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Advanced Sensor Technologies for Biomedical-Information Processing
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Advanced Sensor Technologies for Biomedical-Information Processing

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

Deadline for manuscript submissions: closed (30 December 2024) | Viewed by 25767

Special Issue Editors

Dr. Shuo-Tsung Chen

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Guest Editor
Department of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan
Interests: Internet of Things; biomedicine; artificial intelligence; digital image processing; digital signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chihyu Hsu

E-Mail Website
Guest Editor
School of Transportation, Fujian University of Technology, Fuzhou 350118, China
Interests: artificial intelligence; deep learning; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Huang-Nan Huang

E-Mail Website
Guest Editor
Department of Applied Mathematics, Tunghai University, Taichung 40704, Taiwan
Interests: biomedicine; artificial intelligence; digital signal processing
Dr. Chur-Jen Chen

E-Mail Website
Guest Editor
Department of Applied Mathematics, Tunghai University, Taichung 40704, Taiwan
Interests: biomedicine; artificial intelligence; digital signal processing

Special Issue Information

Dear Colleagues,

With the rapid development of sensors, the Internet of Things, and Artificial Intelligence, academic research and industrial development related to biomedicine are innovating significantly. This Special Issue covers a wide range of topics related to sensor technology in biomedicine, including bio-signal processing, bio-image processing, healthcare, telemedicine, medicine and nursing, etc. Paper submissions are now welcome.

Dr. Shuo-Tsung Chen
Prof. Dr. Chihyu Hsu
Dr. Huang-Nan Huang
Dr. Chur-Jen Chen
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 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. 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

  • sensors
  • Internet of Things
  • artificial Intelligence
  • biomedicine

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Published Papers (7 papers)

