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14 pages, 13932 KiB

Open AccessArticle

Dual-Mode Visual System for Brain–Computer Interfaces: Integrating SSVEP and P300 Responses

by Ekgari Kasawala and Surej Mouli

Sensors 2025, 25(6), 1802; https://doi.org/10.3390/s25061802 - 14 Mar 2025

Viewed by 749

Abstract

In brain–computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. These neurophysiological signals have exhibited robust efficacy and versatility in external device control, demonstrating enhanced [...] Read more.

In brain–computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. These neurophysiological signals have exhibited robust efficacy and versatility in external device control, demonstrating enhanced precision and scalability. However, conventional implementations predominantly utilise liquid crystal display (LCD)-based visual stimulation paradigms, which present limitations in practical deployment scenarios. This investigation presents the development and evaluation of a novel light-emitting diode (LED)-based dual stimulation apparatus designed to enhance SSVEP classification accuracy through the integration of both SSVEP and P300 paradigms. The system employs four distinct frequencies—7 Hz, 8 Hz, 9 Hz, and 10 Hz—corresponding to forward, backward, right, and left directional controls, respectively. Oscilloscopic verification confirmed the precision of these stimulation frequencies. Real-time feature extraction was accomplished through the concurrent analysis of maximum Fast Fourier Transform (FFT) amplitude and P300 peak detection to ascertain user intent. Directional control was determined by the frequency exhibiting maximal amplitude characteristics. The visual stimulation hardware demonstrated minimal frequency deviation, with error differentials ranging from

0.15

% to

0.20

% across all frequencies. The implemented signal processing algorithm successfully discriminated between all four stimulus frequencies whilst correlating them with their respective P300 event markers. Classification accuracy was evaluated based on correct task intention recognition. The proposed hybrid system achieved a mean classification accuracy of

86.25

%, coupled with an average ITR of

42.08

bits per minute (bpm). These performance metrics notably exceed the conventional 70% accuracy threshold typically employed in BCI system evaluation protocols. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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10 pages, 4282 KiB

Open AccessArticle

An Ultrasound Prototype for Remote Hand Movement Sensing: The Finger Tapping Case

by Stefano Franceschini, Maria Maddalena Autorino, Michele Ambrosanio, Vito Pascazio and Fabio Baselice

Sensors 2025, 25(1), 123; https://doi.org/10.3390/s25010123 - 28 Dec 2024

Viewed by 813

Abstract

In the context of neurodegenerative diseases, finger tapping is a gold-standard test used by clinicians to evaluate the severity of the condition. The finger tapping test involves repetitive tapping between the index finger and thumb. Subjects affected by neurodegenerative diseases, such as Parkinson’s [...] Read more.

In the context of neurodegenerative diseases, finger tapping is a gold-standard test used by clinicians to evaluate the severity of the condition. The finger tapping test involves repetitive tapping between the index finger and thumb. Subjects affected by neurodegenerative diseases, such as Parkinson’s disease, often exhibit symptoms like bradykinesia, rigidity, and tremor. As a result, when these individuals perform the finger tapping task, instability in both the tap rate and finger displacement can be observed. Currently, clinicians assess bradykinesia by visually observing the patient’s finger tapping movements and qualitatively rating their severity. In this work, we present a novel ultrasound contactless system that provides quantitative measurements of finger tapping, including tap rate and finger displacements. The system functions as an ultrasound sonar capable of measuring the Doppler spectrum of waves reflected by the hand. From this spectrum, various characteristics of the hand movement can be extracted through appropriate processing techniques. Specifically, by performing time–frequency analysis and applying specialized data processing, tapping rates and finger displacements can be estimated. The system has been tested in real-world scenarios involving volunteer finger tapping sessions, demonstrating its potential for accurately measuring both tap rates and displacements. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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14 pages, 4877 KiB

Open AccessArticle

Systematic Evaluation of IMU Sensors for Application in Smart Glove System for Remote Monitoring of Hand Differences

by Amy Harrison, Andrea Jester, Surej Mouli, Antonio Fratini and Ali Jabran

Sensors 2025, 25(1), 2; https://doi.org/10.3390/s25010002 - 24 Dec 2024

Viewed by 1463

Abstract

Human hands have over 20 degrees of freedom, enabled by a complex system of bones, muscles, and joints. Hand differences can significantly impair dexterity and independence in daily activities. Accurate assessment of hand function, particularly digit movement, is vital for effective intervention and [...] Read more.

