Special Issue "Latest Wearable Biosensors"

A special issue of Biosensors (ISSN 2079-6374).

Deadline for manuscript submissions: closed (15 October 2017)

Special Issue Editor

Guest Editor
Prof. Dr. Donald Y.C. Lie

Texas Tech University Health Sciences Center (TTUHSC), Texas Tech University, Lubbock, TX 79409-3102, USAx 43102, Lubbock, TX 79409-3102, USA
Website | E-Mail
Phone: +806-834-2440
Fax: +806-742-1245
Interests: Low−Power RF/Analog Integrated Circuits & System−on−a−Chip (SoC) Design and Test; Interdisciplinary Research on Medical Electronics, Biosensors, & Biosignal Processing

Special Issue Information

Dear Colleagues,

In recent years, numerous wearable biosensor systems for health monitoring have entered our daily lives and attracted a great deal of attention and discussions in the public, the scientific community, industry, and governmental agencies. The research and development of smart wearable biosensors for personalized e-Health services have, therefore, been done around the world. These wearable systems for health monitoring may consist of different types of miniature biosensors, capable of measuring physiological parameters, such as vital signs (i.e., heart rate, respiration rate, body and skin temperature, oxygen saturation, etc.), skin conductance, blood pressure, electrocardiogram (ECG), electroencephalography (EEG), acceleration, rotation, etc., and one can also use the measured data to calculate the number of steps, calories, mood changes, cardio-health, fitness conditions, posture, cognition state, sleep quality, fall detection, seizure prediction, etc., and for various monitoring purposes to improve the subjects' quality of life, health management, disease control status, and even the survival rate from emergency rescue operators. The wearable biosensors can often be connected wirelessly to a cellphone application to make them very user friendly, inexpensive, and ubiquitous. The rapid world-wide development and public embracement of these wearable biosensors (e.g., Fitbit™ and various smart watches) support the need for a dedicated Special Issue in this area. These wearable biosensors can reduce healthcare costs in global aging communities, and the continuous improvement of wearable biosensors systems can potentially transform the future of world-wide healthcare.

Prof. Dr. Donald Y.C. Lie
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access quarterly 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 350 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

  • accelerometers

  • activity

  • biosensor

  • daily

  • distribution

  • electrocardiogram (ECG)

  • electroencephalography (EEG)

  • falls

  • gait

  • machine learning

  • mobile phone

  • monitoring

  • neuroimaging

  • older

  • patterns

  • people

  • remote

  • sensor

  • skin conductance

  • sleep

  • smart phone

  • smart sensor

  • stress

  • vital signs

  • wearable

  • wearable biosensors

  • wearable sensors

Published Papers (7 papers)