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Research

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19 pages, 1864 KiB  
Article
An FPGA-Based SiNW-FET Biosensing System for Real-Time Viral Detection: Hardware Amplification and 1D CNN for Adaptive Noise Reduction
by Ahmed Hadded, Mossaad Ben Ayed and Shaya A. Alshaya
Sensors 2025, 25(1), 236; https://doi.org/10.3390/s25010236 - 3 Jan 2025
Viewed by 905
Abstract
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon [...] Read more.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency. The integration of a 1D CNN model on FPGA hardware for adaptive, non-linear noise filtering sets this design apart from conventional filtering approaches by achieving high accuracy and low power consumption in a portable format. This integration of SiNW-FET with FPGA-based CNN noise reduction offers a unique approach, as prior noise reduction techniques for biosensors typically rely on linear filtering or digital smoothing, which lack adaptive capabilities for complex, non-linear noise patterns. By introducing the 1D CNN on FPGA, this architecture enables real-time, high-fidelity noise reduction, preserving critical signal characteristics without compromising processing speed. Notably, the findings presented in this work are based exclusively on comprehensive simulations using COMSOL and MATLAB, as no physical prototypes or biomarker detection experiments were conducted. The SiNW-FET biosensor, functionalized with antibodies specific to viral antigens, detects impedance shifts caused by antibody–antigen interactions, providing a highly sensitive platform for viral detection. A high-gain folded-cascade amplifier enhances the Signal-to-Noise Ratio (SNR) to approximately 70 dB, verified through COMSOL and MATLAB simulations. Additionally, a 1D CNN model is employed for adaptive noise reduction, filtering out non-linear noise patterns and achieving an approximate 75% noise reduction across a broad frequency range. The CNN model, implemented on an Altera DE2 FPGA, enables high-throughput, low-latency signal processing, making the system viable for real-time applications. Performance evaluations confirmed the proposed system’s capability to enhance the SNR significantly while maintaining a compact and energy-efficient design suitable for portable diagnostics. This integrated architecture thus provides a powerful solution for high-precision, real-time viral detection, and continuous health monitoring, advancing the role of biosensors in accessible point-of-care diagnostics. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Biomedical-Information Processing)
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17 pages, 2998 KiB  
Article
Machine Learning-Based Approach towards Identification of Pharmaceutical Suspensions Exploiting Speckle Pattern Images
by Valentina Bello, Luca Coghe, Alessia Gerbasi, Elena Figus, Arianna Dagliati and Sabina Merlo
Sensors 2024, 24(20), 6635; https://doi.org/10.3390/s24206635 - 15 Oct 2024
Viewed by 1234
Abstract
Parenteral artificial nutrition (PAN) is a lifesaving medical treatment for many patients worldwide. Administration of the wrong PAN drug can lead to severe consequences on patients’ health, including death in the worst cases. Thus, their correct identification, just before injection, is of crucial [...] Read more.
Parenteral artificial nutrition (PAN) is a lifesaving medical treatment for many patients worldwide. Administration of the wrong PAN drug can lead to severe consequences on patients’ health, including death in the worst cases. Thus, their correct identification, just before injection, is of crucial importance. Since most of these drugs appear as turbid liquids, they cannot be easily discriminated simply by means of basic optical analyses. To overcome this limitation, in this work, we demonstrate that the combination of speckle pattern (SP) imaging and artificial intelligence can provide precise classifications of commercial pharmaceutical suspensions for PAN. Towards this aim, we acquired SP images of each sample and extracted several statistical parameters from them. By training two machine learning algorithms (a Random Forest and a Multi-Layer Perceptron Network), we were able to identify the drugs with accurate performances. The novelty of this work lies in the smart combination of SP imaging and machine learning for realizing an optical sensing platform. For the first time, to our knowledge, this approach is exploited to identify PAN drugs. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Biomedical-Information Processing)
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16 pages, 13292 KiB  
Article
Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network
by Rafael Albuquerque Pinto, Hygo Sousa De Oliveira, Eduardo Souto, Rafael Giusti and Rodrigo Veras
Sensors 2024, 24(18), 6046; https://doi.org/10.3390/s24186046 - 19 Sep 2024
Cited by 1 | Viewed by 2531
Abstract
There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the [...] Read more.
There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Biomedical-Information Processing)
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14 pages, 4202 KiB  
Article
Ultrasound Image Temperature Monitoring Based on a Temporal-Informed Neural Network
by Yuxiang Han, Yongxing Du, Limin He, Xianwei Meng, Minchao Li and Fujun Cao
Sensors 2024, 24(15), 4934; https://doi.org/10.3390/s24154934 - 30 Jul 2024
Viewed by 1263
Abstract
Real-time and accurate temperature monitoring during microwave hyperthermia (MH) remains a critical challenge for ensuring treatment efficacy and patient safety. This study presents a novel approach to simulate real MH and precisely determine the temperature of the target region within biological tissues using [...] Read more.
Real-time and accurate temperature monitoring during microwave hyperthermia (MH) remains a critical challenge for ensuring treatment efficacy and patient safety. This study presents a novel approach to simulate real MH and precisely determine the temperature of the target region within biological tissues using a temporal-informed neural network. We conducted MH experiments on 30 sets of phantoms and 10 sets of ex vivo pork tissues. We proposed a novel perspective: the evolving tissue responses to continuous electromagnetic radiation stimulation are a joint evolution in temporal and spatial dimensions. Our model leverages TimesNet to extract periodic features and Cloblock to capture global information relevance in two-dimensional periodic vectors from ultrasound images. By assimilating more ultrasound temporal data, our model improves temperature-estimation accuracy. In the temperature range 25–65 °C, our neural network achieved temperature-estimation root mean squared errors of approximately 0.886 °C and 0.419 °C for fresh ex vivo pork tissue and phantoms, respectively. The proposed temporal-informed neural network has a modest parameter count, rendering it suitable for deployment on ultrasound mobile devices. Furthermore, it achieves temperature accuracy close to that prescribed by clinical standards, making it effective for non-destructive temperature monitoring during MH of biological tissues. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Biomedical-Information Processing)
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13 pages, 1791 KiB  
Article
Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals
by Shuo-Tsung Chen, Ren-Jie Ye, Tsung-Hsien Wu, Chun-Wen Cheng, Po-You Zhan, Kuan-Ming Chen and Wan-Yu Zhong
Sensors 2023, 23(22), 9199; https://doi.org/10.3390/s23229199 - 15 Nov 2023
Cited by 1 | Viewed by 1776
Abstract
The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an [...] Read more.
The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Biomedical-Information Processing)
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13 pages, 1760 KiB  
Article
Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments
by Wei-Hsun Wang and Wen-Shin Hsu
Sensors 2023, 23(13), 5913; https://doi.org/10.3390/s23135913 - 26 Jun 2023
Cited by 45 | Viewed by 16127
Abstract
With the rapid advancement of information and communication technology (ICT), big data, and artificial intelligence (AI), intelligent healthcare systems have emerged, including the integration of healthcare systems with capital, the introduction of healthcare systems into long-term care institutions, and the integration of measurement [...] Read more.
With the rapid advancement of information and communication technology (ICT), big data, and artificial intelligence (AI), intelligent healthcare systems have emerged, including the integration of healthcare systems with capital, the introduction of healthcare systems into long-term care institutions, and the integration of measurement data for care or exposure. These systems provide comprehensive communication and home exposure reports and enable the involvement of rehabilitation specialists and other experts. Silver technology enables the realization of health management in long-term care services, workplace care, and health applications, facilitating disease prevention and control, improving disease management, reducing home isolation, alleviating family burden in terms of nursing, and promoting health and disease control. Research and development efforts in forward-looking cross-domain precision health technology, system construction, testing, and integration are carried out. This integrated project consists of two main components. The Integrated Intelligent Long-Term Care Service Management System focuses on building a personalized care service system for the elderly, encompassing health, nutrition, diet, and health education aspects. The Wearable Internet of Things Care System primarily supports the development of portable physiological signal detection devices and electronic fences. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Biomedical-Information Processing)
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Review

