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Non-Invasive Biomedical Sensors
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Non-Invasive Biomedical Sensors

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

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 133590

Special Issue Editors

Prof. Dr. Antonio Tricoli

E-Mail Website
Guest Editor
Australian National University, Research School of Engineering, Canberra, Australia
Interests: nanotechnology; self-assembly; sensors; nanomedicine; functional coatings; renewable energy production and chemical storage; aerosols; flame synthesis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giovanni Neri

E-Mail Website
Guest Editor
Department of Engineering, Messina University, Piazza Pugliatti, 1, 98122 Messina, Italy
Interests: synthesis of novel sensing materials; nanostructured materials for chemical and electrochemical sensing; metal oxide semiconductor-based gas sensors; biosensors; fabrication of chemical sensors; environmental sensors; automotive gas sensors; biomedical sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to cordially invite you to participate in a special issue on “Non-Invasive Biomedical Sensors”. This special issue is focused on the latest achievements in chemical and physical biomedical sensors for the non-invasive and personalized health monitoring. Today, a key factor to reduce costs and time of hospitalization while improving the quality of life and mobility of patients with chronic disorders is the development of non-invasive miniaturized sensor systems, which can be deployed in point of care, used at home or integrated in wearable devices. A key challenge is the engineering of low-cost miniaturized sensing systems which provide continuous, real time and long-term monitoring of key biomarkers.

Various biomedical sensors have been successfully implemented to measure some important physiological parameters like heart rate, blood pressure and breath hydrogen, etc., in a non-invasive way. Further, connecting these devices to a portable/wireless power supply and data transmission unit has the potential to support point-of-care treatment and diagnostics of many diseases, with a dramatic advances over current methods.

However, advancement in materials science, flexible electronics and biomedicine are required to achieve reliable non-invasive measurement of a comprehensive set of biomarkers required for the management of numerous diseases such as blood glucose level for type-1 diabetes. The successful development of platform technologies for non-invasive measurement of a sufficiently broad set of biomarkers could open up to doctors and patients new exciting avenues for personalized health-care monitoring applications in the near future.

Contributions to this Special Issue may include, but are not limited to:

  • Novel sensing techniques for non-invasive measurement of biomarkers;
  • New insights into non-invasive biomarkers which provide information on the physiological state of people;
  • Recent advances in sensor materials, properties and device concepts for non-invasive monitoring of chemical and physical body parameters;
  • Sensor fabrication and testing techniques; Wearable sensors, Wireless sensors, and application-oriented non-invasive sensor systems, as well as closely-related topics, are welcome.
Assoc. Prof. Dr. Antonio Tricoli
Prof. Dr. Giovanni Neri
Guest Editors

Manuscript Submission Information

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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

  • non-invasive biomedical sensors
  • wearable sensors
  • wireless sensing devices
  • body-sensor networks
  • breath analysis
  • sweat analysis
  • tear analysis
  • saliva analysis
  • biochemical sensors

Published Papers (21 papers)

