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Special Issue "Biomedical Sensors and Systems"

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (15 April 2014)

Special Issue Editor

Guest Editor
Prof. Dr. Wan-Young Chung (Website)

Department of Electronic Engineering, Pukyong National University, Busan 608-737, South Korea
Interests: biomedical sensors; biomedical signal processing; medical equipment; wireless sensor network; driver drowsiness detection; e-healthcare; BioChemLab-on-a-chip; wireless biomedical sensors

Special Issue Information

Dear Colleagues,

Recently a wide variety of biomedical sensors such as fluid flow sensors, ultrasound sensors, chemical analysis sensor, biomaterial-based sensor, 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, excellent fit, and reliable. 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 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 high rated manuscripts addressing the development of biomedical sensors and system.

Prof. Dr. Wan-Young Chung
Guest Editor

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 monthly 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 1800 CHF (Swiss Francs).


Published Papers (46 papers)

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Open AccessArticle A Real-Time Localization System for an Endoscopic Capsule Using Magnetic Sensors
Sensors 2014, 14(11), 20910-20929; doi:10.3390/s141120910
Received: 13 June 2014 / Revised: 23 August 2014 / Accepted: 22 September 2014 / Published: 5 November 2014
Cited by 5 | PDF Full-text (2036 KB) | HTML Full-text | XML Full-text
Abstract
Magnetic sensing technology offers an attractive alternative for in vivo tracking with much better performance than RF and ultrasound technologies. In this paper, an efficient in vivo magnetic tracking system is presented. The proposed system is intended to localize an endoscopic capsule [...] Read more.
Magnetic sensing technology offers an attractive alternative for in vivo tracking with much better performance than RF and ultrasound technologies. In this paper, an efficient in vivo magnetic tracking system is presented. The proposed system is intended to localize an endoscopic capsule which delivers biomarkers around specific locations of the gastrointestinal (GI) tract. For efficiently localizing a magnetic marker inside the capsule, a mathematical model has been developed for the magnetic field around a cylindrical magnet and used with a localization algorithm that provides minimum error and fast computation. The proposed tracking system has much reduced complexity compared to the ones reported in the literature to date. Laboratory tests and in vivo animal trials have demonstrated the suitability of the proposed system for tracking a magnetic marker with expected accuracy. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Toward Real-Time Automated Detection of Turns during Gait Using Wearable Inertial Measurement Units
Sensors 2014, 14(10), 18800-18822; doi:10.3390/s141018800
Received: 14 April 2014 / Revised: 28 July 2014 / Accepted: 28 September 2014 / Published: 10 October 2014
Cited by 5 | PDF Full-text (3153 KB) | HTML Full-text | XML Full-text
Abstract
Previous studies have presented algorithms for detection of turns during gait using wearable sensors, but those algorithms were not built for real-time use. This paper therefore investigates the optimal approach for real-time detection of planned turns during gait using wearable inertial measurement [...] Read more.
Previous studies have presented algorithms for detection of turns during gait using wearable sensors, but those algorithms were not built for real-time use. This paper therefore investigates the optimal approach for real-time detection of planned turns during gait using wearable inertial measurement units. Several different sensor positions (head, back and legs) and three different detection criteria (orientation, angular velocity and both) are compared with regard to their ability to correctly detect turn onset. Furthermore, the different sensor positions are compared with regard to their ability to predict the turn direction and amplitude. The evaluation was performed on ten healthy subjects who performed left/right turns at three amplitudes (22, 45 and 90 degrees). Results showed that turn onset can be most accurately detected with sensors on the back and using a combination of orientation and angular velocity. The same setup also gives the best prediction of turn direction and amplitude. Preliminary measurements with a single amputee were also performed and highlighted important differences such as slower turning that need to be taken into account. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle Correlation of X-Ray Computed Tomography with Quantitative Nuclear Magnetic Resonance Methods for Pre-Clinical Measurement of Adipose and Lean Tissues in Living Mice
Sensors 2014, 14(10), 18526-18542; doi:10.3390/s141018526
Received: 23 May 2014 / Revised: 30 July 2014 / Accepted: 15 August 2014 / Published: 8 October 2014
Cited by 2 | PDF Full-text (1075 KB) | HTML Full-text | XML Full-text
Abstract
Numerous obesity studies have coupled murine models with non-invasive methods to quantify body composition in longitudinal experiments, including X-ray computed tomography (CT) or quantitative nuclear magnetic resonance (QMR). Both microCT and QMR have been separately validated with invasive techniques of adipose tissue [...] Read more.
Numerous obesity studies have coupled murine models with non-invasive methods to quantify body composition in longitudinal experiments, including X-ray computed tomography (CT) or quantitative nuclear magnetic resonance (QMR). Both microCT and QMR have been separately validated with invasive techniques of adipose tissue quantification, like post-mortem fat extraction and measurement. Here we report a head-to-head study of both protocols using oil phantoms and mouse populations to determine the parameters that best align CT data with that from QMR. First, an in vitro analysis of oil/water mixtures was used to calibrate and assess the overall accuracy of microCT vs. QMR data. Next, experiments were conducted with two cohorts of living mice (either homogenous or heterogeneous by sex, age and genetic backgrounds) to assess the microCT imaging technique for adipose tissue segmentation and quantification relative to QMR. Adipose mass values were obtained from microCT data with three different resolutions, after which the data were analyzed with different filter and segmentation settings. Strong linearity was noted between the adipose mass values obtained with microCT and QMR, with optimal parameters and scan conditions reported herein. Lean tissue (muscle, internal organs) was also segmented and quantified using the microCT method relative to the analogous QMR values. Overall, the rigorous calibration and validation of the microCT method for murine body composition, relative to QMR, ensures its validity for segmentation, quantification and visualization of both adipose and lean tissues. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Whole Body Center of Mass Estimation with Portable Sensors: Using the Statically Equivalent Serial Chain and a Kinect
Sensors 2014, 14(9), 16955-16971; doi:10.3390/s140916955
Received: 16 April 2014 / Revised: 5 August 2014 / Accepted: 7 August 2014 / Published: 11 September 2014
Cited by 7 | PDF Full-text (829 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The trajectory of the whole body center of mass (CoM) is useful as a reliable metric of postural stability. If the evaluation of a subject-specific CoM were available outside of the laboratory environment, it would improve the assessment of the effects of [...] Read more.
The trajectory of the whole body center of mass (CoM) is useful as a reliable metric of postural stability. If the evaluation of a subject-specific CoM were available outside of the laboratory environment, it would improve the assessment of the effects of physical rehabilitation. This paper develops a method that enables tracking CoM position using low-cost sensors that can be moved around by a therapist or easily installed inside a patient’s home. Here, we compare the accuracy of a personalized CoM estimation using the statically equivalent serial chain (SESC) method and measurements obtained with the Kinect to the case of a SESC obtained with high-end equipment (Vicon). We also compare these estimates to literature-based ones for both sensors. The method was validated with seven able-bodied volunteers for whom the SESC was identified using 40 static postures. The literature-based estimation with Vicon measurements had a average error 24.9 ± 3.7 mm; this error was reduced to 12.8 ± 9.1 mm with the SESC identification. When using Kinect measurements, the literature-based estimate had an error of 118.4 ± 50.0 mm, while the SESC error was 26.6 ± 6.0 mm. The subject-specific SESC estimate using low-cost sensors has an equivalent performance as the literature-based one with high-end sensors. The SESC method can improve CoM estimation of elderly and neurologically impaired subjects by considering variations in their mass distribution. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle Development of an Air Pneumatic Suspension System for Transtibial Prostheses
Sensors 2014, 14(9), 16754-16765; doi:10.3390/s140916754
Received: 24 April 2014 / Revised: 25 July 2014 / Accepted: 28 July 2014 / Published: 9 September 2014
Cited by 1 | PDF Full-text (1578 KB) | HTML Full-text | XML Full-text
Abstract
The suspension system and socket fitting of artificial limbs have major roles and vital effects on the comfort, mobility, and satisfaction of amputees. This paper introduces a new pneumatic suspension system that overcomes the drawbacks of current suspension systems in donning and [...] Read more.
The suspension system and socket fitting of artificial limbs have major roles and vital effects on the comfort, mobility, and satisfaction of amputees. This paper introduces a new pneumatic suspension system that overcomes the drawbacks of current suspension systems in donning and doffing, change in volume during daily activities, and pressure distribution in the socket-stump interface. An air pneumatic suspension system (APSS) for total-contact sockets was designed and developed. Pistoning and pressure distribution in the socket-stump interface were tested for the new APSS. More than 95% of the area between each prosthetic socket and liner was measured using a Tekscan F-Scan pressure measurement which has developed matrix-based pressure sensing systems. The variance in pressure around the stump was 8.76 kPa. APSS exhibits less pressure concentration around the stump, improved pressure distribution, easy donning and doffing, adjustability to remain fitted to the socket during daily activities, and more adaptability to the changes in stump volume. The volume changes were adjusted by utility of air pressure sensor. The vertical displacement point and reliability of suspension were assessed using a photographic method. The optimum pressure in every level of loading weight was 55 kPa, and the maximum displacement was 6 mm when 90 N of weight was loaded. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network
