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Advancements in Arterial Pulse Measurement Technologies: Sensors, Signal Processing, and Clinical Applications

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

Deadline for manuscript submissions: 15 July 2026 | Viewed by 2563

Special Issue Editors

Prof. Dr. Zhili Hao

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA 23529, USA
Interests: development of micro-sensors; arterial pulse measurement using microsensors; signal-processing techniques for arterial pulse signals; analytical models of the technical issues associated with arterial pulse measurement
Dr. Jae H. Lee

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA 23529, USA
Interests: digital twins; computational fluid dynamics; fluid-structure interaction; computational physiology; medical devices; drug delivery
Dr. Krishnanand N. Kaipa

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA 23529, USA
Interests: bio-inspired robotics; swarm intelligence; medical robotics; human–robot collaboration; autonomous systems

Special Issue Information

Dear Colleagues,

This Special Issue will explore recent advancements in arterial pulse measurement technologies, with a particular focus on innovations in sensor design, signal processing, and their clinical applications. Arterial pulse measurements play a pivotal role in assessing cardiovascular health, and the development of advanced, non-invasive sensors has revolutionized this field. Innovations such as wearable sensors, including photoplethysmography (PPG) devices, tactile sensors, and accelerometers, have significantly enhanced the ability to monitor pulse waveforms in real-time with greater accuracy and portability.

Equally crucial are advancements in signal processing techniques. Machine learning algorithms, wavelet analysis, and time-frequency methods are enabling the extraction of more precise features from pulse signals, allowing for the early detection of cardiovascular diseases such as hypertension, atherosclerosis, and arrhythmias. These signal processing techniques also contribute to the development of predictive models for cardiovascular risk, offering valuable insights for personalized healthcare.

Moreover, the integration of these technologies into consumer-friendly devices opens new possibilities for continuous health monitoring, making it easier for individuals to track and manage their cardiovascular health. This Special Issue will bring together cutting-edge research that highlights both the technological advancements and the promising clinical applications, underscoring the transformative impact of arterial pulse measurement on early disease detection and health management.

Prof. Dr. Zhili Hao
Dr. Jae H. Lee
Dr. Krishnanand N. Kaipa
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • arterial pulse signals
  • PPG signals
  • arterial pulse waveform (APW)
  • heart rate variability (HRV)
  • respiration rate (RR)

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Published Papers (3 papers)