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19 pages, 2239 KiB  
Article
Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography
by Haiyan Wu, Zhong Ji and Mengze Li
Sensors 2019, 19(24), 5543; https://doi.org/10.3390/s19245543 - 15 Dec 2019
Cited by 15 | Viewed by 5114
Abstract
Blood pressure is an extremely important blood hemodynamic parameter. The pulse wave contains abundant blood-pressure information, and the convenience and non-invasivity of its measurement make it ideal for non-invasive continuous monitoring of blood pressure. Based on combined photoplethysmography and electrocardiogram signals, this study [...] Read more.
Blood pressure is an extremely important blood hemodynamic parameter. The pulse wave contains abundant blood-pressure information, and the convenience and non-invasivity of its measurement make it ideal for non-invasive continuous monitoring of blood pressure. Based on combined photoplethysmography and electrocardiogram signals, this study aimed to extract the waveform information, introduce individual characteristics, and construct systolic and diastolic blood-pressure (SBP and DBP) estimation models using the back-propagation error (BP) neural network. During the model construction process, the mean impact value method was employed to investigate the impact of each feature on the model output and reduce feature redundancy. Moreover, the multiple population genetic algorithm was applied to optimize the BP neural network and determine the initial weights and threshold of the network. Finally, the models were integrated for further optimization to generate the final individualized continuous blood-pressure monitoring models. The results showed that the predicted values of the model in this study correlated significantly with the measured values of the electronic sphygmomanometer. The estimation errors of the model met the Association for the Advancement of Medical Instrumentation (AAMI) criteria (the SBP error was 2.5909 ± 3.4148 mmHg, and the DBP error was 2.6890 ± 3.3117 mmHg) and the Grade A British Hypertension Society criteria. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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16 pages, 2601 KiB  
Article
Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device
by Pei-Yu Chiang, Paul C. -P. Chao, Tse-Yi Tu, Yung-Hua Kao, Chih-Yu Yang, Der-Cherng Tarng and Chin-Long Wey
Sensors 2019, 19(15), 3422; https://doi.org/10.3390/s19153422 - 04 Aug 2019
Cited by 10 | Viewed by 5495
Abstract
The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the [...] Read more.
The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the quality of AVF: the blood flow volume (BFV) and the degree of stenosis (DOS). In hospitals, the BFV and DOS of AVFs are nowadays assessed using an ultrasound Doppler machine, which is bulky, expensive, hard to use, and time consuming. In this study, a newly-developed PPG sensor device was utilized to provide patients and doctors with an inexpensive and small-sized solution for ubiquitous AVF assessment. The readout in this sensor was custom-designed to increase the signal-to-noise ratio (SNR) and reduce the environment interference via maximizing successfully the full dynamic range of measured PPG entering an analog–digital converter (ADC) and effective filtering techniques. With quality PPG measurements obtained, machine learning classifiers including SVM were adopted to assess AVF quality, where the input features are determined based on optical Beer–Lambert’s law and hemodynamic model, to ensure all the necessary features are considered. Finally, the clinical experiment results showed that the proposed PPG sensor device successfully achieved an accuracy of 87.84% based on SVM analysis in assessing DOS at AVF, while an accuracy of 88.61% was achieved for assessing BFV at AVF. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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19 pages, 4065 KiB  
Article
Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue?
by Sergio Fuentes del Toro, Silvia Santos-Cuadros, Ester Olmeda, Carolina Álvarez-Caldas, Vicente Díaz and José Luís San Román
Sensors 2019, 19(14), 3204; https://doi.org/10.3390/s19143204 - 20 Jul 2019
Cited by 32 | Viewed by 9876
Abstract
Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface [...] Read more.
Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface electromyography (sEMG) system that is able to reliably detect muscle fatigue. With this main goal, the contribution of this work is the design of a low-cost sEMG system that allows assessing when fatigue appears in a muscle. To that aim, low-cost sEMG sensors, an Arduino board and a PC were used and afterwards their validity was checked by means of an experiment with 28 volunteers. This experiment collected information from volunteers, such as their level of physical activity, and invited them to perform an isometric contraction while an sEMG signal of their quadriceps was recorded by the low-cost equipment. After a wavelet filtering of the signal, root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) were chosen as representative features to evaluate fatigue. Results show how the behaviour of these parameters across time is shown in the literature coincides with past studies (RMS and MAV increase while MNF decreases when fatigue appears). Thus, this work proves the feasibility of a low-cost system to reliably detect muscle fatigue. This system could be implemented in several fields, such as sport, ergonomics, rehabilitation or human-computer interactions. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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11 pages, 3275 KiB  
Article
Scattering of Microwaves by a Passive Array Antenna Based on Amorphous Ferromagnetic Microwires for Wireless Sensors with Biomedical Applications
by Alberto Moya, Diego Archilla, Elena Navarro, Antonio Hernando and Pilar Marín
Sensors 2019, 19(14), 3060; https://doi.org/10.3390/s19143060 - 11 Jul 2019
Cited by 11 | Viewed by 3224
Abstract
Co-based amorphous microwires presenting the giant magnetoimpedance effect are proposed as sensing elements for high sensitivity biosensors. In this work we report an experimental method for contactless detection of stress, temperature, and liquid concentration with application in medical sensors using the giant magnetoimpedance [...] Read more.