Sensors 2014, 14(9), 16212-16234; doi:10.3390/s140916212
Received: 13 June 2014 / Revised: 5 August 2014 / Accepted: 26 August 2014 / Published: 2 September 2014
Cited by 11 | PDF Full-text (1854 KB) | HTML Full-text | XML Full-text
Abstract
In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden [...] Read more.
In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM) applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs) during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98) for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95) when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle Lab-on-Chip Cytometry Based on Magnetoresistive Sensors for Bacteria Detection in Milk
Sensors 2014, 14(8), 15496-15524; doi:10.3390/s140815496
Received: 23 May 2014 / Revised: 14 July 2014 / Accepted: 1 August 2014 / Published: 21 August 2014
Cited by 11 | PDF Full-text (4767 KB) | HTML Full-text | XML Full-text
Abstract
Flow cytometers have been optimized for use in portable platforms, where cell separation, identification and counting can be achieved in a compact and modular format. This feature can be combined with magnetic detection, where magnetoresistive sensors can be integrated within microfluidic channels [...] Read more.
Flow cytometers have been optimized for use in portable platforms, where cell separation, identification and counting can be achieved in a compact and modular format. This feature can be combined with magnetic detection, where magnetoresistive sensors can be integrated within microfluidic channels to detect magnetically labelled cells. This work describes a platform for in-flow detection of magnetically labelled cells with a magneto-resistive based cell cytometer. In particular, we present an example for the validation of the platform as a magnetic counter that identifies and quantifies Streptococcus agalactiae in milk. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Flexible Capacitive Electrodes for Minimizing Motion Artifacts in Ambulatory Electrocardiograms
Sensors 2014, 14(8), 14732-14743; doi:10.3390/s140814732
Received: 24 June 2014 / Revised: 5 August 2014 / Accepted: 7 August 2014 / Published: 12 August 2014
Cited by 13 | PDF Full-text (1183 KB) | HTML Full-text | XML Full-text
Abstract
This study proposes the use of flexible capacitive electrodes for reducing motion artifacts in a wearable electrocardiogram (ECG) device. The capacitive electrodes have conductive foam on their surface, a shield, an optimal input bias resistor, and guarding feedback. The electrodes are integrated [...] Read more.
This study proposes the use of flexible capacitive electrodes for reducing motion artifacts in a wearable electrocardiogram (ECG) device. The capacitive electrodes have conductive foam on their surface, a shield, an optimal input bias resistor, and guarding feedback. The electrodes are integrated in a chest belt, and the acquired signals are transmitted wirelessly for ambulatory heart rate monitoring. We experimentally validated the electrode performance with subjects standing and walking on a treadmill at speeds of up to 7 km/h. The results confirmed the highly accurate heart rate detection capacity of the developed system and its feasibility for daily-life ECG monitoring. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Development of a Modularized Seating System to Actively Manage Interface Pressure
Sensors 2014, 14(8), 14235-14252; doi:10.3390/s140814235
Received: 14 May 2014 / Revised: 6 July 2014 / Accepted: 28 July 2014 / Published: 5 August 2014
PDF Full-text (2257 KB) | HTML Full-text | XML Full-text
Abstract
Pressure ulcers can be a fatal complication. Many immobile wheelchair users face this threat. Current passive and active cushions do reduce the incidence of pressure ulcers and they have different merits. We proposed an active approach to combine their advantages which is [...] Read more.
Pressure ulcers can be a fatal complication. Many immobile wheelchair users face this threat. Current passive and active cushions do reduce the incidence of pressure ulcers and they have different merits. We proposed an active approach to combine their advantages which is based on the concept that the interface pressure can be changed with different supporting shapes. The purpose of this paper is to verify the proposed approach. With practical applications in mind, we have developed a modular system whose support surface is composed by height-adjustable support elements. Each four-element module was self-contained and composed of force sensors, position sensors, linear actuators, signal conditioners, driving circuits, and signal processors. The modules could be chained and assembled together easily to form different-sized support surfaces. Each support element took up a 3 cm × 3 cm supporting area. The displacement resolution was less than 0.1 mm and the force sensor error was less than 1% in the 2000 g range. Each support element of the system could provide 49 N pushing force (408 mmHg over the 3 cm × 3 cm area) at a speed of 2.36 mm/s. Several verification tests were performed to assess the whole system’s feasibility. Further improvements and clinical applications were discussed. In conclusion, this modularized system is capable of actively managing interface pressure in real time. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle Tracheal Sounds Acquisition Using Smartphones
Sensors 2014, 14(8), 13830-13850; doi:10.3390/s140813830
Received: 12 June 2014 / Revised: 22 July 2014 / Accepted: 25 July 2014 / Published: 30 July 2014
Cited by 3 | PDF Full-text (2802 KB) | HTML Full-text | XML Full-text
Abstract
Tracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly [...] Read more.
Tracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly from tracheal sounds acquired from an acoustic microphone connected to a smartphone. We employed the Samsung Galaxy S4 and iPhone 4s smartphones to acquire tracheal sounds from N = 9 healthy volunteers at airflows ranging from 0.5 to 2.5 L/s. We found that the amplitude of the smartphone-acquired sounds was highly correlated with the airflow from a spirometer, and similar to previously-published studies, we found that the increasing tracheal sounds’ amplitude as flow increases follows a power law relationship. Acquired tracheal sounds were used for breath-phase onset detection and their onsets differed by only 52 ± 51 ms (mean ± SD) for Galaxy S4, and 51 ± 48 ms for iPhone 4s, when compared to those detected from the reference signal via the spirometer. Moreover, it was found that accurate respiratory rates (RR) can be obtained from tracheal sounds. The correlation index, bias and limits of agreement were r2 = 0.9693, 0.11 (−1.41 to 1.63) breaths-per-minute (bpm) for Galaxy S4, and r2 = 0.9672, 0.097 (–1.38 to 1.57) bpm for iPhone 4s, when compared to RR estimated from spirometry. Both smartphone devices performed similarly, as no statistically-significant differences were found. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine
Sensors 2014, 14(8), 13361-13388; doi:10.3390/s140813361
Received: 29 April 2014 / Revised: 11 July 2014 / Accepted: 18 July 2014 / Published: 24 July 2014
Cited by 3 | PDF Full-text (4788 KB) | HTML Full-text | XML Full-text
Abstract
Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this [...] Read more.
Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Reduction of the Dimensionality of the EEG Channels during Scoliosis Correction Surgeries Using a Wavelet Decomposition Technique
Sensors 2014, 14(7), 13046-13069; doi:10.3390/s140713046
Received: 20 March 2014 / Revised: 23 June 2014 / Accepted: 4 July 2014 / Published: 21 July 2014
Cited by 2 | PDF Full-text (1696 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete [...] Read more.
This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria’s value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) (the Medical center of National University of Malaysia). The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle An Optical Fibre-Based Sensor for Respiratory Monitoring
Sensors 2014, 14(7), 13088-13101; doi:10.3390/s140713088
Received: 29 April 2014 / Revised: 29 April 2014 / Accepted: 15 July 2014 / Published: 21 July 2014
Cited by 10 | PDF Full-text (1031 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a textile-based respiratory sensing system is presented. Highly flexible polymeric optical fibres (POFs) that react to applied pressure were integrated into a carrier fabric to form a wearable sensing system. After the evaluation of different optical fibres, different setups [...] Read more.
In this paper, a textile-based respiratory sensing system is presented. Highly flexible polymeric optical fibres (POFs) that react to applied pressure were integrated into a carrier fabric to form a wearable sensing system. After the evaluation of different optical fibres, different setups were compared. To demonstrate the feasibility of such a wearable sensor, the setup featuring the best performance was placed on the human torso, and thus it was possible to measure the respiratory rate. Furthermore, we show that such a wearable system enables to keep track of the way of breathing (diaphragmatic, upper costal and mixed) when the sensor is placed at different positions of the torso. A comparison of the results with the output of some commercial respiratory measurements devices confirmed the utility of such a monitoring device. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine
Sensors 2014, 14(7), 12784-12802; doi:10.3390/s140712784
Received: 19 February 2014 / Revised: 20 June 2014 / Accepted: 10 July 2014 / Published: 17 July 2014
Cited by 7 | PDF Full-text (865 KB) | HTML Full-text | XML Full-text
Abstract
In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three [...] Read more.
In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Implementation of a Data Packet Generator Using Pattern Matching for Wearable ECG Monitoring Systems
Sensors 2014, 14(7), 12623-12639; doi:10.3390/s140712623
Received: 15 April 2014 / Revised: 22 June 2014 / Accepted: 11 July 2014 / Published: 15 July 2014