Human hands have over 20 degrees of freedom, enabled by a complex system of bones, muscles, and joints. Hand differences can significantly impair dexterity and independence in daily activities. Accurate assessment of hand function, particularly digit movement, is vital for effective intervention and rehabilitation. However, current clinical methods rely on subjective observations and limited tests. Smart gloves with inertial measurement unit (IMU) sensors have emerged as tools for capturing digit movements, yet their sensor accuracy remains underexplored. This study developed and validated an IMU-based smart glove system for measuring finger joint movements in individuals with hand differences. The glove measured 3D digit rotations and was evaluated against an industrial robotic arm. Tests included rotations around three axes at 1°, 10°, and 90°, simulating extension/flexion, supination/pronation, and abduction/adduction. The IMU sensors demonstrated high accuracy and reliability, with minimal systematic bias and strong positive correlations (p > 0.95 across all tests). Agreement matrices revealed high agreement (<1°) in 24 trials, moderate (1–10°) in 12 trials, and low (>10°) in only 4 trials. The Root Mean Square Error (RMSE) ranged from 1.357 to 5.262 for the 90° tests, 0.094 to 0.538 for the 10° tests, and 0.129 to 0.36 for the 1° tests. Likewise, mean absolute error (MAE) ranged from 0.967 to 4.679 for the 90° tests, 0.073 to 0.386 for the 10° tests, and 0.102 to 0.309 for the 1° tests. The sensor provided precise measurements of digit angles across 0–90° in multiple directions, enabling reliable clinical assessment, remote monitoring, and improved diagnosis, treatment, and rehabilitation for individuals with hand differences. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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10 pages, 1509 KiB

Open AccessArticle

Emotion Recognition Using PPG Signals of Smartwatch on Purpose of Threat Detection

by Gyuwon Hwang, Sohee Yoo and Jaehyun Yoo

Sensors 2025, 25(1), 18; https://doi.org/10.3390/s25010018 - 24 Dec 2024

Cited by 1 | Viewed by 1156

Abstract

This paper proposes a machine learning approach to detect threats using short-term PPG (photoplethysmogram) signals from a commercial smartwatch. In supervised learning, having accurately annotated training data is essential. However, a key challenge in the threat detection problem is the uncertainty regarding how [...] Read more.

This paper proposes a machine learning approach to detect threats using short-term PPG (photoplethysmogram) signals from a commercial smartwatch. In supervised learning, having accurately annotated training data is essential. However, a key challenge in the threat detection problem is the uncertainty regarding how accurately data labeled as ‘threat’ reflect actual threat responses since participants may react differently to the same experiments. In this paper, Gaussian Mixture Models are learned to remove ambiguously labeled training, and those models are also used to remove ambiguous test data. For the realistic test scenario, PPG measurements are collected from participants playing a horror VR (Virtual Reality) game, and the proposed method validates the superiority of our proposed approach in comparison with other methods. Also, the proposed filtering with GMM improves prediction accuracy by 23% compared to the method that does not incorporate the filtering. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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15 pages, 12114 KiB

Open AccessArticle

A NIRS-Based Technique for Monitoring Brain Tissue Oxygenation in Stroke Patients

by Josefina Gutierrez-Martinez, Gabriel Vega-Martinez, Cinthya Lourdes Toledo-Peral, Jorge Airy Mercado-Gutierrez and Jimena Quinzaños-Fresnedo

Sensors 2024, 24(24), 8175; https://doi.org/10.3390/s24248175 - 21 Dec 2024

Cited by 2 | Viewed by 1690

Abstract

Stroke is a global health issue caused by reduced blood flow to the brain, which leads to severe motor disabilities. Measuring oxygen levels in the brain tissue is crucial for understanding the severity and evolution of stroke. While CT or fMRI scans are [...] Read more.

Stroke is a global health issue caused by reduced blood flow to the brain, which leads to severe motor disabilities. Measuring oxygen levels in the brain tissue is crucial for understanding the severity and evolution of stroke. While CT or fMRI scans are preferred for confirming a stroke due to their high sensitivity, Near-Infrared Spectroscopy (NIRS)-based systems could be an alternative for monitoring stroke evolution. This study explores the potential of fNIRS signals to assess brain tissue in chronic stroke patients along with rehabilitation therapy. To study the feasibility of this proposal, ten healthy subjects and three stroke patients participated. For signal acquisition, two NIRS sensors were placed on the forehead of the subjects, who were asked to remain in a resting state for 5 min, followed by a 30 s motor task for each hand, which consists of opening and closing the hand at a steady pace, with a 1 min rest period in between. Acomplete protocol for placing sensors and a signal processing algorithm are proposed. In healthy subjects, a measurable change in oxygen saturation was found, with statistically significant differences (females p = 0.016, males p = 0.005) between the resting-state and the hand movement conditions. This work showed the feasibility of the complete proposal, including the NIRS sensor, the placement, the tasks protocol, and signal processing, for monitoring the state of the brain tissue cerebral oxygenation in stroke patients undergoing rehabilitation therapy. Thus this is a non-invasive barin assessment test based on fNIRS with the potential to be implemented in non-controlled clinical environments. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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27 pages, 13812 KiB