Co-based amorphous microwires presenting the giant magnetoimpedance effect are proposed as sensing elements for high sensitivity biosensors. In this work we report an experimental method for contactless detection of stress, temperature, and liquid concentration with application in medical sensors using the giant magnetoimpedance effect on microwires in the GHz range. The method is based on the scattering of electromagnetic microwaves by FeCoSiB amorphous metallic microwires. A modulation of the scattering parameter is achieved by applying a magnetic bias field that tunes the magnetic permeability of the ferromagnetic microwires. We demonstrate that the OFF/ON switching of the bias activates or cancels the amorphous ferromagnetic microwires (AFMW) antenna behavior. We show the advantages of measuring the performing time dependent frequency sweeps. In this case, the AC-bias modulation of the scattering coefficient versus frequency may be clearly appreciated. Furthermore, this modulation is enhanced by using arrays of microwires with an increasing number of individual microwires according to the antenna radiation theory. Transmission spectra show significant changes in the range of 3 dB for a relatively weak magnetic field of 15 Oe. A demonstration of the possibilities of the method for biomedical applications is shown by means of wireless temperature detector from 0 to 100 °C. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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12 pages, 7050 KiB  
Article
Experimental and Numerical Investigation of a Photoacoustic Resonator for Solid Samples: Towards a Non-Invasive Glucose Sensor
by Said El-Busaidy, Bernd Baumann, Marcus Wolff, Lars Duggen and Henry Bruhns
Sensors 2019, 19(13), 2889; https://doi.org/10.3390/s19132889 - 29 Jun 2019
Cited by 9 | Viewed by 5304
Abstract
T-cell resonators have been used lately for non-invasive blood glucose measurements for photoacoustic spectroscopy on skin samples. A resonator has a significant role in determining the strength of the measured signal and the overall sensitivity of the sensor. Here we present results of [...] Read more.
T-cell resonators have been used lately for non-invasive blood glucose measurements for photoacoustic spectroscopy on skin samples. A resonator has a significant role in determining the strength of the measured signal and the overall sensitivity of the sensor. Here we present results of the measurement of the photoacoustic signal of such a T-cell resonator. The signal is also modelled using the amplitude mode expansion method, which is based on eigenmode expansion and the introduction of losses in the form of loss factors. The measurement reproduced almost all the calculated resonances from the numerical models with fairly good agreement. The cause of the differences between the measured and the simulated resonances are explained. In addition, the amplitude mode expansion simulation model is established as a faster and computationally less demanding photoacoustic simulation alternative to the viscothermal model. The resonance frequencies from the two models differ by less than 1.8%. It is noted that the relative height of the amplitudes from the two models depends on the location of the antinodes within the different parts of the resonator. The amplitude mode expansion model provides a quick simulation tool for the optimization and design of macro resonators. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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18 pages, 2754 KiB  
Article
A Non-Invasive Continuous Blood Pressure Estimation Approach Based on Machine Learning
by Shuo Chen, Zhong Ji, Haiyan Wu and Yingchao Xu
Sensors 2019, 19(11), 2585; https://doi.org/10.3390/s19112585 - 06 Jun 2019
Cited by 60 | Viewed by 5780
Abstract
Considering the existing issues of traditional blood pressure (BP) measurement methods and non-invasive continuous BP measurement techniques, this study aims to establish the systolic BP and diastolic BP estimation models based on machine learning using pulse transit time and characteristics of pulse waveform. [...] Read more.
Considering the existing issues of traditional blood pressure (BP) measurement methods and non-invasive continuous BP measurement techniques, this study aims to establish the systolic BP and diastolic BP estimation models based on machine learning using pulse transit time and characteristics of pulse waveform. In the process of model construction, the mean impact value method was introduced to investigate the impact of each feature on the models and the genetic algorithm was introduced to implement parameter optimization. The experimental results showed that the proposed models could effectively describe the nonlinear relationship between the features and BP and had higher accuracy than the traditional methods with the error of 3.27 ± 5.52 mmHg for systolic BP and 1.16 ± 1.97 mmHg for diastolic BP. Moreover, the estimation errors met the requirements of the Advancement of Medical Instrumentation and British Hypertension Society criteria. In conclusion, this study was helpful in promoting the practical application of methods for non-invasive continuous BP estimation models. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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11 pages, 10808 KiB  
Article
Wearable Enzymatic Alcohol Biosensor
by Bob Lansdorp, William Ramsay, Rashad Hamid and Evan Strenk
Sensors 2019, 19(10), 2380; https://doi.org/10.3390/s19102380 - 24 May 2019
Cited by 34 | Viewed by 11358
Abstract
Transdermal alcohol biosensors have the ability to detect the alcohol that emanates from the bloodstream and diffuses through the skin. However, previous biosensors have suffered from long-term fouling of the sensor element and drift in the resulting sensor readings over time. Here, we [...] Read more.
Transdermal alcohol biosensors have the ability to detect the alcohol that emanates from the bloodstream and diffuses through the skin. However, previous biosensors have suffered from long-term fouling of the sensor element and drift in the resulting sensor readings over time. Here, we report a wearable alcohol sensor platform that solves the problem of sensor fouling by enabling drift-free signals in vivo for up to 24 h and an interchangeable cartridge connection that enables consecutive days of measurement. We demonstrate how alcohol oxidase enzyme and Prussian Blue can be combined to prevent baseline drift above 25 nA, enabling sensitive detection of transdermal alcohol. Laboratory characterization of the enzymatic alcohol sensor demonstrates that the sensor is mass-transfer-limited by a diffusion-limiting membrane of lower permeability than human skin and a linear sensor range between 0 mM and 50 mM. Further, we show continuous transdermal alcohol data recorded with a human subject for two consecutive days. The non-invasive sensor presented here is an objective alternative to the self-reports used commonly to quantify alcohol consumption in research studies. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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15 pages, 963 KiB  
Article
Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates
by Soojeong Lee, Gangseong Lee and Gwanggil Jeon
Sensors 2019, 19(9), 2137; https://doi.org/10.3390/s19092137 - 08 May 2019
Cited by 3 | Viewed by 2789
Abstract
Oscillometric blood pressure (BP) monitors currently estimate a single point but do not identify variations in response to physiological characteristics. In this paper, to analyze BP’s normality based on oscillometric measurements, we use statistical approaches including kurtosis, skewness, Kolmogorov-Smirnov, and correlation tests. Then, [...] Read more.