Cited by 5 | PDF Full-text (1020 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a packet generator using a pattern matching algorithm for real-time abnormal heartbeat detection is proposed. The packet generator creates a very small data packet which conveys sufficient crucial information for health condition analysis. The data packet envelopes real time [...] Read more.
In this paper, a packet generator using a pattern matching algorithm for real-time abnormal heartbeat detection is proposed. The packet generator creates a very small data packet which conveys sufficient crucial information for health condition analysis. The data packet envelopes real time ECG signals and transmits them to a smartphone via Bluetooth. An Android application was developed specifically to decode the packet and extract ECG information for health condition analysis. Several graphical presentations are displayed and shown on the smartphone. We evaluate the performance of abnormal heartbeat detection accuracy using the MIT/BIH Arrhythmia Database and real time experiments. The experimental result confirm our finding that abnormal heart beat detection is practically possible. We also performed data compression ratio and signal restoration performance evaluations to establish the usefulness of the proposed packet generator and the results were excellent. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Correlation Networks for Identifying Changes in Brain Connectivity during Epileptiform Discharges and Transcranial Magnetic Stimulation
Sensors 2014, 14(7), 12585-12597; doi:10.3390/s140712585
Received: 23 April 2014 / Revised: 7 July 2014 / Accepted: 10 July 2014 / Published: 14 July 2014
Cited by 7 | PDF Full-text (958 KB) | HTML Full-text | XML Full-text
Abstract
The occurrence of epileptiform discharges (ED) in electroencephalographic (EEG) recordings of patients with epilepsy signifies a change in brain dynamics and particularly brain connectivity. Transcranial magnetic stimulation (TMS) has been recently acknowledged as a non-invasive brain stimulation technique that can be used [...] Read more.
The occurrence of epileptiform discharges (ED) in electroencephalographic (EEG) recordings of patients with epilepsy signifies a change in brain dynamics and particularly brain connectivity. Transcranial magnetic stimulation (TMS) has been recently acknowledged as a non-invasive brain stimulation technique that can be used in focal epilepsy for therapeutic purposes. In this case study, it is investigated whether simple time-domain connectivity measures, namely cross-correlation and partial cross-correlation, can detect alterations in the connectivity structure estimated from selected EEG channels before and during ED, as well as how this changes with the application of TMS. The correlation for each channel pair is computed on non-overlapping windows of 1 s duration forming weighted networks. Further, binary networks are derived by thresholding or statistical significance tests (parametric and randomization tests). The information for the binary networks is summarized by statistical network measures, such as the average degree and the average path length. Alterations of brain connectivity before, during and after ED with or without TMS are identified by statistical analysis of the network measures at each state. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Bidirectional and Multi-User Telerehabilitation System: Clinical Effect on Balance, Functional Activity, and Satisfaction in Patients with Chronic Stroke Living in Long-Term Care Facilities
Sensors 2014, 14(7), 12451-12466; doi:10.3390/s140712451
Received: 17 March 2014 / Revised: 12 June 2014 / Accepted: 7 July 2014 / Published: 11 July 2014
Cited by 2 | PDF Full-text (572 KB) | HTML Full-text | XML Full-text
Abstract
Background: The application of internet technology for telerehabilitation in patients with stroke has developed rapidly. Objective: The current study aimed to evaluate the effect of a bidirectional and multi-user telerehabilitation system on balance and satisfaction in patients with chronic stroke [...] Read more.
Background: The application of internet technology for telerehabilitation in patients with stroke has developed rapidly. Objective: The current study aimed to evaluate the effect of a bidirectional and multi-user telerehabilitation system on balance and satisfaction in patients with chronic stroke living in long-term care facilities (LTCFs). Method: This pilot study used a multi-site, blocked randomization design. Twenty-four participants from three LTCFs were recruited, and the participants were randomly assigned into the telerehabilitation (Tele) and conventional therapy (Conv) groups within each LTCF. Tele group received telerehabilitation but the Conv group received conventional therapy with two persons in each group for three sessions per week and for four weeks. The outcome measures included Berg Balance Scale (BBS), Barthel Index (BI), and the telerehabilitation satisfaction of the participants. Setting: A telerehabilitation system included “therapist end” in a laboratory, and the “client end” in LTCFs. The conventional therapy was conducted in LTCFs. Results: Training programs conducted for both the Tele and Conv groups showed significant effects within groups on the participant BBS as well as the total and self-care scores of BI. No significant difference between groups could be demonstrated. The satisfaction of participants between the Tele and the Conv groups also did not show significant difference. Conclusions: This pilot study indicated that the multi-user telerehabilitation program is feasible for improving the balance and functional activity similar to conventional therapy in patients with chronic stroke living in LTCFs. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle In Vitro Evaluation of Fluorescence Glucose Biosensor Response
Sensors 2014, 14(7), 12127-12148; doi:10.3390/s140712127
Received: 6 March 2014 / Revised: 10 June 2014 / Accepted: 3 July 2014 / Published: 8 July 2014
Cited by 5 | PDF Full-text (1650 KB) | HTML Full-text | XML Full-text
Abstract
Rapid, accurate, and minimally-invasive glucose biosensors based on Förster Resonance Energy Transfer (FRET) for glucose measurement have the potential to enhance diabetes control. However, a standard set of in vitro approaches for evaluating optical glucose biosensor response under controlled conditions would facilitate [...] Read more.
Rapid, accurate, and minimally-invasive glucose biosensors based on Förster Resonance Energy Transfer (FRET) for glucose measurement have the potential to enhance diabetes control. However, a standard set of in vitro approaches for evaluating optical glucose biosensor response under controlled conditions would facilitate technological innovation and clinical translation. Towards this end, we have identified key characteristics and response test methods, fabricated FRET-based glucose biosensors, and characterized biosensor performance using these test methods. The biosensors were based on competitive binding between dextran and glucose to concanavalin A and incorporated long-wavelength fluorescence dye pairs. Testing characteristics included spectral response, linearity, sensitivity, limit of detection, kinetic response, reversibility, stability, precision, and accuracy. The biosensor demonstrated a fluorescence change of 45% in the presence of 400 mg/dL glucose, a mean absolute relative difference of less than 11%, a limit of detection of 25 mg/dL, a response time of 15 min, and a decay in fluorescence intensity of 72% over 30 days. The battery of tests presented here for objective, quantitative in vitro evaluation of FRET glucose biosensors performance have the potential to form the basis of future consensus standards. By implementing these test methods for a long-visible-wavelength biosensor, we were able to demonstrate strengths and weaknesses with a new level of thoroughness and rigor. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle Smartphone-Based Hearing Screening in Noisy Environments
Sensors 2014, 14(6), 10346-10360; doi:10.3390/s140610346
Received: 31 March 2014 / Revised: 5 June 2014 / Accepted: 10 June 2014 / Published: 12 June 2014
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Abstract
It is important and recommended to detect hearing loss as soon as possible. If it is found early, proper treatment may help improve hearing and reduce the negative consequences of hearing loss. In this study, we developed smartphone-based hearing screening methods that [...] Read more.
It is important and recommended to detect hearing loss as soon as possible. If it is found early, proper treatment may help improve hearing and reduce the negative consequences of hearing loss. In this study, we developed smartphone-based hearing screening methods that can ubiquitously test hearing. However, environmental noise generally results in the loss of ear sensitivity, which causes a hearing threshold shift (HTS). To overcome this limitation in the hearing screening location, we developed a correction algorithm to reduce the HTS effect. A built-in microphone and headphone were calibrated to provide the standard units of measure. The HTSs in the presence of either white or babble noise were systematically investigated to determine the mean HTS as a function of noise level. When the hearing screening application runs, the smartphone automatically measures the environmental noise and provides the HTS value to correct the hearing threshold. A comparison to pure tone audiometry shows that this hearing screening method in the presence of noise could closely estimate the hearing threshold. We expect that the proposed ubiquitous hearing test method could be used as a simple hearing screening tool and could alert the user if they suffer from hearing loss. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Novel Pseudo-Wavelet Function for MMG Signal Extraction during Dynamic Fatiguing Contractions
Sensors 2014, 14(6), 9489-9504; doi:10.3390/s140609489
Received: 25 March 2014 / Revised: 1 May 2014 / Accepted: 19 May 2014 / Published: 28 May 2014
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Abstract
The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatigue. For training and testing purposes, the [...] Read more.
The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatigue. For training and testing purposes, the signals were labeled in two classes (Non-Fatigue and Fatigue). A genetic algorithm was used to evolve a pseudo-wavelet function for optimizing the detection of muscle fatigue. Tuning of the generalized evolved pseudo-wavelet function was based on the decomposition of 70% of the conducted MMG trials. After completing 25 independent pseudo-wavelet evolution runs, the best run was selected and then tested on the remaining 30% of the data to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 16.61 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0:05). Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Design, Development and Testing of a Low-Cost sEMG System and Its Use in Recording Muscle Activity in Human Gait
