Topical Collection "Wearable Biosensors for Healthcare Applications"
A topical collection in Biosensors (ISSN 2079-6374).
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Collection Editor
Graduate Institute of Biomedical Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan
Interests: motion sensors; medical mechatronics; intelligent mobile healthcare; smart wearable sensors; IOT in healthcare
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Department of Electrical Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan
Interests: inertial motion sensing for healthcare applications; development of wearable devices; embedded system designs
Topical Collection Information
Dear Colleagues,
Wearable technologies play very important roles in biomedical research and have been applied in numerous healthcare applications. In recent years, many sensors have been designed and developed for wearable devices or embedded in wearable devices to measure certain biological or physiological signals from the human body. How these sensors are designed and/or developed for collecting bio-signals from humans in wearable technologies is of great interest to researchers and can be extremely challenging. Furthermore, some important features that reflect the condition of human health could be retrieved after information transformation from data measured by these sensors. Then, they could be applied for the purpose of monitoring, assessing, or improving human health. Articles that either disclose original research contributions or reviews of the current state-of-the-art in the related fields are welcome for submission to this Special Issue.
Topics include, but are not restricted to:
- Novel design and/or development of wearable sensors for biomedical signal measurements;
- Novel materials for wearable sensors;
- Wearable systems for health monitoring and intervention;
- Mobile healthcare applications for wearable sensors;
- Point of care with wearable sensors;
- Manufacturing methods for fabricating wearable, flexible, or stretchable sensors;
- Signal processing and feature extraction schemes for wearable sensors;
- System integration for wearable sensors;
- Wearable sensor fusion for healthcare applications.
Prof. Dr. Ming-Yih Lee
Prof. Dr. Wen-Yen Lin
Collection Editors
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Keywords
- wearable technologies
- body sensor network
- feature extraction
- e-health
- data processing
- wearable sensors
Published Papers (10 papers)
Open AccessCommunication
A Wearable Breath Sensor Based on Fiber-Tip Microcantilever
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Abstract
Respiration rate is an essential vital sign that requires monitoring under various conditions, including in strong electromagnetic environments such as in magnetic resonance imaging systems. To provide an electromagnetically-immune breath-sensing system, we propose an all-fiber-optic wearable breath sensor based on a fiber-tip microcantilever.
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Respiration rate is an essential vital sign that requires monitoring under various conditions, including in strong electromagnetic environments such as in magnetic resonance imaging systems. To provide an electromagnetically-immune breath-sensing system, we propose an all-fiber-optic wearable breath sensor based on a fiber-tip microcantilever. The microcantilever was fabricated on a fiber-tip by two-photon polymerization microfabrication based on femtosecond laser, so that a micro Fabry–Pérot (FP) interferometer was formed between the microcantilever and the end-face of the fiber. The cavity length of the micro FP interferometer was reduced as a result of the bending of the microcantilever induced by breath airflow. The signal of breath rate was rebuilt by detecting power variations of the FP interferometer reflected light and applying dynamic thresholds. The breath sensor achieved a high sensitivity of 0.8 nm/(m/s) by detecting the reflection spectrum upon applied flow velocities from 0.53 to 5.31 m/s. This sensor was also shown to have excellent thermal stability as its cross-sensitivity of airflow with respect to the temperature response was only 0.095 (m/s)/°C. When mounted inside a wearable surgical mask, the sensor demonstrated the capability to detect various breath patterns, including normal, fast, random, and deep breaths. We anticipate the proposed wearable breath sensor could be a useful and reliable tool for respiration rate monitoring.
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Open AccessArticle
A Flexible, Wearable, and Wireless Biosensor Patch with Internet of Medical Things Applications
Cited by 14 | Viewed by 6015
Abstract
Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological
[...] Read more.
Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.
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Open AccessArticle
Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset
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Abstract
Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples
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Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi’s fractal dimension, and Katz’s fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.
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Open AccessArticle
An Instrumented Glove-Controlled Portable Hand-Exoskeleton for Bilateral Hand Rehabilitation
by
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Abstract
Effective bilateral hand training is desired in rehabilitation programs to restore hand function for people with unilateral hemiplegia, so that they can perform daily activities independently. However, owing to limited human resources, the hand function training available in current clinical settings is significantly
[...] Read more.
Effective bilateral hand training is desired in rehabilitation programs to restore hand function for people with unilateral hemiplegia, so that they can perform daily activities independently. However, owing to limited human resources, the hand function training available in current clinical settings is significantly less than the adequate amount needed to drive optimal neural reorganization. In this study, we designed a lightweight and portable hand exoskeleton with a hand-sensing glove for bilateral hand training and home-based rehabilitation. The hand-sensing glove measures the hand movement of the less-affected hand using a flex sensor. Thereafter, the affected hand is driven by the hand exoskeleton using the measured hand movements. Compared with the existing hand exoskeletons, our hand exoskeleton improves the flexible mechanism for the back of the hand for better wearing experience and the thumb mechanism to make the pinch gesture possible. We designed a virtual reality game to increase the willingness of repeated movement practice for rehabilitation. Our system not only facilitates bilateral hand training but also assists in activities of daily living. This system could be beneficial for patients with hemiplegia for starting correct and sufficient hand function training in the early stages to optimize their recovery.