Oscillometric blood pressure (BP) monitors currently estimate a single point but do not identify variations in response to physiological characteristics. In this paper, to analyze BP’s normality based on oscillometric measurements, we use statistical approaches including kurtosis, skewness, Kolmogorov-Smirnov, and correlation tests. Then, to mitigate uncertainties, we use a deep learning method to determine the confidence limits (CLs) of BP measurements based on their normality. The proposed deep learning regression model decreases the standard deviation of error (SDE) of the mean error and the mean absolute error and reduces the uncertainties of the CLs and SDEs of the proposed technique. We validate the normality of the distribution of the BP estimation which fits the standard normal distribution very well. We use a rank test in the deep learning technique to demonstrate the independence of the artificial systolic BP and diastolic BP estimations. We perform statistical tests to verify the normality of the BP measurements for individual subjects. The proposed methodology provides accurate BP estimations and reduces the uncertainties associated with the CLs and SDEs using the deep learning algorithm. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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10 pages, 1996 KiB  
Article
Reliability and Validity of Non-invasive Blood Pressure Measurement System Using Three-Axis Tactile Force Sensor
by Sun-Young Yoo, Ji-Eun Ahn, György Cserey, Hae-Young Lee and Jong-Mo Seo
Sensors 2019, 19(7), 1744; https://doi.org/10.3390/s19071744 - 11 Apr 2019
Cited by 9 | Viewed by 5734
Abstract
Blood pressure (BP) is a physiological parameter reflecting hemodynamic factors and is crucial in evaluating cardiovascular disease and its prognosis. In the present study, the reliability of a non-invasive and continuous BP measurement using a three-axis tactile force sensor was verified. All the [...] Read more.
Blood pressure (BP) is a physiological parameter reflecting hemodynamic factors and is crucial in evaluating cardiovascular disease and its prognosis. In the present study, the reliability of a non-invasive and continuous BP measurement using a three-axis tactile force sensor was verified. All the data were collected every 2 min for the short-term experiment, and every 10 min for the long-term experiment. In addition, the effects on the BP measurement of external physical factors such as the tension to the radial artery on applying the device and wrist circumference were evaluated. A high correlation between the measured BP with the proposed system and with the cuff-based non-invasive blood pressure, and reproducibility, were demonstrated. All data satisfied the Association for the Advancement of Medical Instrumentation criteria. The external physical factors did not affect the measurement results. In addition to previous research indicating the high reliability of the arterial pulse waveforms, the present results have demonstrated the reliability of numerical BP values, and this implies that the three-axis force sensor can be used as a patient monitoring device. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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17 pages, 6151 KiB  
Article
A Smart Wireless Ear-Worn Device for Cardiovascular and Sweat Parameter Monitoring During Physical Exercise: Design and Performance Results
by Bruno Gil, Salzitsa Anastasova and Guang Z. Yang
Sensors 2019, 19(7), 1616; https://doi.org/10.3390/s19071616 - 04 Apr 2019
Cited by 40 | Viewed by 6586
Abstract
Wearable biomedical technology has gained much support lately as devices have become more affordable to the general public and they can easily interact with mobile phones and other platforms. The feasibility and accuracy of the data generated by these devices so as to [...] Read more.
Wearable biomedical technology has gained much support lately as devices have become more affordable to the general public and they can easily interact with mobile phones and other platforms. The feasibility and accuracy of the data generated by these devices so as to replace the standard medical methods in use today is still under scrutiny. In this paper, we present an ear-worn device to measure cardiovascular and sweat parameters during physical exercise. ECG bipolar recordings capture the electric potential around both ears, whereas sweat rate is estimated by the impedance method over one segment of tissue closer to the left ear, complemented by the measurement of the lactate and pH levels using amperiometric and potentiometric sensors, respectively. Together with head acceleration, the acquired data is sent to a mobile phone via BLE, enabling extended periods of signal recording. Results obtained by the device have shown a SNR level of 18 dB for the ECG signal recorded around the ears, a THD value of −20.46 dB for the excitation signal involved in impedance measurements, sweat conductivity of 0.08 S/m at 1 kHz and sensitivities of 50 mV/pH and 0.8 μA/mM for the pH and lactate acquisition channels, respectively. Testing of the device was performed in human subjects during indoors cycling with characteristic level changes. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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12 pages, 3137 KiB  
Article
Hydrogel Heart Model with Temperature Memory Properties for Surgical Simulation
by Hisataka Maruyama, Yuki Yokota, Keisuke Hosono and Fumihito Arai
Sensors 2019, 19(5), 1102; https://doi.org/10.3390/s19051102 - 04 Mar 2019
Cited by 9 | Viewed by 3766
Abstract
The continual development of surgical technology has led to a demand for surgical simulators for evaluating and improving the surgical technique of surgeons. To meet these needs, simulators must incorporate a sensing function into the organ model for evaluating the surgical techniques. However, [...] Read more.
The continual development of surgical technology has led to a demand for surgical simulators for evaluating and improving the surgical technique of surgeons. To meet these needs, simulators must incorporate a sensing function into the organ model for evaluating the surgical techniques. However, it is difficult to incorporate a temperature sensor into the conventional cardiac training model. In this study, we propose a heart model for surgical training of cardiac catheter ablation made from hydrogel, which has temperature memory properties. The heart model consists of a photo-crosslinkable hydrogel mixed with an irreversible temperature indicator that exhibits a color change from magenta to colorless at 55 °C. The Young’s modulus, electrical resistivity, thermal conductivity, and specific heat capacity of the hydrogel material were evaluated and compared with those of human heart. Furthermore, temperature calibration based on the color of the hydrogel material confirmed that the temperature measurement accuracy of the material is ±0.18 °C (at 56 °C). A heart model for catheter ablation was fabricated using the hydrogel material and a molding method, and the color change due to temperature change was evaluated. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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20 pages, 1413 KiB  