Sensors 2014, 14(5), 8235-8258; doi:10.3390/s140508235
Received: 19 February 2014 / Revised: 29 April 2014 / Accepted: 29 April 2014 / Published: 7 May 2014
Cited by 7 | PDF Full-text (1009 KB) | HTML Full-text | XML Full-text | Correction
Abstract
Surface electromyography (sEMG) is an important measurement technique used in biomechanical, rehabilitation and sport environments. In this article the design, development and testing of a low-cost wearable sEMG system are described. The hardware architecture consists of a two-cascade small-sized bioamplifier with a [...] Read more.
Surface electromyography (sEMG) is an important measurement technique used in biomechanical, rehabilitation and sport environments. In this article the design, development and testing of a low-cost wearable sEMG system are described. The hardware architecture consists of a two-cascade small-sized bioamplifier with a total gain of 2,000 and band-pass of 3 to 500 Hz. The sampling frequency of the system is 1,000 Hz. Since real measured EMG signals are usually corrupted by various types of noises (motion artifacts, white noise and electromagnetic noise present at 50 Hz and higher harmonics), we have tested several denoising techniques, both on artificial and measured EMG signals. Results showed that a wavelet—based technique implementing Daubechies5 wavelet and soft sqtwolog thresholding is the most appropriate for EMG signals denoising. To test the system performance, EMG activities of six dominant muscles of ten healthy subjects during gait were measured (gluteus maximus, biceps femoris, sartorius, rectus femoris, tibialis anterior and medial gastrocnemius). The obtained EMG envelopes presented against the duration of gait cycle were compared favourably with the EMG data available in the literature, suggesting that the proposed system is suitable for a wide range of applications in biomechanics. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Human Movement Detection and Identification Using Pyroelectric Infrared Sensors
Sensors 2014, 14(5), 8057-8081; doi:10.3390/s140508057
Received: 26 February 2014 / Revised: 11 April 2014 / Accepted: 24 April 2014 / Published: 5 May 2014
Cited by 13 | PDF Full-text (5600 KB) | HTML Full-text | XML Full-text
Abstract
Pyroelectric infrared (PIR) sensors are widely used as a presence trigger, but the analog output of PIR sensors depends on several other aspects, including the distance of the body from the PIR sensor, the direction and speed of movement, the body shape [...] Read more.
Pyroelectric infrared (PIR) sensors are widely used as a presence trigger, but the analog output of PIR sensors depends on several other aspects, including the distance of the body from the PIR sensor, the direction and speed of movement, the body shape and gait. In this paper, we present an empirical study of human movement detection and identification using a set of PIR sensors. We have developed a data collection module having two pairs of PIR sensors orthogonally aligned and modified Fresnel lenses. We have placed three PIR-based modules in a hallway for monitoring people; one module on the ceiling; two modules on opposite walls facing each other. We have collected a data set from eight subjects when walking in three different conditions: two directions (back and forth), three distance intervals (close to one wall sensor, in the middle, close to the other wall sensor) and three speed levels (slow, moderate, fast). We have used two types of feature sets: a raw data set and a reduced feature set composed of amplitude and time to peaks; and passage duration extracted from each PIR sensor. We have performed classification analysis with well-known machine learning algorithms, including instance-based learning and support vector machine. Our findings show that with the raw data set captured from a single PIR sensor of each of the three modules, we could achieve more than 92% accuracy in classifying the direction and speed of movement, the distance interval and identifying subjects. We could also achieve more than 94% accuracy in classifying the direction, speed and distance and identifying subjects using the reduced feature set extracted from two pairs of PIR sensors of each of the three modules. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Calibration-Free Pulse Oximetry Based on Two Wavelengths in the Infrared — A Preliminary Study
Sensors 2014, 14(4), 7420-7434; doi:10.3390/s140407420
Received: 23 January 2014 / Revised: 9 April 2014 / Accepted: 14 April 2014 / Published: 23 April 2014
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Abstract
The assessment of oxygen saturation in arterial blood by pulse oximetry (SpO2) is based on the different light absorption spectra for oxygenated and deoxygenated hemoglobin and the analysis of photoplethysmographic (PPG) signals acquired at two wavelengths. Commercial pulse oximeters use [...] Read more.
The assessment of oxygen saturation in arterial blood by pulse oximetry (SpO2) is based on the different light absorption spectra for oxygenated and deoxygenated hemoglobin and the analysis of photoplethysmographic (PPG) signals acquired at two wavelengths. Commercial pulse oximeters use two wavelengths in the red and infrared regions which have different pathlengths and the relationship between the PPG-derived parameters and oxygen saturation in arterial blood is determined by means of an empirical calibration. This calibration results in an inherent error, and pulse oximetry thus has an error of about 4%, which is too high for some clinical problems. We present calibration-free pulse oximetry for measurement of SpO2, based on PPG pulses of two nearby wavelengths in the infrared. By neglecting the difference between the path-lengths of the two nearby wavelengths, SpO2 can be derived from the PPG parameters with no need for calibration. In the current study we used three laser diodes of wavelengths 780, 785 and 808 nm, with narrow spectral line-width. SaO2 was calculated by using each pair of PPG signals selected from the three wavelengths. In measurements on healthy subjects, SpO2 values, obtained by the 780–808 nm wavelength pair were found to be in the normal range. The measurement of SpO2 by two nearby wavelengths in the infrared with narrow line-width enables the assessment of SpO2 without calibration. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle An Upper-Limb Power-Assist Exoskeleton Using Proportional Myoelectric Control
Sensors 2014, 14(4), 6677-6694; doi:10.3390/s140406677
Received: 12 November 2013 / Revised: 17 February 2014 / Accepted: 20 March 2014 / Published: 10 April 2014
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Abstract
We developed an upper-limb power-assist exoskeleton actuated by pneumatic muscles. The exoskeleton included two metal links: a nylon joint, four size-adjustable carbon fiber bracers, a potentiometer and two pneumatic muscles. The proportional myoelectric control method was proposed to control the exoskeleton according [...] Read more.
We developed an upper-limb power-assist exoskeleton actuated by pneumatic muscles. The exoskeleton included two metal links: a nylon joint, four size-adjustable carbon fiber bracers, a potentiometer and two pneumatic muscles. The proportional myoelectric control method was proposed to control the exoskeleton according to the user’s motion intention in real time. With the feature extraction procedure and the classification (back-propagation neural network), an electromyogram (EMG)-angle model was constructed to be used for pattern recognition. Six healthy subjects performed elbow flexion-extension movements under four experimental conditions: (1) holding a 1-kg load, wearing the exoskeleton, but with no actuation and for different periods (2-s, 4-s and 8-s periods); (2) holding a 1-kg load, without wearing the exoskeleton, for a fixed period; (3) holding a 1-kg load, wearing the exoskeleton, but with no actuation, for a fixed period; (4) holding a 1-kg load, wearing the exoskeleton under proportional myoelectric control, for a fixed period. The EMG signals of the biceps brachii, the brachioradialis, the triceps brachii and the anconeus and the angle of the elbow were collected. The control scheme’s reliability and power-assist effectiveness were evaluated in the experiments. The results indicated that the exoskeleton could be controlled by the user’s motion intention in real time and that it was useful for augmenting arm performance with neurological signal control, which could be applied to assist in elbow rehabilitation after neurological injury. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle New System for Tracking a Device for Diagnosing Scalp Skin
Sensors 2014, 14(4), 6516-6534; doi:10.3390/s140406516
Received: 13 November 2013 / Revised: 13 March 2014 / Accepted: 30 March 2014 / Published: 9 April 2014
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Abstract
In scalp skin examinations, it is difficult to find a previously treated region on a patient’s scalp through images captured by a camera attached to a diagnostic device because the zoom lens on camera has a small field of view. Thus, doctors [...] Read more.
In scalp skin examinations, it is difficult to find a previously treated region on a patient’s scalp through images captured by a camera attached to a diagnostic device because the zoom lens on camera has a small field of view. Thus, doctors manually record the region on a chart or manually mark the region. However, this process is slow and inconveniences the patient. Thus, we propose a new system for tracking the diagnostic device for the scalp skin of patients. Our research is novel in four ways. First, our proposed system consists of two cameras to capture the face and the diagnostic device. Second, the user can easily set the position of camera to capture the diagnostic device by manually moving a frame to which the camera is attached. Third, the position of patient’s nostrils and corners of the eyes are detected to align the position of his/her head more accurately with the recorded position from previous sessions. Fourth, the position of the diagnostic device is continuously tracked during the examination through images that help detect the position of the color marker attached to the device. Experimental results show that our system has a higher performance than conventional method. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle Low-Power Wearable Respiratory Sound Sensing
Sensors 2014, 14(4), 6535-6566; doi:10.3390/s140406535
Received: 24 December 2013 / Revised: 19 March 2014 / Accepted: 30 March 2014 / Published: 9 April 2014