Open AccessArticle

A Quantitative Method to Guide the Integration of Textile Inductive Electrodes in Automotive Applications for Respiratory Monitoring

by James Elber Duverger, Victor Bellemin, Patricia Forcier, Justine Decaens, Ghyslain Gagnon and Alireza Saidi

Sensors 2024, 24(23), 7483; https://doi.org/10.3390/s24237483 - 23 Nov 2024

Cited by 1 | Viewed by 1117

Abstract

Induction-based breathing sensors in automobiles enable unobtrusive respiratory rate monitoring as an indicator of a driver’s alertness and health. This paper introduces a quantitative method based on signal quality to guide the integration of textile inductive electrodes in automotive applications. A case study [...] Read more.

Induction-based breathing sensors in automobiles enable unobtrusive respiratory rate monitoring as an indicator of a driver’s alertness and health. This paper introduces a quantitative method based on signal quality to guide the integration of textile inductive electrodes in automotive applications. A case study with a simplified setup illustrated the ability of the method to successfully provide basic design rules about where and how to integrate the electrodes on seat belts and seat backs to gather good quality respiratory signals in an automobile. The best signals came from the subject’s waist, then from the chest, then from the upper back, and finally from the lower back. Furthermore, folding the electrodes before their integration on a seat back improves the signal quality for both the upper and lower back. This analysis provided guidelines with three design rules to increase the chance of acquiring good quality signals: (1) use a multi-electrode acquisition approach, (2) place the electrodes in locations that maximize breathing-induced body displacement, and (3) use a mechanical amplifying method such as folding the electrodes in locations with little potential for breathing-induced displacement. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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19 pages, 1794 KiB

Open AccessArticle

Estimation of the Number of Active Sweat Glands Through Discrete Sweat Sensing

by Jelte R. Haakma, Elisabetta Peri, Simona Turco, Eduard Pelssers, Jaap M. J. den Toonder and Massimo Mischi

Sensors 2024, 24(22), 7187; https://doi.org/10.3390/s24227187 - 9 Nov 2024

Viewed by 1187

Abstract

Sweat is a biomarker-rich fluid with potential for continuous patient monitoring via wearable devices. However, biomarker concentrations vary with the sweat rate per gland, posing a challenge for sweat sensing. To address this, we propose an algorithm to compute both the number of [...] Read more.

Sweat is a biomarker-rich fluid with potential for continuous patient monitoring via wearable devices. However, biomarker concentrations vary with the sweat rate per gland, posing a challenge for sweat sensing. To address this, we propose an algorithm to compute both the number of active sweat glands and their individual sweat rates. We developed models of sweat glands and a discrete sweat-sensing device to sense sweat volume. Our algorithm estimates the number of active glands by decomposing the signal into patterns generated by the individual sweat glands, allowing for the calculation of individual sweat rates. We assessed the algorithm’s accuracy using synthetic datasets for varying physiological parameters (sweat rate and number of active sweat glands) and device layouts. The results show that device layout significantly affects accuracy, with error rates below 0.2% for low and medium sweat rates (below 0.2 nL min⁻¹ per gland). However, the method is not suitable for high sweat rates. The suitable sweat rate range can be adapted to different needs through the choice of device. Based on our findings, we provide recommendations for optimal device layouts to improve accuracy in estimating active sweat glands. This is the first study to focus on estimating the sweat rate per gland, which essential for accurate biomarker concentration estimation and advancing sweat sensing towards clinical applications. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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20 pages, 22057 KiB

Open AccessEditor’s ChoiceArticle

Design and Evaluation of a Novel Venturi-Based Spirometer for Home Respiratory Monitoring

by Mariana Ferreira Nunes, Hugo Plácido da Silva, Liliana Raposo and Fátima Rodrigues

Sensors 2024, 24(17), 5622; https://doi.org/10.3390/s24175622 - 30 Aug 2024

Cited by 2 | Viewed by 2428

Abstract

The high cost and limited availability of home spirometers pose a significant barrier to effective respiratory disease management and monitoring. To address this challenge, this paper introduces a novel Venturi-based spirometer designed for home use, leveraging the Bernoulli principle. The device features a [...] Read more.