Article
Signature Inspired Home Environments Monitoring System Using IR-UWB Technology
by Soumya Prakash Rana, Maitreyee Dey, Mohammad Ghavami and Sandra Dudley
Sensors 2019, 19(2), 385; https://doi.org/10.3390/s19020385 - 18 Jan 2019
Cited by 21 | Viewed by 4555
Abstract
Home monitoring and remote care systems aim to ultimately provide independent living care scenarios through non-intrusive, privacy-protecting means. Their main aim is to provide care through appreciating normal habits, remotely recognizing changes and acting upon those changes either through informing the person themselves, [...] Read more.
Home monitoring and remote care systems aim to ultimately provide independent living care scenarios through non-intrusive, privacy-protecting means. Their main aim is to provide care through appreciating normal habits, remotely recognizing changes and acting upon those changes either through informing the person themselves, care providers, family members, medical practitioners, or emergency services, depending on need. Care giving can be required at any age, encompassing young to the globally growing aging population. A non-wearable and unobtrusive architecture has been developed and tested here to provide a fruitful health and wellbeing-monitoring framework without interfering in a user’s regular daily habits and maintaining privacy. This work focuses on tracking locations in an unobtrusive way, recognizing daily activities, which are part of maintaining a healthy/regular lifestyle. This study shows an intelligent and locally based edge care system (ECS) solution to identify the location of an occupant’s movement from daily activities using impulse radio-ultra wide band (IR-UWB) radar. A new method is proposed calculating the azimuth angle of a movement from the received pulse and employing radar principles to determine the range of that movement. Moreover, short-term fourier transform (STFT) has been performed to determine the frequency distribution of the occupant’s action. Therefore, STFT, azimuth angle, and range calculation together provide the information to understand how occupants engage with their environment. An experiment has been carried out for an occupant at different times of the day during daily household activities and recorded with time and room position. Subsequently, these time-frequency outcomes, along with the range and azimuth information, have been employed to train a support vector machine (SVM) learning algorithm for recognizing indoor locations when the person is moving around the house, where little or no movement indicates the occurrence of abnormalities. The implemented framework is connected with a cloud server architecture, which enables to act against any abnormality remotely. The proposed methodology shows very promising results through statistical validation and achieved over 90% testing accuracy in a real-time scenario. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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14 pages, 2725 KiB  
Article
Non-Contact, Simple Neonatal Monitoring by Photoplethysmography
by Juan-Carlos Cobos-Torres, Mohamed Abderrahim and José Martínez-Orgado
Sensors 2018, 18(12), 4362; https://doi.org/10.3390/s18124362 - 10 Dec 2018
Cited by 36 | Viewed by 4058
Abstract
This paper presents non-contact vital sign monitoring in neonates, based on image processing, where a standard color camera captures the plethysmographic signal and the heart and breathing rates are processed and estimated online. It is important that the measurements are taken in a [...] Read more.
This paper presents non-contact vital sign monitoring in neonates, based on image processing, where a standard color camera captures the plethysmographic signal and the heart and breathing rates are processed and estimated online. It is important that the measurements are taken in a non-invasive manner, which is imperceptible to the patient. Currently, many methods have been proposed for non-contact measurement. However, to the best of the authors’ knowledge, it has not been possible to identify methods with low computational costs and a high tolerance to artifacts. With the aim of improving contactless measurement results, the proposed method based on the computer vision technique is enhanced to overcome the mentioned drawbacks. The camera is attached to an incubator in the Neonatal Intensive Care Unit and a single area in the neonate’s diaphragm is monitored. Several factors are considered in the stages of image acquisition, as well as in the plethysmographic signal formation, pre-filtering and filtering. The pre-filter step uses numerical analysis techniques to reduce the signal offset. The proposed method decouples the breath rate from the frequency of sinus arrhythmia. This separation makes it possible to analyze independently any cardiac and respiratory dysrhythmias. Nine newborns were monitored with our proposed method. A Bland-Altman analysis of the data shows a close correlation of the heart rates measured with the two approaches (correlation coefficient of 0.94 for heart rate (HR) and 0.86 for breath rate (BR)) with an uncertainty of 4.2 bpm for HR and 4.9 for BR (k = 1). The comparison of our method and another non-contact method considered as a standard independent component analysis (ICA) showed lower central processing unit (CPU) usage for our method (75% less CPU usage). Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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13 pages, 2570 KiB  
Article
Research on the Temperature Characteristics of the Photoacoustic Sensor of Glucose Solution
by Wei Tao, Zhiqian Lu, Qiaozhi He, Pengfei Lv, Qian Wang and Hui Zhao
Sensors 2018, 18(12), 4323; https://doi.org/10.3390/s18124323 - 07 Dec 2018
Cited by 8 | Viewed by 3238
Abstract
In order to weaken the influence of temperature on photoacoustic (PA) measurements and compensate PA signals with a proposed theoretical model, the relationship of PA signal amplitude with temperature, under the condition of different glucose concentrations and different light intensities, was studied in [...] Read more.
In order to weaken the influence of temperature on photoacoustic (PA) measurements and compensate PA signals with a proposed theoretical model, the relationship of PA signal amplitude with temperature, under the condition of different glucose concentrations and different light intensities, was studied in this paper. First, the theoretical model was derived from the theory of the PA effect. Then, the temperature characteristics of the PA signals were investigated, based on the analyses of the temperature-dependent Grüneisen parameter in glucose solution. Next, the concept of a PA temperature coefficient was proposed in this paper. The result of the theoretical analysis shows that this coefficient is linear to light intensity and irrelevant to the concentration of glucose solution. Furthermore, a new concept of a PA temperature coefficient of unit light intensity was proposed in this paper. This coefficient is approximately constant, with different light intensities and solution concentrations, which is similar to the thermal expansion coefficient. After calculation, the PA temperature coefficient by the unit light intensity of glucose solution is about 0.936 bar/K. Finally, relevant experiments were carried out to verify the theoretical analysis, and the PA temperature coefficient of the unit light intensity of glucose solution is about 0.04/°C. This method can also be used in sensors measuring concentrations in other aqueous solutions. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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15 pages, 2416 KiB  
