Statistical Relationship between Regional Cerebral Oxygen Saturation (rSO2) and Brain Tissue Oxygen Tension (PbtO2) in Moderate-to-Severe Traumatic Brain Injury: A Canadian High Resolution-TBI (CAHR-TBI) Cohort Study
by Alwyn Gomez, Donald Griesdale, Logan Froese, Eleen Yang, Eric P. Thelin, Rahul Raj, Marcel Aries, Clare Gallagher, Francis Bernard, Andreas H. Kramer and Frederick A. Zeiler
Bioengineering 2023, 10(10), 1124; https://doi.org/10.3390/bioengineering10101124 - 25 Sep 2023
Cited by 7 | Viewed by 1788
Abstract
Brain tissue oxygen tension (PbtO2) has emerged as a cerebral monitoring modality following traumatic brain injury (TBI). Near-infrared spectroscopy (NIRS)-based regional cerebral oxygen saturation (rSO2) can non-invasively examine cerebral oxygen content and has the potential for high spatial resolution. [...] Read more.
Brain tissue oxygen tension (PbtO2) has emerged as a cerebral monitoring modality following traumatic brain injury (TBI). Near-infrared spectroscopy (NIRS)-based regional cerebral oxygen saturation (rSO2) can non-invasively examine cerebral oxygen content and has the potential for high spatial resolution. Past studies examining the relationship between PbtO2 and NIRS-based parameters have had conflicting results with varying degrees of correlation. Understanding this relationship will help guide multimodal monitoring practices and impact patient care. The aim of this study is to examine the relationship between PbtO2 and rSO2 in a cohort of TBI patients by leveraging contemporary statistical methods. A multi-institutional retrospective cohort study of prospectively collected data was performed. Moderate-to-severe adult TBI patients were included with concurrent rSO2 and PbtO2 monitoring during their stay in the intensive care unit (ICU). The high-resolution data were analyzed utilizing time series techniques to examine signal stationarity as well as the cross-correlation relationship between the change in PbtO2 and the change in rSO2 signals. Finally, modeling of the change in PbtO2 by the change in rSO2 was attempted utilizing linear methods that account for the autocorrelative nature of the data signals. A total of 20 subjects were included in the study. Cross-correlative analysis found that changes in PbtO2 were most significantly correlated with changes in rSO2 one minute earlier. Through mixed-effects and time series modeling of parameters, changes in rSO2 were found to often have a statistically significant linear relationship with changes in PbtO2 that occurred a minute later. However, changes in rSO2 were inadequate to predict changes in PbtO2. In this study, changes in PbtO2 were found to correlate most with changes in rSO2 approximately one minute earlier. While changes in rSO2 were found to contain information about future changes in PbtO2, they were not found to adequately model them. This strengthens the body of literature indicating that NIRS-based rSO2 is not an adequate substitute for PbtO2 in the management of TBI. Full article
(This article belongs to the Special Issue Wearable Sensors and Measurement Systems for Human Physiology Monitoring)
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Review

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23 pages, 2755 KiB  
Review
A Sensor-Based Classification for Neuromotor Robot-Assisted Rehabilitation
by Calin Vaida, Gabriela Rus and Doina Pisla
Bioengineering 2025, 12(3), 287; https://doi.org/10.3390/bioengineering12030287 - 13 Mar 2025
Viewed by 777
Abstract
Neurological diseases leading to motor deficits constitute significant challenges to healthcare systems. Despite technological advancements in data acquisition, sensor development, data processing, and virtual reality (VR), a suitable framework for patient-centered neuromotor robot-assisted rehabilitation using collective sensor information does not exist. An extensive [...] Read more.
Neurological diseases leading to motor deficits constitute significant challenges to healthcare systems. Despite technological advancements in data acquisition, sensor development, data processing, and virtual reality (VR), a suitable framework for patient-centered neuromotor robot-assisted rehabilitation using collective sensor information does not exist. An extensive literature review was achieved based on 124 scientific publications regarding different types of sensors and the usage of the bio-signals they measure for neuromotor robot-assisted rehabilitation. A comprehensive classification of sensors was proposed, distinguishing between specific and non-specific parameters. The classification criteria address essential factors such as the type of sensors, the data they measure, their usability, ergonomics, and their overall impact on personalized treatment. In addition, a framework designed to collect and utilize relevant data for the optimal rehabilitation process efficiently is proposed. The proposed classifications aim to identify a set of key variables that can be used as a building block for a dynamic framework tailored for personalized treatments, thereby enhancing the effectiveness of patient-centered procedures in rehabilitation. Full article
(This article belongs to the Special Issue Wearable Sensors and Measurement Systems for Human Physiology Monitoring)
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20 pages, 3488 KiB  
Review
Digital Twins for Healthcare Using Wearables
by Zachary Johnson and Manob Jyoti Saikia
Bioengineering 2024, 11(6), 606; https://doi.org/10.3390/bioengineering11060606 - 13 Jun 2024
Cited by 4 | Viewed by 4728
Abstract
Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a [...] Read more.
Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors. Full article
(This article belongs to the Special Issue Wearable Sensors and Measurement Systems for Human Physiology Monitoring)
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