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Research

18 pages, 2068 KB  
Article
Signal Quality of Reflective-Mode Photoplethysmograms Across Anatomical Sites
by Federica Ricci, Cecilia Vivarelli, Eugenio Mattei and Giovanni Calcagnini
Sensors 2026, 26(10), 2986; https://doi.org/10.3390/s26102986 - 9 May 2026
Viewed by 506
Abstract
Reflective-mode photoplethysmography (PPG) potentially enables non-invasive physiological monitoring of heart rate and Peripheral Oxygen Saturation (SpO2) from virtually any anatomical body site, but its performances are strongly affected by several parameters such as local perfusion, skin temperature, and microvascular bed and [...] Read more.
Reflective-mode photoplethysmography (PPG) potentially enables non-invasive physiological monitoring of heart rate and Peripheral Oxygen Saturation (SpO2) from virtually any anatomical body site, but its performances are strongly affected by several parameters such as local perfusion, skin temperature, and microvascular bed and tissue optical properties. This study systematically evaluates the quality of reflective-mode PPG signals acquired at the finger, wrist, ear, nose, temple, upper lip, and lower lip, using two commercial PPG sensors. PPG signal quality was quantified via Skewness, Kurtosis, Perfusion Index, and Shannon entropy. Heart rate (HR) and pulse transit time (PTT) were also computed. Skewness and Perfusion Index were the most informative quality indices, revealing the finger as the site with the best signal quality and the wrist as the most challenging location. Several facial regions—including the lips, nose, and temple—showed signal quality comparable to the finger. HR estimation was most accurate using the GREEN wavelength, with the lower lip achieving the lowest error, followed by the upper lip and finger. PTT values reflected physiological differences in pulse propagation, being longest at the finger and wrist and shortest at the lips. These findings highlight the potential of non-conventional anatomical sites as alternatives to the finger and wrist for reflective-mode PPG acquisition. Full article
(This article belongs to the Special Issue Advancements in Arterial Pulse Measurement Technologies: Sensors, Signal Processing, and Clinical Applications)
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26 pages, 3427 KB  
Article
Relationship of Photoplethysmography Morphological Variability Indices and Ankle-Brachial Index in Peripheral Artery Disease Patients
by David Hernández-Obín, Adriana Torres-Machorro and Claudia Lerma
Sensors 2026, 26(6), 1864; https://doi.org/10.3390/s26061864 - 16 Mar 2026
Viewed by 895
Abstract
The ankle-brachial index (ABI) is the most non-invasive technique used for diagnosing and assessing peripheral artery disease (PAD), although it is operator-dependent and limited by arterial calcification. Since photoplethysmography (PPG) is a non-invasive, low-cost, and easy-to-use technique that is not limited by arterial [...] Read more.
The ankle-brachial index (ABI) is the most non-invasive technique used for diagnosing and assessing peripheral artery disease (PAD), although it is operator-dependent and limited by arterial calcification. Since photoplethysmography (PPG) is a non-invasive, low-cost, and easy-to-use technique that is not limited by arterial compressibility, PPG morphological parameters have been studied for PAD diagnosis, mostly based on mean values. In this work, the relationship between variability indices of PPG morphological parameters and ABI was studied in 52 legs of 32 PAD patients. The morphological PPG parameters, including amplitude, pulse transit time (PTT), and maximum systolic slope, were measured. The mean, standard deviation, and frequency spectral energy for very low, low, and high frequencies were computed as PPG morphological variability indices. The variability indices of PPG morphological parameters have a significant correlation with ABI, indicating that they differ not only between legs with altered and normal ABI but also that they may relate to PAD progression. Fourteen of the 15 variability indices showed significant diagnostic value, with the standard deviation of PTT being the most effective (sensitivity of 96% and specificity of 71%). The differences between normal and non-compressible legs were not significant. The comparison between contralateral legs was also not significant. This suggests that variability indices may provide valuable insights into changes in physiological regulatory mechanisms as PAD progresses, which could aid in the diagnosis, assessment, and prognosis of PAD in future research. Full article
(This article belongs to the Special Issue Advancements in Arterial Pulse Measurement Technologies: Sensors, Signal Processing, and Clinical Applications)
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26 pages, 7144 KB  
Article
Atrial Fibrillation Detection from At-Rest PPG Signals Using an SDOF-TF Method
by Mamun Hasan and Zhili Hao
Sensors 2026, 26(2), 416; https://doi.org/10.3390/s26020416 - 8 Jan 2026
Viewed by 855
Abstract
At-rest PPG signals have been explored for detecting atrial fibrillation (AF), yet current signal-processing techniques do not achieve perfect accuracy even under low-motion artifact (MA) conditions. This study evaluates the effectiveness of a single-degree-of-freedom time–frequency (SDOF-TF) method in analyzing at-rest PPG signals for [...] Read more.
At-rest PPG signals have been explored for detecting atrial fibrillation (AF), yet current signal-processing techniques do not achieve perfect accuracy even under low-motion artifact (MA) conditions. This study evaluates the effectiveness of a single-degree-of-freedom time–frequency (SDOF-TF) method in analyzing at-rest PPG signals for AF detection. The method leverages the influence of MA on the instant parameters of each harmonic, which is identified using an SDOF model in which the tissue–contact–sensor (TCS) stack is treated as an SDOF system. In this model, MA induces baseline drift and time-varying system parameters. The SDOF-TF method enables the quantification and removal of MA and noise, allowing for the accurate extraction of the arterial pulse waveform, heart rate (HR), heart rate variability (HRV), respiration rate (RR), and respiration modulation (RM). Using data from the MIMIC PERform AF dataset, the method achieved 100% accuracy in distinguishing AF from non-AF cases based on three features: (1) RM, (2) HRV derived from instant frequency and instant initial phase, and (3) standard deviation of HR across harmonics. Compared with non-AF, the RM for each harmonic was increased by AF. RM exhibited an increasing trend with harmonic order in non-AF subjects, whereas this trend was diminished in AF subjects. Full article
(This article belongs to the Special Issue Advancements in Arterial Pulse Measurement Technologies: Sensors, Signal Processing, and Clinical Applications)
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