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Open AccessArticle
Design and Implementation of a Wearable Accelerometer-Based Motion/Tilt Sensing Internet of Things Module and Its Application to Bed Fall Prevention
Cited by 4 | Viewed by 1833
Abstract
Accelerometer-based motion sensing has been extensively applied to fall detection. However, such applications can only detect fall accidents; therefore, a system that can prevent fall accidents is desirable. Bed falls account for more than half of patient falls and are preceded by a
[...] Read more.
Accelerometer-based motion sensing has been extensively applied to fall detection. However, such applications can only detect fall accidents; therefore, a system that can prevent fall accidents is desirable. Bed falls account for more than half of patient falls and are preceded by a clear warning indicator: the patient attempting to get out of bed. This study designed and implemented an Internet of Things module, namely, Bluetooth low-energy-enabled Accelerometer-based Sensing In a Chip-packaging (BASIC) module, with a tilt-sensing algorithm based on the patented low-complexity COordinate Rotation DIgital Computer (CORDIC)-based algorithm for tilt angle conversions. It is applied for detecting the postural changes (from lying down to sitting up) and to protect individuals at a high risk of bed falls by prompting caregivers to take preventive actions and assist individuals trying to get up. This module demonstrates how motion and tilt sensing can be applied to bed fall prevention. The module can be further miniaturized or integrated into a wearable device and commercialized in smart health-care applications for bed fall prevention in hospitals and homes.
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Open AccessReview
Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring
Cited by 52 | Viewed by 7618
Abstract
IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to
[...] Read more.
IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to integrate it with IoT-assisted wearable sensor systems seamlessly. This review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms. It compiles various communication technologies and the devices commonly used in IoT-assisted wearable sensor systems and deals with its various applications in healthcare and their advantages to the world. A comparative analysis of all the wearable technology in healthcare is also discussed with tabulation of various research and technology. This review also analyses all the problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare and describes the various future implementations that can be made to the architecture and the technology to improve the healthcare industry.
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Open AccessArticle
Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation
Cited by 5 | Viewed by 2218
Abstract
In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due
[...] Read more.
In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.
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Open AccessArticle
A Study of One-Class Classification Algorithms for Wearable Fall Sensors
Cited by 6 | Viewed by 1944
Abstract
In recent years, the popularity of wearable devices has fostered the investigation of automatic fall detection systems based on the analysis of the signals captured by transportable inertial sensors. Due to the complexity and variety of human movements, the detection algorithms that offer
[...] Read more.
In recent years, the popularity of wearable devices has fostered the investigation of automatic fall detection systems based on the analysis of the signals captured by transportable inertial sensors. Due to the complexity and variety of human movements, the detection algorithms that offer the best performance when discriminating falls from conventional Activities of Daily Living (ADLs) are those built on machine learning and deep learning mechanisms. In this regard, supervised machine learning binary classification methods have been massively employed by the related literature. However, the learning phase of these algorithms requires mobility patterns caused by falls, which are very difficult to obtain in realistic application scenarios. An interesting alternative is offered by One-Class Classifiers (OCCs), which can be exclusively trained and configured with movement traces of a single type (ADLs). In this paper, a systematic study of the performance of various typical OCCs (for diverse sets of input features and hyperparameters) is performed when applied to nine public repositories of falls and ADLs. The results show the potentials of these classifiers, which are capable of achieving performance metrics very similar to those of supervised algorithms (with values for the specificity and the sensitivity higher than 95%). However, the study warns of the need to have a wide variety of types of ADLs when training OCCs, since activities with a high degree of mobility can significantly increase the frequency of false alarms (ADLs identified as falls) if not considered in the data subsets used for training.
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Open AccessArticle
Micro-Droplet Platform for Exploring the Mechanism of Mixed Field Agglutination in B3 Subtype
Cited by 1 | Viewed by 2122
Abstract
B
3 is the most common subtype of blood group B in the Taiwanese population, and most of the B
3 individuals in the Taiwanese population have the IVS3 + 5 G > A (rs55852701) gene variation. Additionally, a typical mixed field agglutination
[...] Read more.