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Research

Open AccessArticle Coverage of Emotion Recognition for Common Wearable Biosensors
Biosensors 2018, 8(2), 30; https://doi.org/10.3390/bios8020030
Received: 20 February 2018 / Revised: 16 March 2018 / Accepted: 22 March 2018 / Published: 24 March 2018
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Abstract
The present research proposes a novel emotion recognition framework for the computer prediction of human emotions using common wearable biosensors. Emotional perception promotes specific patterns of biological responses in the human body, and this can be sensed and used to predict emotions using
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The present research proposes a novel emotion recognition framework for the computer prediction of human emotions using common wearable biosensors. Emotional perception promotes specific patterns of biological responses in the human body, and this can be sensed and used to predict emotions using only biomedical measurements. Based on theoretical and empirical psychophysiological research, the foundation of autonomic specificity facilitates the establishment of a strong background for recognising human emotions using machine learning on physiological patterning. However, a systematic way of choosing the physiological data covering the elicited emotional responses for recognising the target emotions is not obvious. The current study demonstrates through experimental measurements the coverage of emotion recognition using common off-the-shelf wearable biosensors based on the synchronisation between audiovisual stimuli and the corresponding physiological responses. The work forms the basis of validating the hypothesis for emotional state recognition in the literature and presents coverage of the use of common wearable biosensors coupled with a novel preprocessing algorithm to demonstrate the practical prediction of the emotional states of wearers. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors)
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Open AccessFeature PaperArticle A Radar-Based Smart Sensor for Unobtrusive Elderly Monitoring in Ambient Assisted Living Applications
Biosensors 2017, 7(4), 55; https://doi.org/10.3390/bios7040055
Received: 17 October 2017 / Revised: 16 November 2017 / Accepted: 21 November 2017 / Published: 24 November 2017
Cited by 1 | PDF Full-text (15156 KB) | HTML Full-text | XML Full-text
Abstract
Continuous in-home monitoring of older adults living alone aims to improve their quality of life and independence, by detecting early signs of illness and functional decline or emergency conditions. To meet requirements for technology acceptance by seniors (unobtrusiveness, non-intrusiveness, and privacy-preservation), this study
[...] Read more.
Continuous in-home monitoring of older adults living alone aims to improve their quality of life and independence, by detecting early signs of illness and functional decline or emergency conditions. To meet requirements for technology acceptance by seniors (unobtrusiveness, non-intrusiveness, and privacy-preservation), this study presents and discusses a new smart sensor system for the detection of abnormalities during daily activities, based on ultra-wideband radar providing rich, not privacy-sensitive, information useful for sensing both cardiorespiratory and body movements, regardless of ambient lighting conditions and physical obstructions (through-wall sensing). The radar sensing is a very promising technology, enabling the measurement of vital signs and body movements at a distance, and thus meeting both requirements of unobtrusiveness and accuracy. In particular, impulse-radio ultra-wideband radar has attracted considerable attention in recent years thanks to many properties that make it useful for assisted living purposes. The proposed sensing system, evaluated in meaningful assisted living scenarios by involving 30 participants, exhibited the ability to detect vital signs, to discriminate among dangerous situations and activities of daily living, and to accommodate individual physical characteristics and habits. The reported results show that vital signs can be detected also while carrying out daily activities or after a fall event (post-fall phase), with accuracy varying according to the level of movements, reaching up to 95% and 91% in detecting respiration and heart rates, respectively. Similarly, good results were achieved in fall detection by using the micro-motion signature and unsupervised learning, with sensitivity and specificity greater than 97% and 90%, respectively. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors)
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Open AccessArticle Use of Wavelet Transform to Detect Compensated and Decompensated Stages in the Congestive Heart Failure Patient
Biosensors 2017, 7(3), 40; https://doi.org/10.3390/bios7030040
Received: 24 August 2017 / Revised: 12 September 2017 / Accepted: 16 September 2017 / Published: 20 September 2017
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Abstract
This research work is aimed at improving health care, reducing cost, and the occurrence of emergency hospitalization in patients with Congestive Heart Failure (CHF) by analyzing heart and lung sounds to distinguish between the compensated and decompensated states. Compensated state defines stable state
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This research work is aimed at improving health care, reducing cost, and the occurrence of emergency hospitalization in patients with Congestive Heart Failure (CHF) by analyzing heart and lung sounds to distinguish between the compensated and decompensated states. Compensated state defines stable state of the patient but with lack of retention of fluids in lungs, whereas decompensated state leads to unstable state of the patient with lots of fluid retention in the lungs, where the patient needs medication. Acoustic signals from the heart and the lung were analyzed using wavelet transforms to measure changes in the CHF patient’s status from the decompensated to compensated and vice versa. Measurements were taken on CHF patients diagnosed to be in compensated and decompensated states by using a digital stethoscope and electrocardiogram (ECG) in order to monitor their progress in the management of their disease. Analysis of acoustic signals of the heart due to the opening and closing of heart valves as well as the acoustic signals of the lungs due to respiration and the ECG signals are presented. Fourier, short-time Fourier, and wavelet transforms are evaluated to determine the best method to detect shifts in the status of a CHF patient. The power spectra obtained through the Fourier transform produced results that differentiate the signals from healthy people and CHF patients, while the short-time Fourier transform (STFT) technique did not provide the desired results. The most promising results were obtained by using wavelet analysis. Wavelet transforms provide better resolution, in time, for higher frequencies, and a better resolution, in frequency, for lower frequencies. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors)