Article
Cuffless Blood Pressure Estimation Using Pressure Pulse Wave Signals
by Zeng-Ding Liu, Ji-Kui Liu, Bo Wen, Qing-Yun He, Ye Li and Fen Miao
Sensors 2018, 18(12), 4227; https://doi.org/10.3390/s18124227 - 02 Dec 2018
Cited by 53 | Viewed by 8915
Abstract
Pulse transit time (PTT) has received considerable attention for noninvasive cuffless blood pressure measurement. However, this approach is inconvenient to deploy in wearable devices because two sensors are required for collecting two-channel physiological signals, such as electrocardiogram and pulse wave signals. In this [...] Read more.
Pulse transit time (PTT) has received considerable attention for noninvasive cuffless blood pressure measurement. However, this approach is inconvenient to deploy in wearable devices because two sensors are required for collecting two-channel physiological signals, such as electrocardiogram and pulse wave signals. In this study, we investigated the pressure pulse wave (PPW) signals collected from one piezoelectric-induced sensor located at a single site for cuffless blood pressure estimation. Twenty-one features were extracted from PPW that collected from the radial artery, and then a linear regression method was used to develop blood pressure estimation models by using the extracted PPW features. Sixty-five middle-aged and elderly participants were recruited to evaluate the performance of the constructed blood pressure estimation models, with oscillometric technique-based blood pressure as a reference. The experimental results indicated that the mean ± standard deviation errors for the estimated systolic blood pressure and diastolic blood pressure were 0.70 ± 7.78 mmHg and 0.83 ± 5.45 mmHg, which achieved a decrease of 1.33 ± 0.37 mmHg in systolic blood pressure and 1.14 ± 0.20 mmHg in diastolic blood pressure, compared with the conventional PTT-based method. The proposed model also demonstrated a high level of robustness in a maximum 60-day follow-up study. These results indicated that PPW obtained from the piezoelectric sensor has great feasibility for cuffless blood pressure estimation, and could serve as a promising method in home healthcare settings. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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9 pages, 2133 KiB  
Article
Highly Sensitive Room-Temperature Sensor Based on Nanostructured K2W7O22 for Application in the Non-Invasive Diagnosis of Diabetes
by Md Razuan Hossain, Qifeng Zhang, Michael Johnson and Danling Wang
Sensors 2018, 18(11), 3703; https://doi.org/10.3390/s18113703 - 31 Oct 2018
Cited by 9 | Viewed by 3805
Abstract
Diabetes is one of the most rapidly-growing chronic diseases in the world. Acetone, a volatile organic compound in exhaled breath, shows a positive correlation with blood glucose and has proven to be a biomarker for type-1 diabetes. Measuring the level of acetone in [...] Read more.
Diabetes is one of the most rapidly-growing chronic diseases in the world. Acetone, a volatile organic compound in exhaled breath, shows a positive correlation with blood glucose and has proven to be a biomarker for type-1 diabetes. Measuring the level of acetone in exhaled breath can provide a non-invasive, low risk of infection, low cost, and convenient way to monitor the health condition of diabetics. There has been continuous demand for the improvement of this non-invasive, sensitive sensor system to provide a fast and real-time electronic readout of blood glucose levels. A novel nanostructured K2W7O22 has been recently used to test acetone with concentration from 0 parts-per-million (ppm) to 50 ppm at room temperature. The results revealed that a K2W7O22 sensor shows a sensitive response to acetone, but the detection limit is not ideal due to the limitations of the detection system of the device. In this paper, we report a K2W7O22 sensor with an improved sensitivity and detection limit by using an optimized circuit to minimize the electronic noise and increase the signal to noise ratio for the purpose of weak signal detection while the concentration of acetone is very low. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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14 pages, 3971 KiB  
Article
LTCC Packaged Ring Oscillator Based Sensor for Evaluation of Cell Proliferation
by Joni Kilpijärvi, Niina Halonen, Maciej Sobocinski, Antti Hassinen, Bathiya Senevirathna, Kajsa Uvdal, Pamela Abshire, Elisabeth Smela, Sakari Kellokumpu, Jari Juuti and Anita Lloyd Spetz
Sensors 2018, 18(10), 3346; https://doi.org/10.3390/s18103346 - 07 Oct 2018
Cited by 9 | Viewed by 5233
Abstract
A complementary metal-oxide-semiconductor (CMOS) chip biosensor was developed for cell viability monitoring based on an array of capacitance sensors utilizing a ring oscillator. The chip was packaged in a low temperature co-fired ceramic (LTCC) module with a flip chip bonding technique. A microcontroller [...] Read more.
A complementary metal-oxide-semiconductor (CMOS) chip biosensor was developed for cell viability monitoring based on an array of capacitance sensors utilizing a ring oscillator. The chip was packaged in a low temperature co-fired ceramic (LTCC) module with a flip chip bonding technique. A microcontroller operates the chip, while the whole measurement system was controlled by PC. The developed biosensor was applied for measurement of the proliferation stage of adherent cells where the sensor response depends on the ratio between healthy, viable and multiplying cells, which adhere onto the chip surface, and necrotic or apoptotic cells, which detach from the chip surface. This change in cellular adhesion caused a change in the effective permittivity in the vicinity of the sensor element, which was sensed as a change in oscillation frequency of the ring oscillator. The sensor was tested with human lung epithelial cells (BEAS-2B) during cell addition, proliferation and migration, and finally detachment induced by trypsin protease treatment. The difference in sensor response with and without cells was measured as a frequency shift in the scale of 1.1 MHz from the base frequency of 57.2 MHz. Moreover, the number of cells in the sensor vicinity was directly proportional to the frequency shift. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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Graphical abstract

