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Biomedical Sensors and Systems 2017

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (31 October 2017) | Viewed by 217555

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Special Issue Editor

Prof. Dr. Wan-Young Chung

E-Mail Website
Guest Editor

Department of Artificial Intelligence Convergence, Pukyong National University (PKNU), Busan 48531, Korea
Interests: wearable devices; wearable healthcare; AI-powered sensor-signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, a wide variety of biomedical sensors such as fluid flow sensors, ultrasound sensors, chemical analysis sensors, biomaterial-based sensors, wearable biomedical sensors and wireless biomedical sensors has been used in modern medicine. Modern biomedical sensors developed with advanced microfabrication and signal processing techniques are becoming inexpensive, accurate, reliable and with excellent fit. The miniaturization of classical measurement techniques has led to the realization of complex analytical systems, including such sensors as the BioChemLab-on-a-chip. Also, with recent advances in wireless communication technology, it becomes possible to build miniature and reliable wireless biomedical sensors for e-healthcare or u-healthcare. The Special Issue will publish those full research, review and highly-rated manuscripts addressing the development of biomedical sensors and systems.

Prof. Dr. Wan-Young Chung
Guest Editor

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Keywords

biomedical sensors
biomedical signal processing
wearable biomedical sensors
medical equipment
wireless sensor network
driver drowsiness detection
e-healthcare
BioChemLab-on-a-chip
wireless biomedical sensors

Published Papers (26 papers)

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Research

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22 pages, 8667 KiB

Open AccessArticle

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment

by Francesco Rundo, Sabrina Conoci, Alessandro Ortis and Sebastiano Battiato

Sensors 2018, 18(2), 405; https://doi.org/10.3390/s18020405 - 30 Jan 2018

Cited by 115 | Viewed by 12114

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used [...] Read more.

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG “combo” pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting “clean” PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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13 pages, 3847 KiB

Open AccessArticle

Time Domain Near Infrared Spectroscopy Device for Monitoring Muscle Oxidative Metabolism: Custom Probe and In Vivo Applications

by Rebecca Re, Ileana Pirovano, Davide Contini, Lorenzo Spinelli and Alessandro Torricelli

Sensors 2018, 18(1), 264; https://doi.org/10.3390/s18010264 - 17 Jan 2018

Cited by 35 | Viewed by 6110

Measurement of muscle oxidative metabolism is of interest for monitoring the training status in athletes and the rehabilitation process in patients. Time domain near infrared spectroscopy (TD NIRS) is an optical technique that allows the non-invasive measurement of the hemodynamic parameters in muscular [...] Read more.

Measurement of muscle oxidative metabolism is of interest for monitoring the training status in athletes and the rehabilitation process in patients. Time domain near infrared spectroscopy (TD NIRS) is an optical technique that allows the non-invasive measurement of the hemodynamic parameters in muscular tissue: concentrations of oxy- and deoxy-hemoglobin, total hemoglobin content, and tissue oxygen saturation. In this paper, we present a novel TD NIRS medical device for muscle oxidative metabolism. A custom-printed 3D probe, able to host optical elements for signal acquisition from muscle, was develop for TD NIRS in vivo measurements. The system was widely characterized on solid phantoms and during in vivo protocols on healthy subjects. In particular, we tested the in vivo repeatability of the measurements to quantify the error that we can have by repositioning the probe. Furthermore, we considered a series of acquisitions on different muscles that were not yet previously performed with this custom probe: a venous-arterial cuff occlusion of the arm muscle, a cycling exercise, and an isometric contraction of the vastus lateralis. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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13 pages, 3341 KiB

Open AccessArticle

Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning

by Jongryun Roh, Hyeong-jun Park, Kwang Jin Lee, Joonho Hyeong, Sayup Kim and Boreom Lee

Sensors 2018, 18(1), 208; https://doi.org/10.3390/s18010208 - 12 Jan 2018

Cited by 78 | Viewed by 15879

Sitting posture monitoring systems (SPMSs) help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, [...] Read more.

Sitting posture monitoring systems (SPMSs) help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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15 pages, 8111 KiB

Open AccessArticle

Highly Integrated MEMS-ASIC Sensing System for Intracorporeal Physiological Condition Monitoring

by Ning Xue, Chao Wang, Cunxiu Liu and Jianhai Sun

Sensors 2018, 18(1), 107; https://doi.org/10.3390/s18010107 - 02 Jan 2018

Cited by 4 | Viewed by 6157

In this paper, a highly monolithic-integrated multi-modality sensor is proposed for intracorporeal monitoring. The single-chip sensor consists of a solid-state based temperature sensor, a capacitive based pressure sensor, and an electrochemical oxygen sensor with their respective interface application-specific integrated circuits (ASICs). The solid-state-based [...] Read more.