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Open AccessArticle Design and Development of Non-Contact Bio-Potential Electrodes for Pervasive Health Monitoring Applications
Biosensors 2017, 7(1), 2; https://doi.org/10.3390/bios7010002
Received: 26 July 2016 / Revised: 7 November 2016 / Accepted: 14 November 2016 / Published: 1 January 2017
Cited by 1 | PDF Full-text (1115 KB) | HTML Full-text | XML Full-text
Abstract
For the advent of pervasive bio-potential monitoring, it will be necessary to utilize a combination of cheap, quick to apply, low-noise electrodes and compact electronics with wireless technologies. Once available, all electrical activity resulting from the processes of the human body could be
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For the advent of pervasive bio-potential monitoring, it will be necessary to utilize a combination of cheap, quick to apply, low-noise electrodes and compact electronics with wireless technologies. Once available, all electrical activity resulting from the processes of the human body could be actively and constantly monitored without the need for cumbersome application and maintenance. This could significantly improve the early diagnosis of a range of different conditions in high-risk individuals, opening the possibility for new treatments and interventions as conditions develop. This paper presents the design and implementation of compact, non-contact capacitive bio-potential electrodes utilising a low impedance current-to-voltage configuration and a bootstrapped voltage follower, demonstrating results applicable to research applications for capacitive electrocardiography and capacitive electromyography. The presented electrodes use few components, have a small surface area and are capable of acquiring a range of bio-potential signals. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors)
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Open AccessArticle Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor
Biosensors 2016, 6(4), 58; https://doi.org/10.3390/bios6040058
Received: 1 September 2016 / Revised: 14 November 2016 / Accepted: 17 November 2016 / Published: 29 November 2016
Cited by 2 | PDF Full-text (8670 KB) | HTML Full-text | XML Full-text
Abstract
Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU)
[...] Read more.
Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors)
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Open AccessArticle Wrist Pulse Rate Monitor Using Self-Injection-Locked Radar Technology
Biosensors 2016, 6(4), 54; https://doi.org/10.3390/bios6040054
Received: 1 September 2016 / Revised: 7 October 2016 / Accepted: 19 October 2016 / Published: 26 October 2016
Cited by 2 | PDF Full-text (3263 KB) | HTML Full-text | XML Full-text
Abstract
To achieve sensitivity, comfort, and durability in vital sign monitoring, this study explores the use of radar technologies in wearable devices. The study first detected the respiratory rates and heart rates of a subject at a one-meter distance using a self-injection-locked (SIL) radar
[...] Read more.
To achieve sensitivity, comfort, and durability in vital sign monitoring, this study explores the use of radar technologies in wearable devices. The study first detected the respiratory rates and heart rates of a subject at a one-meter distance using a self-injection-locked (SIL) radar and a conventional continuous-wave (CW) radar to compare the sensitivity versus power consumption between the two radars. Then, a pulse rate monitor was constructed based on a bistatic SIL radar architecture. This monitor uses an active antenna that is composed of a SIL oscillator (SILO) and a patch antenna. When attached to a band worn on the subject’s wrist, the active antenna can monitor the pulse on the subject’s wrist by modulating the SILO with the associated Doppler signal. Subsequently, the SILO’s output signal is received and demodulated by a remote frequency discriminator to obtain the pulse rate information. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors)
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Open AccessArticle Lactate Sensors on Flexible Substrates
Biosensors 2016, 6(3), 48; https://doi.org/10.3390/bios6030048
Received: 26 July 2016 / Revised: 1 September 2016 / Accepted: 18 September 2016 / Published: 21 September 2016
Cited by 2 | PDF Full-text (5948 KB) | HTML Full-text | XML Full-text
Abstract
Lactate detection by an in situ sensor is of great need in clinical medicine, food processing, and athletic performance monitoring. In this paper, a flexible, easy to fabricate, and low-cost biosensor base on lactate oxidase is presented. The fabrication processes, including metal deposition,
[...] Read more.
Lactate detection by an in situ sensor is of great need in clinical medicine, food processing, and athletic performance monitoring. In this paper, a flexible, easy to fabricate, and low-cost biosensor base on lactate oxidase is presented. The fabrication processes, including metal deposition, sol-gel IrOx deposition, and drop-dry enzyme loading method, are described in detail. The loaded enzyme was examined by scanning electron microscopy. Cyclic voltammetry was used to characterize the sensors. Durability, sensibility, and selectivity of the biosensors were examined. The comparison for different electrode sizes and different sensing film materials was conducted. The sensor could last for four weeks with an average surface area normalized sensitivity of 950 nA/(cm2 mM) and 9250 nA/(cm2 mM) for Au-based electrodes, and IrOx-modified electrodes respectively, both with an electrode size of 100 × 50 μm. The self-referencing method to record noises simultaneously with the working electrode greatly improved sensor sensitivity and selectivity. The sensor showed little response to interference chemicals, such as glutamate and dopamine. Full article
(This article belongs to the Special Issue Latest Wearable Biosensors)
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