20 pages, 1106 KiB  
Article
Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques
by Monika Simjanoska, Martin Gjoreski, Matjaž Gams and Ana Madevska Bogdanova
Sensors 2018, 18(4), 1160; https://doi.org/10.3390/s18041160 - 11 Apr 2018
Cited by 123 | Viewed by 12056
Abstract
Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. [...] Read more.
Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Methods: Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user. Results: Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP. Conclusion: The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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11 pages, 3437 KiB  
Article
Split-Ring Resonator Sensor Penetration Depth Assessment Using In Vivo Microwave Reflectivity and Ultrasound Measurements for Lower Extremity Trauma Rehabilitation
by Syaiful Redzwan Mohd Shah, Jacob Velander, Parul Mathur, Mauricio D. Perez, Noor Badariah Asan, Dhanesh G. Kurup, Taco J. Blokhuis and Robin Augustine
Sensors 2018, 18(2), 636; https://doi.org/10.3390/s18020636 - 21 Feb 2018
Cited by 16 | Viewed by 6456
Abstract
In recent research, microwave sensors have been used to follow up the recovery of lower extremity trauma patients. This is done mainly by monitoring the changes of dielectric properties of lower limb tissues such as skin, fat, muscle, and bone. As part of [...] Read more.
In recent research, microwave sensors have been used to follow up the recovery of lower extremity trauma patients. This is done mainly by monitoring the changes of dielectric properties of lower limb tissues such as skin, fat, muscle, and bone. As part of the characterization of the microwave sensor, it is crucial to assess the signal penetration in in vivo tissues. This work presents a new approach for investigating the penetration depth of planar microwave sensors based on the Split-Ring Resonator in the in vivo context of the femoral area. This approach is based on the optimization of a 3D simulation model using the platform of CST Microwave Studio and consisting of a sensor of the considered type and a multilayered material representing the femoral area. The geometry of the layered material is built based on information from ultrasound images and includes mainly the thicknesses of skin, fat, and muscle tissues. The optimization target is the measured S11 parameters at the sensor connector and the fitting parameters are the permittivity of each layer of the material. Four positions in the femoral area (two at distal and two at thigh) in four volunteers are considered for the in vivo study. The penetration depths are finally calculated with the help of the electric field distribution in simulations of the optimized model for each one of the 16 considered positions. The numerical results show that positions at the thigh contribute the highest penetration values of up to 17.5 mm. This finding has a high significance in planning in vitro penetration depth measurements and other tests that are going to be performed in the future. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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12 pages, 4386 KiB  
Article
Three-Dimensional Blood Vessel Model with Temperature-Indicating Function for Evaluation of Thermal Damage during Surgery
by Takeshi Hayakawa, Hisataka Maruyama, Takafumi Watanabe and Fumihito Arai
Sensors 2018, 18(2), 345; https://doi.org/10.3390/s18020345 - 25 Jan 2018
Cited by 3 | Viewed by 4487
Abstract
Surgical simulators have recently attracted attention because they enable the evaluation of the surgical skills of medical doctors and the performance of medical devices. However, thermal damage to the human body during surgery is difficult to evaluate using conventional surgical simulators. In this [...] Read more.
Surgical simulators have recently attracted attention because they enable the evaluation of the surgical skills of medical doctors and the performance of medical devices. However, thermal damage to the human body during surgery is difficult to evaluate using conventional surgical simulators. In this study, we propose a functional surgical model with a temperature-indicating function for the evaluation of thermal damage during surgery. The simulator is made of a composite material of polydimethylsiloxane and a thermochromic dye, which produces an irreversible color change as the temperature increases. Using this material, we fabricated a three-dimensional blood vessel model using the lost-wax process. We succeeded in fabricating a renal vessel model for simulation of catheter ablation. Increases in the temperature of the materials can be measured by image analysis of their color change. The maximum measurement error of the temperature was approximately −1.6 °C/+2.4 °C within the range of 60 °C to 100 °C. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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Review