B
3 is the most common subtype of blood group B in the Taiwanese population, and most of the B
3 individuals in the Taiwanese population have the IVS3 + 5 G > A (rs55852701) gene variation. Additionally, a typical mixed field agglutination is observed when the B
3 subtype is tested with anti-B antibody or anti-AB antibody. The molecular biology of the gene variation in the B
3 subtype has been identified, however, the mechanism of the mixed field agglutination caused by the type B
3 blood samples is still unclear. Therefore, the purpose of this study was to understand the reason for the mixed field agglutination caused by B
3. A micro-droplet platform was used to observe the agglutination of type B and type B
3 blood samples in different blood sample concentrations, antibody concentrations, and at reaction times. We found that the agglutination reaction in every droplet slowed down with an increase in the dilution ratio of blood sample and antibody, whether type B blood or type B
3 blood was used. However, as the reaction time increased, the complete agglutination in the droplet was seen in type B blood, while the mixed field agglutination still occurred in B
3 within 1 min. In addition, the degree of agglutination was similar in each droplet, which showed high reproducibility. As a result, we inferred that there are two types of cells in the B
3 subtype that simultaneously create a mixed field agglutination, rather than each red blood cell carrying a small amount of antigen, resulting in less agglutination.
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Open AccessArticle
A Novel Lightweight Wearable Soft Exosuit for Reducing the Metabolic Rate and Muscle Fatigue
Cited by 15 | Viewed by 2921
Abstract
Wearable robotic devices have been proved to considerably reduce the energy expenditure of human walking. It is not only suitable for healthy people, but also for some patients who require rehabilitation exercises. However, in many cases, the weight of soft exosuits are relatively
[...] Read more.
Wearable robotic devices have been proved to considerably reduce the energy expenditure of human walking. It is not only suitable for healthy people, but also for some patients who require rehabilitation exercises. However, in many cases, the weight of soft exosuits are relatively large, which makes it difficult for the assistant effect of the system to offset the metabolic consumption caused by the extra weight, and the heavy weight will make people uncomfortable. Therefore, reducing the weight of the whole system as much as possible and keeping the soft exosuit output power unchanged, may improve the comfort of users and further reduce the metabolic consumption. In this paper, we show that a novel lightweight soft exosuit which is currently the lightest among all known powered exoskeletons, which assists hip flexion. Indicated from the result of experiment, the novel lightweight soft exosuit reduces the metabolic consumption rate of wearers when walking on the treadmill at 5 km per hour by 11.52% compared with locomotion without the exosuit. Additionally, it can reduce more metabolic consumption than the hip extension assisted (HEA) and hip flexion assisted (HFA) soft exosuit which our team designed previously, which has a large weight. The muscle fatigue experiments show that using the lightweight soft exosuit can also reduce muscle fatigue by about 10.7%, 40.5% and 5.9% for rectus femoris, vastus lateralis and gastrocnemius respectively compared with locomotion without the exosuit. It is demonstrated that decreasing the weight of soft exosuit while maintaining the output almost unchanged can further reduce metabolic consumption and muscle fatigue, and appropriately improve the users’ comfort.
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Planned Papers
The below list represents only planned manuscripts. Some of these
manuscripts have not been received by the Editorial Office yet. Papers
submitted to MDPI journals are subject to peer-review.
Title: Smart Wearable Dual Biosensor for detecting Cortisol and Lactate from saliva or sweat using Graphene
Authors: Harshit Shukla, Tushar Rajvanshi & Vipin Kumar
Author Affiliations: University of Petroleum & Energy Studies, Dehradun, India
Abstract: Sweat and saliva carries valuable information about our physiological as well as cognitive status. Measurement of various biomarkers in sweat and saliva provides a pathway for the assessment of fatigue or cognitive ability, disease, or even physical conditions such as blood glucose or hydration levels for individuals. There has been a significant interest in developing a non-invasive, wearable sensor platform to facilitate the detection of a range of biomarkers from sweat and saliva. In this proposed research, we will intend to develop nanostructures wearable microfluidic platform suitable for non-invasive detection of sweat and saliva based biomarkers with ultrafast response, high sensitivity, great selectivity and high durability to detect the lactate and cortisol antigen even in very low concentrations. The wearable platform is composed of miniaturized electrical sensors integrated with flexible, printed microfluidic components that allow for on demand sample acquisition and transport of sweat, and subsequent detection of biomarkers at the sensing surface site. Sample acquisition is achieved utilizing absorbent pads affixed to the skin that feed hydrophilic microchannels with on-demand sample acquisition controlled through printed electrowetting valves enabling capillary pumping to continuously deliver sample to the sensor. The idea underlying the selection of Graphene as nanostructure for sensor application is its variable conductivity, highly porosity and protein adsorbing properties. The proposed study will allow the optimization of sensor parameters for making sensors with good selectivity for target analyte by using different linkers. The idea underlying the present proposal is that by coating and conjugating Graphene with metal nanoparticles, one could increase the effective surface area and modify the work functions, thereby improving the sensitivity and selectivity of the antigen. The proposed research with an aim that could take few seconds to produce a positive ID, the device could detect lactate and cortisol with in a minute, making it several thousand times faster state of the art biosensors, and, effectively a real-time instrument. It may possible to use only one electrochemical or electronic parameter (change in current/resistance) instead of using several optical/light source or several lasers as used in traditional biosensors. This means that we can make multiple readings and monitor several hormones/antigens simultaneously through one multiplexed electronic chip. Successful completion of this research will have enormous benefit to Human society through numerous applications, including the reduction of human health risks and animal health risks.