In this paper, a highly monolithic-integrated multi-modality sensor is proposed for intracorporeal monitoring. The single-chip sensor consists of a solid-state based temperature sensor, a capacitive based pressure sensor, and an electrochemical oxygen sensor with their respective interface application-specific integrated circuits (ASICs). The solid-state-based temperature sensor and the interface ASICs were first designed and fabricated based on a 0.18-μm 1.8-V CMOS (complementary metal-oxide-semiconductor) process. The oxygen sensor and pressure sensor were fabricated by the standard CMOS process and subsequent CMOS-compatible MEMS (micro-electromechanical systems) post-processing. The multi-sensor single chip was completely sealed by the nafion, parylene, and PDMS (polydimethylsiloxane) layers for biocompatibility study. The size of the compact sensor chip is only 3.65 mm × 1.65 mm × 0.72 mm. The functionality, stability, and sensitivity of the multi-functional sensor was tested ex vivo. Cytotoxicity assessment was performed to verify that the bio-compatibility of the device is conforming to the ISO 10993-5:2009 standards. The measured sensitivities of the sensors for the temperature, pressure, and oxygen concentration are 10.2 mV/°C, 5.58 mV/kPa, and 20 mV·L/mg, respectively. The measurement results show that the proposed multi-sensor single chip is suitable to sense the temperature, pressure, and oxygen concentration of human tissues for intracorporeal physiological condition monitoring. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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1598 KiB

Open AccessArticle

Wireless sEMG System with a Microneedle-Based High-Density Electrode Array on a Flexible Substrate

by Minjae Kim, Gangyong Gu, Kyoung Je Cha, Dong Sung Kim and Wan Kyun Chung

Sensors 2018, 18(1), 92; https://doi.org/10.3390/s18010092 - 30 Dec 2017

Cited by 13 | Viewed by 7920

Surface electromyography (sEMG) signals reflect muscle contraction and hence, can provide information regarding a user’s movement intention. High-density sEMG systems have been proposed to measure muscle activity in small areas and to estimate complex motion using spatial patterns. However, conventional systems based on [...] Read more.

Surface electromyography (sEMG) signals reflect muscle contraction and hence, can provide information regarding a user’s movement intention. High-density sEMG systems have been proposed to measure muscle activity in small areas and to estimate complex motion using spatial patterns. However, conventional systems based on wet electrodes have several limitations. For example, the electrolyte enclosed in wet electrodes restricts spatial resolution, and these conventional bulky systems limit natural movements. In this paper, a microneedle-based high-density electrode array on a circuit integrated flexible substrate for sEMG is proposed. Microneedles allow for high spatial resolution without requiring conductive substances, and flexible substrates guarantee stable skin–electrode contact. Moreover, a compact signal processing system is integrated with the electrode array. Therefore, sEMG measurements are comfortable to the user and do not interfere with the movement. The system performance was demonstrated by testing its operation and estimating motion using a Gaussian mixture model-based, simplified 2D spatial pattern. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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3431 KiB

Open AccessArticle

Extraction and Analysis of Respiratory Motion Using Wearable Inertial Sensor System during Trunk Motion

by Apoorva Gaidhani, Kee S. Moon, Yusuf Ozturk, Sung Q. Lee and Woosub Youm

Sensors 2017, 17(12), 2932; https://doi.org/10.3390/s17122932 - 17 Dec 2017

Cited by 33 | Viewed by 5121

Respiratory activity is an essential vital sign of life that can indicate changes in typical breathing patterns and irregular body functions such as asthma and panic attacks. Many times, there is a need to monitor breathing activity while performing day-to-day functions such as [...] Read more.