Jump to: Research

18 pages, 4453 KiB  
Review
Miniaturized Bio-and Chemical-Sensors for Point-of-Care Monitoring of Chronic Kidney Diseases
by Antonio Tricoli and Giovanni Neri
Sensors 2018, 18(4), 942; https://doi.org/10.3390/s18040942 - 22 Mar 2018
Cited by 49 | Viewed by 12759
Abstract
This review reports the latest achievements in point-of-care (POC) sensor technologies for the monitoring of ammonia, creatinine and urea in patients suffering of chronic kidney diseases (CKDs). Abnormal levels of these nitrogen biomarkers are found in the physiological fluids, such as blood, urine [...] Read more.
This review reports the latest achievements in point-of-care (POC) sensor technologies for the monitoring of ammonia, creatinine and urea in patients suffering of chronic kidney diseases (CKDs). Abnormal levels of these nitrogen biomarkers are found in the physiological fluids, such as blood, urine and sweat, of CKD patients. Delocalized at-home monitoring of CKD biomarkers via integration of miniaturized, portable, and low cost chemical- and bio-sensors in POC devices, is an emerging approach to improve patients’ health monitoring and life quality. The successful monitoring of CKD biomarkers, performed on the different body fluids by means of sensors having strict requirements in term of size, cost, large-scale production capacity, response time and simple operation procedures for use in POC devices, is reported and discussed. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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