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25 pages, 7477 KiB  
Review
Human-Centered Sensor Technologies for Soft Robotic Grippers: A Comprehensive Review
by Md. Tasnim Rana, Md. Shariful Islam and Azizur Rahman
Sensors 2025, 25(5), 1508; https://doi.org/10.3390/s25051508 - 28 Feb 2025
Viewed by 889
Abstract
The importance of bio-robotics has been increasing day by day. Researchers are trying to mimic nature in a more creative way so that the system can easily adapt to the complex nature and its environment. Hence, bio-robotic grippers play a role in the [...] Read more.
The importance of bio-robotics has been increasing day by day. Researchers are trying to mimic nature in a more creative way so that the system can easily adapt to the complex nature and its environment. Hence, bio-robotic grippers play a role in the physical connection between the environment and the bio-robotics system. While handling the physical world using a bio-robotic gripper, complexity occurs in the feedback system, where the sensor plays a vital role. Therefore, a human-centered gripper sensor can have a good impact on the bio-robotics field. But categorical classification and the selection process are not very systematic. This review paper follows the PRISMA methodology to summarize the previous works on bio-robotic gripper sensors and their selection process. This paper discusses challenges in soft robotic systems, the importance of sensing systems in facilitating critical control mechanisms, along with their selection considerations. Furthermore, a classification of soft actuation based on grippers has been introduced. Moreover, some unique characteristics of soft robotic sensors are explored, namely compliance, flexibility, multifunctionality, sensor nature, surface properties, and material requirements. In addition, a categorization of sensors for soft robotic grippers in terms of modalities has been established, ranging from the tactile and force sensor to the slippage sensor. Various tactile sensors, ranging from piezoelectric sensing to optical sensing, are explored as they are of the utmost importance in soft grippers to effectively address the increasing requirements for intelligence and automation. Finally, taking everything into consideration, a flow diagram has been suggested for selecting sensors specific to soft robotic applications. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Biomedical-Information Processing)
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