Respiratory activity is an essential vital sign of life that can indicate changes in typical breathing patterns and irregular body functions such as asthma and panic attacks. Many times, there is a need to monitor breathing activity while performing day-to-day functions such as standing, bending, trunk stretching or during yoga exercises. A single IMU (inertial measurement unit) can be used in measuring respiratory motion; however, breathing motion data may be influenced by a body trunk movement that occurs while recording respiratory activity. This research employs a pair of wireless, wearable IMU sensors custom-made by the Department of Electrical Engineering at San Diego State University. After appropriate sensor placement for data collection, this research applies principles of robotics, using the Denavit-Hartenberg convention, to extract relative angular motion between the two sensors. One of the obtained relative joint angles in the “Sagittal” plane predominantly yields respiratory activity. An improvised version of the proposed method and wearable, wireless sensors can be suitable to extract respiratory information while performing sports or exercises, as they do not restrict body motion or the choice of location to gather data. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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6581 KiB

Open AccessArticle

Mathematical Model for Localised and Surface Heat Flux of the Human Body Obtained from Measurements Performed with a Calorimetry Minisensor

by Fabiola Socorro, Pedro Jesús Rodríguez de Rivera, Miriam Rodríguez de Rivera and Manuel Rodríguez de Rivera

Sensors 2017, 17(12), 2749; https://doi.org/10.3390/s17122749 - 28 Nov 2017

Cited by 8 | Viewed by 5109

The accuracy of the direct and local measurements of the heat power dissipated by the surface of the human body, using a calorimetry minisensor, is directly related to the calibration rigor of the sensor and the correct interpretation of the experimental results. For [...] Read more.

The accuracy of the direct and local measurements of the heat power dissipated by the surface of the human body, using a calorimetry minisensor, is directly related to the calibration rigor of the sensor and the correct interpretation of the experimental results. For this, it is necessary to know the characteristics of the body’s local heat dissipation. When the sensor is placed on the surface of the human body, the body reacts until a steady state is reached. We propose a mathematical model that represents the rate of heat flow at a given location on the surface of a human body by the sum of a series of exponentials: W(t) = A₀ + ∑A_iexp(−t/τ_i). In this way, transient and steady states of heat dissipation can be interpreted. This hypothesis has been tested by simulating the operation of the sensor. At the steady state, the power detected in the measurement area (4 cm²) varies depending on the sensor’s thermostat temperature, as well as the physical state of the subject. For instance, for a thermostat temperature of 24 °C, this power can vary between 100–250 mW in a healthy adult. In the transient state, two exponentials are sufficient to represent this dissipation, with 3 and 70 s being the mean values of its time constants. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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5885 KiB

Open AccessArticle

Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing

by Denis Delisle-Rodriguez, Ana Cecilia Villa-Parra, Teodiano Bastos-Filho, Alberto López-Delis, Anselmo Frizera-Neto, Sridhar Krishnan and Eduardo Rocon

Sensors 2017, 17(12), 2725; https://doi.org/10.3390/s17122725 - 25 Nov 2017

Cited by 22 | Viewed by 7660

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) [...] Read more.

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly

(p < 0.01)

improved for most of the subjects

(A C C \geq 74.79 %)

, when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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6341 KiB

Open AccessArticle

Low Computational-Cost Footprint Deformities Diagnosis Sensor through Angles, Dimensions Analysis and Image Processing Techniques

by J. Rodolfo Maestre-Rendon, Tomas A. Rivera-Roman, Juan M. Sierra-Hernandez, Ivan Cruz-Aceves, Luis M. Contreras-Medina, Carlos Duarte-Galvan and Arturo A. Fernandez-Jaramillo

Sensors 2017, 17(11), 2700; https://doi.org/10.3390/s17112700 - 22 Nov 2017

Cited by 11 | Viewed by 6975

Manual measurements of foot anthropometry can lead to errors since this task involves the experience of the specialist who performs them, resulting in different subjective measures from the same footprint. Moreover, some of the diagnoses that are given to classify a footprint deformity [...] Read more.

Manual measurements of foot anthropometry can lead to errors since this task involves the experience of the specialist who performs them, resulting in different subjective measures from the same footprint. Moreover, some of the diagnoses that are given to classify a footprint deformity are based on a qualitative interpretation by the physician; there is no quantitative interpretation of the footprint. The importance of providing a correct and accurate diagnosis lies in the need to ensure that an appropriate treatment is provided for the improvement of the patient without risking his or her health. Therefore, this article presents a smart sensor that integrates the capture of the footprint, a low computational-cost analysis of the image and the interpretation of the results through a quantitative evaluation. The smart sensor implemented required the use of a camera (Logitech C920) connected to a Raspberry Pi 3, where a graphical interface was made for the capture and processing of the image, and it was adapted to a podoscope conventionally used by specialists such as orthopedist, physiotherapists and podiatrists. The footprint diagnosis smart sensor (FPDSS) has proven to be robust to different types of deformity, precise, sensitive and correlated in 0.99 with the measurements from the digitalized image of the ink mat. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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2101 KiB

Open AccessArticle

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

by Aiming Liu, Kun Chen, Quan Liu, Qingsong Ai, Yi Xie and Anqi Chen

Sensors 2017, 17(11), 2576; https://doi.org/10.3390/s17112576 - 08 Nov 2017

Cited by 59 | Viewed by 5688

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only [...] Read more.