Cited by 8 | PDF Full-text (2135 KB) | HTML Full-text | XML Full-text
Abstract
Building upon the findings from the field of automated recognition of respiratory sound patterns, we propose a wearable wireless sensor implementing on-board respiratory sound acquisition and classification, to enable continuous monitoring of symptoms, such as asthmatic wheezing. Low-power consumption of such a [...] Read more.
Building upon the findings from the field of automated recognition of respiratory sound patterns, we propose a wearable wireless sensor implementing on-board respiratory sound acquisition and classification, to enable continuous monitoring of symptoms, such as asthmatic wheezing. Low-power consumption of such a sensor is required in order to achieve long autonomy. Considering that the power consumption of its radio is kept minimal if transmitting only upon (rare) occurrences of wheezing, we focus on optimizing the power consumption of the digital signal processor (DSP). Based on a comprehensive review of asthmatic wheeze detection algorithms, we analyze the computational complexity of common features drawn from short-time Fourier transform (STFT) and decision tree classification. Four algorithms were implemented on a low-power TMS320C5505 DSP. Their classification accuracies were evaluated on a dataset of prerecorded respiratory sounds in two operating scenarios of different detection fidelities. The execution times of all algorithms were measured. The best classification accuracy of over 92%, while occupying only 2.6% of the DSP’s processing time, is obtained for the algorithm featuring the time-frequency tracking of shapes of crests originating from wheezing, with spectral features modeled using energy. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle Wavelet-Based Watermarking and Compression for ECG Signals with Verification Evaluation
Sensors 2014, 14(2), 3721-3736; doi:10.3390/s140203721
Received: 14 December 2013 / Revised: 6 February 2014 / Accepted: 18 February 2014 / Published: 21 February 2014
Cited by 7 | PDF Full-text (499 KB) | HTML Full-text | XML Full-text
Abstract
In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG) data in order to protect individual information. [...] Read more.
In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG) data in order to protect individual information. An ECG signal can not only be used to analyze disease, but also to provide crucial biometric information for identification and authentication. In this study, we propose a new idea of integrating electrocardiogram watermarking and compression approach, which has never been researched before. ECG watermarking can ensure the confidentiality and reliability of a user’s data while reducing the amount of data. In the evaluation, we apply the embedding capacity, bit error rate (BER), signal-to-noise ratio (SNR), compression ratio (CR), and compressed-signal to noise ratio (CNR) methods to assess the proposed algorithm. After comprehensive evaluation the final results show that our algorithm is robust and feasible. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Techniques for Clutter Suppression in the Presence of Body Movements during the Detection of Respiratory Activity through UWB Radars
Sensors 2014, 14(2), 2595-2618; doi:10.3390/s140202595
Received: 20 December 2013 / Revised: 16 January 2014 / Accepted: 24 January 2014 / Published: 7 February 2014
Cited by 11 | PDF Full-text (661 KB) | HTML Full-text | XML Full-text
Abstract
This paper focuses on the feasibility of tracking the chest wall movement of a human subject during respiration from the waveforms recorded using an impulse-radio (IR) ultra-wideband radar. The paper describes the signal processing to estimate sleep apnea detection and breathing rate. [...] Read more.
This paper focuses on the feasibility of tracking the chest wall movement of a human subject during respiration from the waveforms recorded using an impulse-radio (IR) ultra-wideband radar. The paper describes the signal processing to estimate sleep apnea detection and breathing rate. Some techniques to solve several problems in these types of measurements, such as the clutter suppression, body movement and body orientation detection are described. Clutter suppression is achieved using a moving averaging filter to dynamically estimate it. The artifacts caused by body movements are removed using a threshold method before analyzing the breathing signal. The motion is detected using the time delay that maximizes the received signal after a clutter removing algorithm is applied. The periods in which the standard deviations of the time delay exceed a threshold are considered macro-movements and they are neglected. The sleep apnea intervals are detected when the breathing signal is below a threshold. The breathing rate is determined from the robust spectrum estimation based on Lomb periodogram algorithm. On the other hand the breathing signal amplitude depends on the body orientation respect to the antennas, and this could be a problem. In this case, in order to maximize the signal-to-noise ratio, multiple sensors are proposed to ensure that the backscattered signal can be detected by at least one sensor, regardless of the direction the human subject is facing. The feasibility of the system is compared with signals recorded by a microphone. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Modeling and Characterization of the Implant Intra-Body Communication Based on Capacitive Coupling Using a Transfer Function Method
Sensors 2014, 14(1), 1740-1756; doi:10.3390/s140101740
Received: 23 October 2013 / Revised: 7 January 2014 / Accepted: 16 January 2014 / Published: 20 January 2014
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Abstract
Implantable devices have important applications in biomedical sensor networks used for biomedical monitoring, diagnosis and treatment, etc. In this paper, an implant intra-body communication (IBC) method based on capacitive coupling has been proposed, and the modeling and characterization of this kind [...] Read more.
Implantable devices have important applications in biomedical sensor networks used for biomedical monitoring, diagnosis and treatment, etc. In this paper, an implant intra-body communication (IBC) method based on capacitive coupling has been proposed, and the modeling and characterization of this kind of IBC has been investigated. Firstly, the transfer function of the implant IBC based on capacitive coupling was derived. Secondly, the corresponding parameters of the transfer function are discussed. Finally, both measurements and simulations based on the proposed transfer function were carried out, while some important conclusions have been achieved, which indicate that the achieved transfer function and conclusions are able to help to achieve an implant communication method with the highly desirable characteristics of low power consumption, high data rate, high transmission quality, etc. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle Mobile Cloud-Computing-Based Healthcare Service by Noncontact ECG Monitoring
Sensors 2013, 13(12), 16451-16473; doi:10.3390/s131216451
Received: 9 September 2013 / Revised: 14 October 2013 / Accepted: 21 November 2013 / Published: 2 December 2013
Cited by 15 | PDF Full-text (2332 KB) | HTML Full-text | XML Full-text
Abstract
Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical [...] Read more.
Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset) to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier
Sensors 2013, 13(12), 16494-16511; doi:10.3390/s131216494
Received: 9 September 2013 / Revised: 4 November 2013 / Accepted: 25 November 2013 / Published: 2 December 2013
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Abstract
Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the [...] Read more.
Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC) analysis and a support vector machine (SVM) classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO) classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Development and Evaluation of an Improved Technique for Pulmonary Function Testing Using Electrical Impedance Pneumography Intended for the Diagnosis of Chronic Obstructive Pulmonary Disease Patients
Sensors 2013, 13(11), 15846-15860; doi:10.3390/s131115846
Received: 8 September 2013 / Revised: 22 October 2013 / Accepted: 12 November 2013 / Published: 21 November 2013
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Abstract
Spirometry is regarded as the only effective method for detecting pulmonary function test (PFT) indices. In this study, a novel impedance pulmonary function measurement system (IPFS) is developed for directly assessing PFT indices. IPFS can obtain high resolution values and remove motion [...] Read more.
Spirometry is regarded as the only effective method for detecting pulmonary function test (PFT) indices. In this study, a novel impedance pulmonary function measurement system (IPFS) is developed for directly assessing PFT indices. IPFS can obtain high resolution values and remove motion artifacts through real-time base impedance feedback. Feedback enables the detection of PFT indices using only both hands for convenience. IPFS showed no differences in the sitting, supine, and standing postures during the measurements, indicating that patient posture has no effect on IPFS. Mean distance analysis showed good agreement between the volume and flow signal of IPFS (p < 0.05). PFT indices were detected in subjects to differentiate a chronic obstructive pulmonary disease (COPD) patient group from a normal group. The forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), FEV1/FVC, and peak expiratory flow (PEF) in the COPD group were lower than those in the normal group by IPFS (p < 0.05). IPFS is therefore suitable for evaluating pulmonary function in normal and COPD patients. Moreover, IPFS could be useful for periodic monitoring of existing patients diagnosed with obstructive lung disease. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Digital Signal Processing by Virtual Instrumentation of a MEMS Magnetic Field Sensor for Biomedical Applications
Sensors 2013, 13(11), 15068-15084; doi:10.3390/s131115068
Received: 1 July 2013 / Revised: 24 October 2013 / Accepted: 28 October 2013 / Published: 5 November 2013
Cited by 5 | PDF Full-text (610 KB) | HTML Full-text | XML Full-text
Abstract
We present a signal processing system with virtual instrumentation of a MEMS sensor to detect magnetic flux density for biomedical applications. This system consists of a magnetic field sensor, electronic components implemented on a printed circuit board (PCB), a data acquisition (DAQ) [...] Read more.