The high cost and limited availability of home spirometers pose a significant barrier to effective respiratory disease management and monitoring. To address this challenge, this paper introduces a novel Venturi-based spirometer designed for home use, leveraging the Bernoulli principle. The device features a 3D-printed Venturi tube that narrows to create a pressure differential, which is measured by a differential pressure sensor and converted into airflow rate. The airflow is then integrated over time to calculate parameters such as the Forced Vital Capacity (FVC) and Forced Expiratory Volume in one second (FEV₁). The system also includes a bacterial filter for hygienic use and a circuit board for data acquisition and streaming. Evaluation with eight healthy individuals demonstrated excellent test-retest reliability, with intraclass correlation coefficients (ICCs) of 0.955 for FVC and 0.853 for FEV₁. Furthermore, when compared to standard Pulmonary Function Test (PFT) equipment, the spirometer exhibited strong correlation, with Pearson correlation coefficients of 0.992 for FVC and 0.968 for FEV₁, and high reliability, with ICCs of 0.987 for FVC and 0.907 for FEV₁. These findings suggest that the Venturi-based spirometer could significantly enhance access to spirometry at home. However, further large-scale validation and reliability studies are necessary to confirm its efficacy and reliability for widespread use. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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21 pages, 9453 KiB

Open AccessArticle

A 3 MHz Low-Error Adaptive Howland Current Source for High-Frequency Bioimpedance Applications

by Ifeabunike I. Nwokoye and Iasonas F. Triantis

Sensors 2024, 24(13), 4357; https://doi.org/10.3390/s24134357 - 4 Jul 2024

Cited by 1 | Viewed by 2030

Abstract

Bioimpedance is a diagnostic sensing method used in medical applications, ranging from body composition assessment to detecting skin cancer. Commonly, discrete-component (and at times integrated) circuit variants of the Howland Current Source (HCS) topology are employed for injection of an AC current. Ideally, [...] Read more.

Bioimpedance is a diagnostic sensing method used in medical applications, ranging from body composition assessment to detecting skin cancer. Commonly, discrete-component (and at times integrated) circuit variants of the Howland Current Source (HCS) topology are employed for injection of an AC current. Ideally, its amplitude should remain within 1% of its nominal value across a frequency range, and that nominal value should be programmable. However, the method’s applicability and accuracy are hindered due to the current amplitude diminishing at frequencies above 100 kHz, with very few designs accomplishing 1 MHz, and only at a single nominal amplitude. This paper presents the design and implementation of an adaptive current source for bioimpedance applications employing automatic gain control (AGC). The “Adaptive Howland Current Source” (AHCS) was experimentally tested, and the results indicate that the design can achieve less than 1% amplitude error for both 1 mA and 100 µA currents for bandwidths up to 3 MHz. Simulations also indicate that the system can be designed to achieve up to 19% noise reduction relative to the most common HCS design. AHCS addresses the need for high bandwidth AC current sources in bioimpedance spectroscopy, offering automatic output current compensation without constant recalibration. The novel structure of AHCS proves crucial in applications requiring higher β-dispersion frequencies exceeding 1 MHz, where greater penetration depths and better cell status assessment can be achieved, e.g., in the detection of skin or breast cancer. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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20 pages, 4794 KiB

Open AccessArticle

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals

by Jessica Centracchio, Salvatore Parlato, Daniele Esposito and Emilio Andreozzi

Sensors 2024, 24(5), 1525; https://doi.org/10.3390/s24051525 - 27 Feb 2024

Cited by 7 | Viewed by 2058

Abstract

Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, [...] Read more.

Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, which is not a trivial task. This study proposes a method for an accurate recognition and localization of heart sounds in Forcecardiography (FCG) recordings. FCG is a novel technique able to measure subsonic vibrations and sounds via small force sensors placed onto a subject’s thorax, allowing continuous cardio-respiratory monitoring. In this study, a template-matching technique based on normalized cross-correlation was used to automatically recognize heart sounds in FCG signals recorded from six healthy subjects at rest. Distinct templates were manually selected from each FCG recording and used to separately localize S1 and S2 sounds, as well as S1–S2 pairs. A simultaneously recorded electrocardiography (ECG) trace was used for performance evaluation. The results show that the template matching approach proved capable of separately classifying S1 and S2 sounds in more than 96% of all heartbeats. Linear regression, correlation, and Bland–Altman analyses showed that inter-beat intervals were estimated with high accuracy. Indeed, the estimation error was confined within 10 ms, with negligible impact on heart rate estimation. Heart rate variability (HRV) indices were also computed and turned out to be almost comparable with those obtained from ECG. The preliminary yet encouraging results of this study suggest that the template matching approach based on normalized cross-correlation allows very accurate heart sounds localization and inter-beat intervals estimation. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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14 pages, 2376 KiB