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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2975 KiB

Open AccessArticle

Arrhythmia Evaluation in Wearable ECG Devices

by Muammar Sadrawi, Chien-Hung Lin, Yin-Tsong Lin, Yita Hsieh, Chia-Chun Kuo, Jen Chien Chien, Koichi Haraikawa, Maysam F. Abbod and Jiann-Shing Shieh

Sensors 2017, 17(11), 2445; https://doi.org/10.3390/s17112445 - 25 Oct 2017

Cited by 40 | Viewed by 10140

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the [...] Read more.

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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1521 KiB

Open AccessArticle

Spatiotemporal Pixelization to Increase the Recognition Score of Characters for Retinal Prostheses

by Hyun Seok Kim and Kwang Suk Park

Sensors 2017, 17(10), 2439; https://doi.org/10.3390/s17102439 - 24 Oct 2017

Cited by 2 | Viewed by 3666

Most of the retinal prostheses use a head-fixed camera and a video processing unit. Some studies proposed various image processing methods to improve visual perception for patients. However, previous studies only focused on using spatial information. The present study proposes a spatiotemporal pixelization [...] Read more.

Most of the retinal prostheses use a head-fixed camera and a video processing unit. Some studies proposed various image processing methods to improve visual perception for patients. However, previous studies only focused on using spatial information. The present study proposes a spatiotemporal pixelization method mimicking fixational eye movements to generate stimulation images for artificial retina arrays by combining spatial and temporal information. Input images were sampled with a resolution that was four times higher than the number of pixel arrays. We subsampled this image and generated four different phosphene images. We then evaluated the recognition scores of characters by sequentially presenting phosphene images with varying pixel array sizes (6 × 6, 8 × 8 and 10 × 10) and stimulus frame rates (10 Hz, 15 Hz, 20 Hz, 30 Hz, and 60 Hz). The proposed method showed the highest recognition score at a stimulus frame rate of approximately 20 Hz. The method also significantly improved the recognition score for complex characters. This method provides a new way to increase practical resolution over restricted spatial resolution by merging the higher resolution image into high-frame time slots. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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1795 KiB

Open AccessArticle

Bias-Voltage Stabilizer for HVHF Amplifiers in VHF Pulse-Echo Measurement Systems

by Hojong Choi, Chulwoo Park, Jungsuk Kim and Hayong Jung

Sensors 2017, 17(10), 2425; https://doi.org/10.3390/s17102425 - 23 Oct 2017

Cited by 22 | Viewed by 6667

The impact of high-voltage–high-frequency (HVHF) amplifiers on echo-signal quality is greater with very-high-frequency (VHF, ≥100 MHz) ultrasound transducers than with low-frequency (LF, ≤15 MHz) ultrasound transducers. Hence, the bias voltage of an HVHF amplifier must be stabilized to ensure stable echo-signal amplitudes. We [...] Read more.

The impact of high-voltage–high-frequency (HVHF) amplifiers on echo-signal quality is greater with very-high-frequency (VHF, ≥100 MHz) ultrasound transducers than with low-frequency (LF, ≤15 MHz) ultrasound transducers. Hence, the bias voltage of an HVHF amplifier must be stabilized to ensure stable echo-signal amplitudes. We propose a bias-voltage stabilizer circuit to maintain stable DC voltages over a wide input range, thus reducing the harmonic-distortion components of the echo signals in VHF pulse-echo measurement systems. To confirm the feasibility of the bias-voltage stabilizer, we measured and compared the deviations in the gain of the HVHF amplifier with and without a bias-voltage stabilizer. Between −13 and 26 dBm, the measured gain deviations of a HVHF amplifier with a bias-voltage stabilizer are less than that of an amplifier without a bias-voltage stabilizer. In order to confirm the feasibility of the bias-voltage stabilizer, we compared the pulse-echo responses of the amplifiers, which are typically used for the evaluation of transducers or electronic components used in pulse-echo measurement systems. From the responses, we observed that the amplitudes of the echo signals of a VHF transducer triggered by the HVHF amplifier with a bias-voltage stabilizer were higher than those of the transducer triggered by the HVHF amplifier alone. The second, third, and fourth harmonic-distortion components of the HVHF amplifier with the bias-voltage stabilizer were also lower than those of the HVHF amplifier alone. Hence, the proposed scheme is a promising method for stabilizing the bias voltage of an HVHF amplifier, and improving the echo-signal quality of VHF transducers. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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1901 KiB