We present a signal processing system with virtual instrumentation of a MEMS sensor to detect magnetic flux density for biomedical applications. This system consists of a magnetic field sensor, electronic components implemented on a printed circuit board (PCB), a data acquisition (DAQ) card, and a virtual instrument. It allows the development of a semi-portable prototype with the capacity to filter small electromagnetic interference signals through digital signal processing. The virtual instrument includes an algorithm to implement different configurations of infinite impulse response (IIR) filters. The PCB contains a precision instrumentation amplifier, a demodulator, a low-pass filter (LPF) and a buffer with operational amplifier. The proposed prototype is used for real-time non-invasive monitoring of magnetic flux density in the thoracic cage of rats. The response of the rat respiratory magnetogram displays a similar behavior as the rat electromyogram (EMG). Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle EOG Artifact Correction from EEG Recording Using Stationary Subspace Analysis and Empirical Mode Decomposition
Sensors 2013, 13(11), 14839-14859; doi:10.3390/s131114839
Received: 18 September 2013 / Revised: 21 October 2013 / Accepted: 29 October 2013 / Published: 1 November 2013
Cited by 14 | PDF Full-text (1276 KB) | HTML Full-text | XML Full-text
Abstract
Ocular contamination of EEG data is an important and very common problem in the diagnosis of neurobiological events. An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. First, it conducts the blind source separation [...] Read more.
Ocular contamination of EEG data is an important and very common problem in the diagnosis of neurobiological events. An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. First, it conducts the blind source separation on the raw EEG recording by the stationary subspace analysis, which can concentrate artifacts in fewer components than the representative blind source separation methods. Next, to recover the neural information that has leaked into the artifactual components, the adaptive signal decomposition technique EMD is applied to denoise the components. Finally, the artifact-only components are projected back to be subtracted from EEG signals to get the clean EEG data. The experimental results on both the artificially contaminated EEG data and publicly available real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly non-stationary and the underlying sources cannot be assumed to be independent or uncorrelated. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Programmable Gain Amplifiers with DC Suppression and Low Output Offset for Bioelectric Sensors
Sensors 2013, 13(10), 13123-13142; doi:10.3390/s131013123
Received: 4 July 2013 / Revised: 17 September 2013 / Accepted: 22 September 2013 / Published: 27 September 2013
Cited by 4 | PDF Full-text (1209 KB) | HTML Full-text | XML Full-text
Abstract
DC-offset and DC-suppression are key parameters in bioelectric amplifiers. However, specific DC analyses are not often explained. Several factors influence the DC-budget: the programmable gain, the programmable cut-off frequencies for high pass filtering and, the low cut-off values and the capacitor blocking [...] Read more.
DC-offset and DC-suppression are key parameters in bioelectric amplifiers. However, specific DC analyses are not often explained. Several factors influence the DC-budget: the programmable gain, the programmable cut-off frequencies for high pass filtering and, the low cut-off values and the capacitor blocking issues involved. A new intermediate stage is proposed to address the DC problem entirely. Two implementations were tested. The stage is composed of a programmable gain amplifier (PGA) with DC-rejection and low output offset. Cut-off frequencies are selectable and values from 0.016 to 31.83 Hz were tested, and the capacitor deblocking is embedded in the design. Hence, this PGA delivers most of the required gain with constant low output offset, notwithstanding the gain or cut-off frequency selected. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
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Open AccessArticle Model-Based Spike Detection of Epileptic EEG Data
Sensors 2013, 13(9), 12536-12547; doi:10.3390/s130912536
Received: 17 June 2013 / Revised: 6 September 2013 / Accepted: 13 September 2013 / Published: 17 September 2013
Cited by 8 | PDF Full-text (399 KB) | HTML Full-text | XML Full-text
Abstract
Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic electroencephalogram (EEG) data. In this paper, a new two-stage approach is proposed for epileptic spike detection. First, the k-point nonlinear energy operator (k-NEO) is adopted to detect all possible spike [...] Read more.
Accurate automatic spike detection is highly beneficial to clinical assessment of epileptic electroencephalogram (EEG) data. In this paper, a new two-stage approach is proposed for epileptic spike detection. First, the k-point nonlinear energy operator (k-NEO) is adopted to detect all possible spike candidates, then a newly proposed spike model with slow wave features is applied to these candidates for spike classification. Experimental results show that the proposed system, using the AdaBoost classifier, outperforms the conventional method in both two- and three-class EEG pattern classification problems. The proposed system not only achieves better accuracy for spike detection, but also provides new ability to differentiate between spikes and spikes with slow waves. Though spikes with slow waves occur frequently in epileptic EEGs, they are not used in conventional spike detection. Identifying spikes with slow waves allows the proposed system to have better capability for assisting clinical neurologists in routine EEG examinations and epileptic diagnosis. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle An Acetylcholinesterase-Based Chronoamperometric Biosensor for Fast and Reliable Assay of Nerve Agents
Sensors 2013, 13(9), 11498-11506; doi:10.3390/s130911498
Received: 16 July 2013 / Revised: 19 August 2013 / Accepted: 27 August 2013 / Published: 30 August 2013
Cited by 12 | PDF Full-text (223 KB) | HTML Full-text | XML Full-text
Abstract
The enzyme acetylcholinesterase (AChE) is an important part of cholinergic nervous system, where it stops neurotransmission by hydrolysis of the neurotransmitter acetylcholine. It is sensitive to inhibition by organophosphate and carbamate insecticides, some Alzheimer disease drugs, secondary metabolites such as aflatoxins and [...] Read more.
The enzyme acetylcholinesterase (AChE) is an important part of cholinergic nervous system, where it stops neurotransmission by hydrolysis of the neurotransmitter acetylcholine. It is sensitive to inhibition by organophosphate and carbamate insecticides, some Alzheimer disease drugs, secondary metabolites such as aflatoxins and nerve agents used in chemical warfare. When immobilized on a sensor (physico-chemical transducer), it can be used for assay of these inhibitors. In the experiments described herein, an AChE- based electrochemical biosensor using screen printed electrode systems was prepared. The biosensor was used for assay of nerve agents such as sarin, soman, tabun and VX. The limits of detection achieved in a measuring protocol lasting ten minutes were 7.41 × 10−12 mol/L for sarin, 6.31 × 10−12 mol /L for soman, 6.17 × 10−11 mol/L for tabun, and 2.19 × 10−11 mol/L for VX, respectively. The assay was reliable, with minor interferences caused by the organic solvents ethanol, methanol, isopropanol and acetonitrile. Isopropanol was chosen as suitable medium for processing lipophilic samples. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Fluidic Automation of Nitrate and Nitrite Bioassays in Whole Blood by Dissolvable-Film Based Centrifugo-Pneumatic Actuation
Sensors 2013, 13(9), 11336-11349; doi:10.3390/s130911336
Received: 18 July 2013 / Revised: 7 August 2013 / Accepted: 14 August 2013 / Published: 26 August 2013
Cited by 11 | PDF Full-text (748 KB) | HTML Full-text | XML Full-text
Abstract
This paper demonstrates the full centrifugal microfluidic integration and automation of all liquid handling steps of a 7-step fluorescence-linked immunosorbent assay (FLISA) for quantifying nitrate and nitrite levels in whole blood within about 15 min. The assay protocol encompasses the extraction of [...] Read more.
This paper demonstrates the full centrifugal microfluidic integration and automation of all liquid handling steps of a 7-step fluorescence-linked immunosorbent assay (FLISA) for quantifying nitrate and nitrite levels in whole blood within about 15 min. The assay protocol encompasses the extraction of metered plasma, the controlled release of sample and reagents (enzymes, co-factors and fluorescent labels), and incubation and detection steps. Flow control is implemented by a rotationally actuated dissolvable film (DF) valving scheme. In the valves, the burst pressure is primarily determined by the radial position, geometry and volume of the valve chamber and its inlet channel and can thus be individually tuned over an extraordinarily wide range of equivalent spin rates between 1,000 RPM and 5,500 RPM. Furthermore, the vapour barrier properties of the DF valves are investigated in this paper in order to further show the potential for commercially relevant on-board storage of liquid reagents during shelf-life of bioanalytical, ready-to-use discs. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessArticle Automatic and Direct Identification of Blink Components from Scalp EEG
Sensors 2013, 13(8), 10783-10801; doi:10.3390/s130810783
Received: 15 July 2013 / Revised: 8 August 2013 / Accepted: 12 August 2013 / Published: 16 August 2013
Cited by 5 | PDF Full-text (2899 KB) | HTML Full-text | XML Full-text
Abstract
Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink [...] Read more.
Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink as an external source has a higher sum of correlation with frontal EEG channels than all other sources due to both its location and significant amplitude, in this paper, we proposed a method based on correlation index and the feature of power distribution to automatically detect eye blink components. Furthermore, we prove mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA. This helps to simplify calculations and understand the implications of the correlation. The proposed method doesn’t need to select a template or thresholds in advance, and it works without simultaneously recording an electrooculography (EOG) reference. The experimental results demonstrate that the proposed method can automatically recognize eye blink components with a high accuracy on entire datasets from 15 subjects. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)