Open AccessArticle

Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device

by Maria Gragnaniello, Alessandro Borghese, Vincenzo Romano Marrazzo, Luca Maresca, Giovanni Breglio, Andrea Irace and Michele Riccio

Sensors 2024, 24(3), 828; https://doi.org/10.3390/s24030828 - 26 Jan 2024

Cited by 7 | Viewed by 3219

Abstract

Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for [...] Read more.

Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for continuous monitoring using wearable devices. This paper proposes a single-microcontroller-based system designed for the automatic detection of MI based on the Edge Computing paradigm. Two solutions for MI detection are evaluated, based on Machine Learning (ML) and Deep Learning (DL) techniques. The developed algorithms are based on two different approaches currently available in the literature, and they are optimized for deployment on low-resource hardware. A feasibility assessment of their implementation on a single 32-bit microcontroller with an ARM Cortex-M4 core was examined, and a comparison in terms of accuracy, inference time, and memory usage was detailed. For ML techniques, significant data processing for feature extraction, coupled with a simpler Neural Network (NN) is involved. On the other hand, the second method, based on DL, employs a Spectrogram Analysis for feature extraction and a Convolutional Neural Network (CNN) with a longer inference time and higher memory utilization. Both methods employ the same low power hardware reaching an accuracy of 89.40% and 94.76%, respectively. The final prototype is an energy-efficient system capable of real-time detection of MI without the need to connect to remote servers or the cloud. All processing is performed at the edge, enabling NN inference on the same microcontroller. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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18 pages, 5074 KiB

Open AccessArticle

Sensorized T-Shirt with Intarsia-Knitted Conductive Textile Integrated Interconnections: Performance Assessment of Cardiac Measurements during Daily Living Activities

by Abdelakram Hafid, Emanuel Gunnarsson, Alberto Ramos, Kristian Rödby, Farhad Abtahi, Panagiotis D. Bamidis, Antonis Billis, Panagiotis Papachristou and Fernando Seoane

Sensors 2023, 23(22), 9208; https://doi.org/10.3390/s23229208 - 16 Nov 2023

Cited by 4 | Viewed by 2369

Abstract

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We [...] Read more.

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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40 pages, 1298 KiB

Open AccessSystematic Review

Systematic Review of Commercially Available Clinical CMUT-Based Systems for Use in Medical Ultrasound Imaging: Products, Applications, and Performance

by Ahmed Sewify, Maria Antico, Laith Alzubaidi, Haider A. Alwzwazy, Jacqueline Roots, Peter Pivonka and Davide Fontanarosa

Sensors 2025, 25(7), 2245; https://doi.org/10.3390/s25072245 - 2 Apr 2025

Viewed by 694

Abstract

An emerging alternative to conventional piezoelectric technologies, which continue to dominate the ultrasound medical imaging (US) market, is Capacitive Micromachined Ultrasonic Transducers (CMUTs). Ultrasound transducers based on this technology offer a wider frequency bandwidth, improved cost-effectiveness, miniaturized size and effective integration with electronics. [...] Read more.

An emerging alternative to conventional piezoelectric technologies, which continue to dominate the ultrasound medical imaging (US) market, is Capacitive Micromachined Ultrasonic Transducers (CMUTs). Ultrasound transducers based on this technology offer a wider frequency bandwidth, improved cost-effectiveness, miniaturized size and effective integration with electronics. These features have led to an increase in the commercialization of CMUTs in the last 10 years. We conducted a review to answer three main research questions: (1) What are the commercially available CMUT-based clinical sonographic devices in the medical imaging space? (2) What are the medical imaging applications of these devices? (3) What is the performance of the devices in these applications? We additionally reported on all the future work expressed by modern studies released in the past 2 years to predict the trend of development in future CMUT device developments and express gaps in current research. The search retrieved 19 commercially available sonographic CMUT products belonging to seven companies. Four of the products were clinically approved. Sonographic CMUT devices have established their niche in the medical US imaging market mainly through the Butterfly iQ and iQ+ for quick preliminary screening, emergency care in resource-limited settings, clinical training, teleguidance, and paramedical applications. There were no commercialized 3D CMUT probes. Full article

(This article belongs to the Special Issue Biomedical Sensors: New Technologies, Integration and Signal Processing)

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