Open AccessArticle

Automated Dielectrophoretic Tweezers-Based Force Spectroscopy System in a Microfluidic Device

by Min Hyung Kim, Jeongjick Lee, Kihwan Nam, In Soo Park, Myeonggu Son, Hyunchul Ko, Sangyoup Lee, Dae Sung Yoon, Woo-Jin Chang, Sei Young Lee, Young Ro Yoon and Sang Woo Lee

Sensors 2017, 17(10), 2272; https://doi.org/10.3390/s17102272 - 04 Oct 2017

Cited by 8 | Viewed by 4557

We reported an automated dielectrophoretic (DEP) tweezers-based force spectroscopy system to examine intermolecular weak binding interactions, which consists of three components: (1) interdigitated electrodes and micro-sized polystyrene particles used as DEP tweezers and probes inside a microfluidic device, along with an arbitrary function [...] Read more.

We reported an automated dielectrophoretic (DEP) tweezers-based force spectroscopy system to examine intermolecular weak binding interactions, which consists of three components: (1) interdigitated electrodes and micro-sized polystyrene particles used as DEP tweezers and probes inside a microfluidic device, along with an arbitrary function generator connected to a high voltage amplifier; (2) microscopy hooked up to a high-speed charge coupled device (CCD) camera with an image acquisition device; and (3) a computer aid control system based on the LabVIEW program. Using this automated system, we verified the measurement reliability by measuring intermolecular weak binding interactions, such as hydrogen bonds and Van der Waals interactions. In addition, we also observed the linearity of the force loading rates, which is applied to the probes by the DEP tweezers, by varying the number of voltage increment steps and thus affecting the linearity of the force loading rates. This system provides a simple and low-cost platform to investigate intermolecular weak binding interactions. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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6102 KiB

Open AccessArticle

Microfluidic-Based Measurement Method of Red Blood Cell Aggregation under Hematocrit Variations

by Yang Jun Kang

Sensors 2017, 17(9), 2037; https://doi.org/10.3390/s17092037 - 06 Sep 2017

Cited by 27 | Viewed by 6102

Red blood cell (RBC) aggregation and erythrocyte sedimentation rate (ESR) are considered to be promising biomarkers for effectively monitoring blood rheology at extremely low shear rates. In this study, a microfluidic-based measurement technique is suggested to evaluate RBC aggregation under hematocrit variations due [...] Read more.

Red blood cell (RBC) aggregation and erythrocyte sedimentation rate (ESR) are considered to be promising biomarkers for effectively monitoring blood rheology at extremely low shear rates. In this study, a microfluidic-based measurement technique is suggested to evaluate RBC aggregation under hematocrit variations due to the continuous ESR. After the pipette tip is tightly fitted into an inlet port, a disposable suction pump is connected to the outlet port through a polyethylene tube. After dropping blood (approximately 0.2 mL) into the pipette tip, the blood flow can be started and stopped by periodically operating a pinch valve. To evaluate variations in RBC aggregation due to the continuous ESR, an EAI (Erythrocyte-sedimentation-rate Aggregation Index) is newly suggested, which uses temporal variations of image intensity. To demonstrate the proposed method, the dynamic characterization of the disposable suction pump is first quantitatively measured by varying the hematocrit levels and cavity volume of the suction pump. Next, variations in RBC aggregation and ESR are quantified by varying the hematocrit levels. The conventional aggregation index (AI) is maintained constant, unrelated to the hematocrit values. However, the EAI significantly decreased with respect to the hematocrit values. Thus, the EAI is more effective than the AI for monitoring variations in RBC aggregation due to the ESR. Lastly, the proposed method is employed to detect aggregated blood and thermally-induced blood. The EAI gradually increased as the concentration of a dextran solution increased. In addition, the EAI significantly decreased for thermally-induced blood. From this experimental demonstration, the proposed method is able to effectively measure variations in RBC aggregation due to continuous hematocrit variations, especially by quantifying the EAI. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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2454 KiB

Open AccessArticle

EEG-Based Brain-Computer Interface for Decoding Motor Imagery Tasks within the Same Hand Using Choi-Williams Time-Frequency Distribution

by Rami Alazrai, Hisham Alwanni, Yara Baslan, Nasim Alnuman and Mohammad I. Daoud

Sensors 2017, 17(9), 1937; https://doi.org/10.3390/s17091937 - 23 Aug 2017

Cited by 30 | Viewed by 7711

This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used [...] Read more.