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Open AccessReview Dry EEG Electrodes
Sensors 2014, 14(7), 12847-12870; doi:10.3390/s140712847
Received: 2 May 2014 / Revised: 1 July 2014 / Accepted: 14 July 2014 / Published: 18 July 2014
Cited by 19 | PDF Full-text (788 KB) | HTML Full-text | XML Full-text
Abstract
Electroencephalography (EEG) emerged in the second decade of the 20th century as a technique for recording the neurophysiological response. Since then, there has been little variation in the physical principles that sustain the signal acquisition probes, otherwise called electrodes. Currently, new advances [...] Read more.
Electroencephalography (EEG) emerged in the second decade of the 20th century as a technique for recording the neurophysiological response. Since then, there has been little variation in the physical principles that sustain the signal acquisition probes, otherwise called electrodes. Currently, new advances in technology have brought new unexpected fields of applications apart from the clinical, for which new aspects such as usability and gel-free operation are first order priorities. Thanks to new advances in materials and integrated electronic systems technologies, a new generation of dry electrodes has been developed to fulfill the need. In this manuscript, we review current approaches to develop dry EEG electrodes for clinical and other applications, including information about measurement methods and evaluation reports. We conclude that, although a broad and non-homogeneous diversity of approaches has been evaluated without a consensus in procedures and methodology, their performances are not far from those obtained with wet electrodes, which are considered the gold standard, thus enabling the former to be a useful tool in a variety of novel applications. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessReview Automatic Fall Monitoring: A Review
Sensors 2014, 14(7), 12900-12936; doi:10.3390/s140712900
Received: 8 April 2014 / Revised: 2 July 2014 / Accepted: 7 July 2014 / Published: 18 July 2014
Cited by 15 | PDF Full-text (2752 KB) | HTML Full-text | XML Full-text
Abstract
Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more [...] Read more.
Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessReview The Effectiveness of FES-Evoked EMG Potentials to Assess Muscle Force and Fatigue in Individuals with Spinal Cord Injury
Sensors 2014, 14(7), 12598-12622; doi:10.3390/s140712598
Received: 28 May 2014 / Revised: 9 July 2014 / Accepted: 9 July 2014 / Published: 14 July 2014
Cited by 6 | PDF Full-text (884 KB) | HTML Full-text | XML Full-text
Abstract
The evoked electromyographic signal (eEMG) potential is the standard index used to monitor both electrical changes within the motor unit during muscular activity and the electrical patterns during evoked contraction. However, technical and physiological limitations often preclude the acquisition and analysis of [...] Read more.
The evoked electromyographic signal (eEMG) potential is the standard index used to monitor both electrical changes within the motor unit during muscular activity and the electrical patterns during evoked contraction. However, technical and physiological limitations often preclude the acquisition and analysis of the signal especially during functional electrical stimulation (FES)-evoked contractions. Hence, an accurate quantification of the relationship between the eEMG potential and FES-evoked muscle response remains elusive and continues to attract the attention of researchers due to its potential application in the fields of biomechanics, muscle physiology, and rehabilitation science. We conducted a systematic review to examine the effectiveness of eEMG potentials to assess muscle force and fatigue, particularly as a biofeedback descriptor of FES-evoked contractions in individuals with spinal cord injury. At the outset, 2867 citations were identified and, finally, fifty-nine trials met the inclusion criteria. Four hypotheses were proposed and evaluated to inform this review. The results showed that eEMG is effective at quantifying muscle force and fatigue during isometric contraction, but may not be effective during dynamic contractions including cycling and stepping. Positive correlation of up to r = 0.90 (p < 0.05) between the decline in the peak-to-peak amplitude of the eEMG and the decline in the force output during fatiguing isometric contractions has been reported. In the available prediction models, the performance index of the eEMG signal to estimate the generated muscle force ranged from 3.8% to 34% for 18 s to 70 s ahead of the actual muscle force generation. The strength and inherent limitations of the eEMG signal to assess muscle force and fatigue were evident from our findings with implications in clinical management of spinal cord injury (SCI) population. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessReview The Theory and Fundamentals of Bioimpedance Analysis in Clinical Status Monitoring and Diagnosis of Diseases
Sensors 2014, 14(6), 10895-10928; doi:10.3390/s140610895
Received: 8 April 2014 / Revised: 3 June 2014 / Accepted: 4 June 2014 / Published: 19 June 2014
Cited by 11 | PDF Full-text (821 KB) | HTML Full-text | XML Full-text
Abstract
Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance [...] Read more.
Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance in body composition estimation and evaluation of clinical status. This paper reviews the main concepts of bioimpedance measurement techniques including the frequency based, the allocation based, bioimpedance vector analysis and the real time bioimpedance analysis systems. Commonly used prediction equations for body composition assessment and influence of anthropometric measurements, gender, ethnic groups, postures, measurements protocols and electrode artifacts in estimated values are also discussed. In addition, this paper also contributes to the deliberations of bioimpedance analysis assessment of abnormal loss in lean body mass and unbalanced shift in body fluids and to the summary of diagnostic usage in different kinds of conditions such as cardiac, pulmonary, renal, and neural and infection diseases. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Open AccessReview Application of Wireless Power Transmission Systems in Wireless Capsule Endoscopy: An Overview
Sensors 2014, 14(6), 10929-10951; doi:10.3390/s140610929
Received: 13 March 2014 / Revised: 18 May 2014 / Accepted: 26 May 2014 / Published: 19 June 2014
Cited by 11 | PDF Full-text (684 KB) | HTML Full-text | XML Full-text
Abstract
Wireless capsule endoscopy (WCE) is a promising technology for direct diagnosis of the entire small bowel to detect lethal diseases, including cancer and obscure gastrointestinal bleeding (OGIB). To improve the quality of diagnosis, some vital specifications of WCE such as image resolution, [...] Read more.
Wireless capsule endoscopy (WCE) is a promising technology for direct diagnosis of the entire small bowel to detect lethal diseases, including cancer and obscure gastrointestinal bleeding (OGIB). To improve the quality of diagnosis, some vital specifications of WCE such as image resolution, frame rate and working time need to be improved. Additionally, future multi-functioning robotic capsule endoscopy (RCE) units may utilize advanced features such as active system control over capsule motion, drug delivery systems, semi-surgical tools and biopsy. However, the inclusion of the above advanced features demands additional power that make conventional power source methods impractical. In this regards, wireless power transmission (WPT) system has received attention among researchers to overcome this problem. Systematic reviews on techniques of using WPT for WCE are limited, especially when involving the recent technological advancements. This paper aims to fill that gap by providing a systematic review with emphasis on the aspects related to the amount of transmitted power, the power transmission efficiency, the system stability and patient safety. It is noted that, thus far the development of WPT system for this WCE application is still in initial stage and there is room for improvements, especially involving system efficiency, stability, and the patient safety aspects. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)
Figures

Open AccessReview Surface Electromyography Signal Processing and Classification Techniques
Sensors 2013, 13(9), 12431-12466; doi:10.3390/s130912431
Received: 20 July 2013 / Revised: 21 August 2013 / Accepted: 11 September 2013 / Published: 17 September 2013
Cited by 46 | PDF Full-text (1288 KB) | HTML Full-text | XML Full-text
Abstract
Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. [...] Read more.
Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)

Other

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Open AccessCorrection Correction: Supuk, T.G., et al. Design, Development and Testing of a Low-Cost sEMG System and Its Use in Recording Muscle Activity in Human Gait. Sensors 2014, 14, 8235–8258
Sensors 2014, 14(8), 15639-15640; doi:10.3390/s140815639
Received: 25 July 2014 / Accepted: 4 August 2014 / Published: 22 August 2014
PDF Full-text (620 KB) | HTML Full-text | XML Full-text
Abstract
The authors wish to make the following correction to this paper [1]. Due to an error Figure 15 was a duplicate of Figure 13, the former Figure 15 (labelled here as Previous Figure 15) should be replaced by the new version shown [...] Read more.
The authors wish to make the following correction to this paper [1]. Due to an error Figure 15 was a duplicate of Figure 13, the former Figure 15 (labelled here as Previous Figure 15) should be replaced by the new version shown below (labeled here as New Figure 15):[...] Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems)

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