This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used to extract five categories of time-frequency features (TFFs). The TFFs are processed using a hierarchical classification model to identify the MI task encapsulated within the EEG signals. To evaluate the performance of the proposed approach, EEG data were recorded for eighteen intact subjects and four amputated subjects while imagining to perform each of the eleven hand MI tasks. Two performance evaluation analyses, namely channel- and TFF-based analyses, are conducted to identify the best subset of EEG channels and the TFFs category, respectively, that enable the highest classification accuracy between the MI tasks. In each evaluation analysis, the hierarchical classification model is trained using two training procedures, namely subject-dependent and subject-independent procedures. These two training procedures quantify the capability of the proposed approach to capture both intra- and inter-personal variations in the EEG signals for different MI tasks within the same hand. The results demonstrate the efficacy of the approach for classifying the MI tasks within the same hand. In particular, the classification accuracies obtained for the intact and amputated subjects are as high as

88.8 %

and

90.2 %

, respectively, for the subject-dependent training procedure, and

80.8 %

and

87.8 %

, respectively, for the subject-independent training procedure. These results suggest the feasibility of applying the proposed approach to control dexterous prosthetic hands, which can be of great benefit for individuals suffering from hand amputations. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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Open AccessArticle

The Role of Visual Noise in Influencing Mental Load and Fatigue in a Steady-State Motion Visual Evoked Potential-Based Brain-Computer Interface

by Jun Xie, Guanghua Xu, Ailing Luo, Min Li, Sicong Zhang, Chengcheng Han and Wenqiang Yan

Sensors 2017, 17(8), 1873; https://doi.org/10.3390/s17081873 - 14 Aug 2017

Cited by 27 | Viewed by 5075

As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. However, its benefits come at the cost of mental load and [...] Read more.

As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. However, its benefits come at the cost of mental load and fatigue occurring in the concentration on the visual stimuli. Noise, as a ubiquitous random perturbation with the power of randomness, may be exploited by the human visual system to enhance higher-level brain functions. In this study, a novel steady-state motion visual evoked potential (SSMVEP, i.e., one kind of SSVEP)-based BCI paradigm with spatiotemporal visual noise was used to investigate the influence of noise on the compensation of mental load and fatigue deterioration during prolonged attention tasks. Changes in α, θ, θ + α powers, θ/α ratio, and electroencephalography (EEG) properties of amplitude, signal-to-noise ratio (SNR), and online accuracy, were used to evaluate mental load and fatigue. We showed that presenting a moderate visual noise to participants could reliably alleviate the mental load and fatigue during online operation of visual BCI that places demands on the attentional processes. This demonstrated that noise could provide a superior solution to the implementation of visual attention controlling-based BCI applications. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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Open AccessArticle

A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces

by Jeong Heo, Heenam Yoon and Kwang Suk Park

Sensors 2017, 17(7), 1485; https://doi.org/10.3390/s17071485 - 23 Jun 2017

Cited by 57 | Viewed by 15007

Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is [...] Read more.

Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is generated by eye movements and can be measured with electrodes placed around the eye. In this study, we proposed a new practical electrode position on the forehead to measure EOG signals, and we developed a wearable forehead EOG measurement system for use in Human Computer/Machine interfaces (HCIs/HMIs). Four electrodes, including the ground electrode, were placed on the forehead. The two channels were arranged vertically and horizontally, sharing a positive electrode. Additionally, a real-time eye movement classification algorithm was developed based on the characteristics of the forehead EOG. Three applications were employed to evaluate the proposed system: a virtual keyboard using a modified Bremen BCI speller and an automatic sequential row-column scanner, and a drivable power wheelchair. The mean typing speeds of the modified Bremen brain–computer interface (BCI) speller and automatic row-column scanner were 10.81 and 7.74 letters per minute, and the mean classification accuracies were 91.25% and 95.12%, respectively. In the power wheelchair demonstration, the user drove the wheelchair through an 8-shape course without collision with obstacles. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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Open AccessArticle

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner

by Yubo Wang and Kalyana C. Veluvolu

Sensors 2017, 17(6), 1386; https://doi.org/10.3390/s17061386 - 14 Jun 2017

Cited by 13 | Viewed by 6143

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a [...] Read more.

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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Open AccessArticle

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

by Qi Huang, Dapeng Yang, Li Jiang, Huajie Zhang, Hong Liu and Kiyoshi Kotani

Sensors 2017, 17(6), 1370; https://doi.org/10.3390/s17061370 - 13 Jun 2017

Cited by 45 | Viewed by 6602

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents [...] Read more.

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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Open AccessArticle

An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling

by Chen Gong, Dake Liu, Zhidong Miao, Wei Wang and Min Li

Sensors 2017, 17(6), 1358; https://doi.org/10.3390/s17061358 - 11 Jun 2017

Cited by 18 | Viewed by 7634

The inductive link is widely used in implantable biomedical sensor systems to achieve near-field communication (NFC) and wireless power transfer (WPT). However, it is tough to achieve reliable NFC on an inductive WPT link when the coupling coefficient is ultra-low (0.01 typically), since [...] Read more.

The inductive link is widely used in implantable biomedical sensor systems to achieve near-field communication (NFC) and wireless power transfer (WPT). However, it is tough to achieve reliable NFC on an inductive WPT link when the coupling coefficient is ultra-low (0.01 typically), since the NFC signal (especially for the uplink from the in-body part to the out-body part) could be too weak to be detected. Traditional load shift keying (LSK) requires strong coupling to pass the load modulation information to the power source. Instead of using LSK, we propose a dual-carrier NFC scheme for the weak-coupled inductive link; using binary phase shift keying (BPSK) modulation, its downlink data are modulated on the power carrier (2 MHz), while its uplink data are modulated on another carrier (125 kHz). The two carriers are transferred through the same coil pair. To overcome the strong interference of the power carrier, dedicated circuits are introduced. In addition, to minimize the power transfer efficiency decrease caused by adding NFC, we optimize the inductive link circuit parameters and approach the receiver sensitivity limit. In the prototype experiments, even though the coupling coefficient is as low as 0.008, the in-body transmitter costs only 0.61 mW power carrying 10 kbps of data, and achieves a 1 × 10

- 7

bit error rate under the strong interference of WPT. This dual-carrier NFC scheme could be useful for small-sized implantable biomedical sensor applications. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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Open AccessArticle

Smartphone-Based pH Sensor for Home Monitoring of Pulmonary Exacerbations in Cystic Fibrosis

by Alexander Sun, Tom Phelps, Chengyang Yao, A. G. Venkatesh, Douglas Conrad and Drew A. Hall

Sensors 2017, 17(6), 1245; https://doi.org/10.3390/s17061245 - 30 May 2017

Cited by 14 | Viewed by 7845

Currently, Cystic Fibrosis (CF) patients lack the ability to track their lung health at home, relying instead on doctor checkups leading to delayed treatment and lung damage. By leveraging the ubiquity of the smartphone to lower costs and increase portability, a smartphone-based peripheral [...] Read more.

Currently, Cystic Fibrosis (CF) patients lack the ability to track their lung health at home, relying instead on doctor checkups leading to delayed treatment and lung damage. By leveraging the ubiquity of the smartphone to lower costs and increase portability, a smartphone-based peripheral pH measurement device was designed to attach directly to the headphone port to harvest power and communicate with a smartphone application. This platform was tested using prepared pH buffers and sputum samples from CF patients. The system matches within ~0.03 pH of a benchtop pH meter while fully powering itself and communicating with a Samsung Galaxy S3 smartphone paired with either a glass or Iridium Oxide (IrOx) electrode. The IrOx electrodes were found to have 25% higher sensitivity than the glass probes at the expense of larger drift and matrix sensitivity that can be addressed with proper calibration. The smartphone-based platform has been demonstrated as a portable replacement for laboratory pH meters, and supports both highly robust glass probes and the sensitive and miniature IrOx electrodes with calibration. This tool can enable more frequent pH sputum tracking for CF patients to help detect the onset of pulmonary exacerbation to provide timely and appropriate treatment before serious damage occurs. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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Open AccessArticle

Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms

by Radek Martinek, Radana Kahankova, Homer Nazeran, Jaromir Konecny, Janusz Jezewski, Petr Janku, Petr Bilik, Jan Zidek, Jan Nedoma and Marcel Fajkus

Sensors 2017, 17(5), 1154; https://doi.org/10.3390/s17051154 - 19 May 2017

Cited by 85 | Viewed by 10609

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size

μ

and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The [...] Read more.

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size

μ

and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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Open AccessArticle

A Combined Independent Source Separation and Quality Index Optimization Method for Fetal ECG Extraction from Abdominal Maternal Leads

by Lucia Billeci and Maurizio Varanini

Sensors 2017, 17(5), 1135; https://doi.org/10.3390/s17051135 - 16 May 2017

Cited by 24 | Viewed by 5217

The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index [...] Read more.

The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index optimization (ICAQIO-based). The algorithm was compared with two methods applying the two different criteria independently—the ICA-based and the QIO-based methods—which were previously developed by our group. All three methods were tested on the recently implemented Fetal ECG Synthetic Database (FECGSYNDB). Moreover, the performance of the algorithm was tested on real data from the PhysioNet fetal ECG Challenge 2013 Database. The proposed combined method outperformed the other two algorithms on the FECGSYNDB (ICAQIO-based: 98.78%, QIO-based: 97.77%, ICA-based: 97.61%). Significant differences were obtained in particular in the conditions when uterine contractions and maternal and fetal ectopic beats occurred. On the real data, all three methods obtained very high performances, with the QIO-based method proving slightly better than the other two (ICAQIO-based: 99.38%, QIO-based: 99.76%, ICA-based: 99.37%). The findings from this study suggest that the proposed method could potentially be applied as a novel algorithm for accurate extraction of fECG, especially in critical recording conditions. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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Open AccessArticle

Wearable Contactless Respiration Sensor Based on Multi-Material Fibers Integrated into Textile

by Philippe Guay, Stepan Gorgutsa, Sophie LaRochelle and Younes Messaddeq

Sensors 2017, 17(5), 1050; https://doi.org/10.3390/s17051050 - 06 May 2017

Cited by 42 | Viewed by 21753

In this paper, we report on a novel sensor for the contactless monitoring of the respiration rate, made from multi-material fibers arranged in the form of spiral antenna (2.45 GHz central frequency). High flexibility of the used composite metal-glass-polymer fibers permits their integration [...] Read more.

In this paper, we report on a novel sensor for the contactless monitoring of the respiration rate, made from multi-material fibers arranged in the form of spiral antenna (2.45 GHz central frequency). High flexibility of the used composite metal-glass-polymer fibers permits their integration into a cotton t-shirt without compromising comfort or restricting movement of the user. At the same time, change of the antenna geometry, due to the chest expansion and the displacement of the air volume in the lungs, is found to cause a significant shift of the antenna operational frequency, thus allowing respiration detection. In contrast with many current solutions, respiration is detected without attachment of the electrodes of any kind to the user’s body, neither direct contact of the fiber with the skin is required. Respiration patterns for two male volunteers were recorded with the help of a sensor prototype integrated into standard cotton t-shirt in sitting, standing, and lying scenarios. The typical measured frequency shift for the deep and shallow breathing was found to be in the range 120–200 MHz and 10–15 MHz, respectively. The same spiral fiber antenna is also shown to be suitable for short-range wireless communication, thus allowing respiration data transmission, for example, via the Bluetooth protocol, to mobile handheld devices. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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Review

Jump to: Research

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Open AccessReview

Sensors and Biosensors for C-Reactive Protein, Temperature and pH, and Their Applications for Monitoring Wound Healing: A Review

by Pietro Salvo, Valentina Dini, Arno Kirchhain, Agata Janowska, Teresa Oranges, Andrea Chiricozzi, Tommaso Lomonaco, Fabio Di Francesco and Marco Romanelli

Sensors 2017, 17(12), 2952; https://doi.org/10.3390/s17122952 - 19 Dec 2017

Cited by 86 | Viewed by 12800

Wound assessment is usually performed in hospitals or specialized labs. However, since patients spend most of their time at home, a remote real time wound monitoring would help providing a better care and improving the healing rate. This review describes the advances in [...] Read more.

Wound assessment is usually performed in hospitals or specialized labs. However, since patients spend most of their time at home, a remote real time wound monitoring would help providing a better care and improving the healing rate. This review describes the advances in sensors and biosensors for monitoring the concentration of C-reactive protein (CRP), temperature and pH in wounds. These three parameters can be used as qualitative biomarkers to assess the wound status and the effectiveness of therapy. CRP biosensors can be classified in: (a) field effect transistors, (b) optical immunosensors based on surface plasmon resonance, total internal reflection, fluorescence and chemiluminescence, (c) electrochemical sensors based on potentiometry, amperometry, and electrochemical impedance, and (d) piezoresistive sensors, such as quartz crystal microbalances and microcantilevers. The last section reports the most recent developments for wearable non-invasive temperature and pH sensors suitable for wound monitoring. Full article

(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)

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