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Special Issue "Smart Sensors for eHealth Applications"

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

Deadline for manuscript submissions: closed (15 April 2020).

Special Issue Editor

Dr. Paulo Antunes
Website1 Website2 Website3
Guest Editor
1. I3N & Physics Department, Aveiro University, 3810-193 Aveiro, Portugal
2. Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Interests: optical fiber sensors; eHealth platforms; structural health monitoring
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Smart sensors are a recent interesting technology with unlimited potential for diverse application fields, including in medicine and biomedical. Such devices are able to receive an input from the physical environment and, using built-in capabilities, process the data before passing it on.

In 2018, the World Health Assembly acknowledged the potential of digital technology in improving public health, urging Member States to prioritize its development and to use digital technology in health care as a means of promoting Universal Health Coverage and advancing the Sustainable Development Goals (71st World Health Assembly, 2018; Geneva, Switzerland). For such a goal, smart sensors can play a key role, enabling more accurate and automated collection of data.

This Special Issue will focus on the current state-of-the-art of smart sensors for eHealth applications, covering recent technological improvements in new devices/sensors and emerging applications. Both original research papers and review articles describing the current state-of-the-art in this research field are welcome. We hope this Special Issue will provide you an overview of the present status and future outlook of the aforementioned topics.

The manuscripts should cover, but are not limited to the following topics:

  • Wearable biomedical sensors
  • Sensing of physiological parameters
  • Bio/chemical probes for medicine applications
  • Integrated biomedical system (lab-on-a-fiber/ Lab-on-a-chip)
  • Optical fiber systems with microfluid integration
  • Non-invasive devices
  • Sensors in e-Health architectures
  • Energy efficient eHealth architectures
  • Big data analysis for eHealth
  • Sensors for physical rehabilitation
  • mHealth systems
  • Optical fiber immunosensors
  • Biomarkers detection
  • Low-cost, miniaturized, selective, and multiparameter devices
  • Advanced signal processing techniques
  • Applications including, but not limited to, surgery, dentistry, robotics, medical diagnostics and therapy, pharmaceutical research, and cardiovascular and chronic diseases.

Dr. Paulo Antunes
Guest Editor

Manuscript Submission Information

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

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • e-Health
  • m-Health
  • Biomedicine
  • Optical fiber sensors
  • Biomedical sensors
  • Lab-on-a-fiber
  • Lab-on-a-chip
  • Wearable sensors
  • Body-sensor networks

Published Papers (9 papers)

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Research

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Open AccessArticle
Enabling Medicine Reuse Using a Digital Time Temperature Humidity Sensor in an Internet of Pharmaceutical Things Concept
Sensors 2020, 20(11), 3080; https://doi.org/10.3390/s20113080 - 29 May 2020
Cited by 3
Abstract
Medicinal waste due to improper handling of unwanted medicines creates health and environmental risks. However, the re-dispensing of unused prescribed medicines from patients seems to be accepted by stakeholders when quality and safety requirements are met. Reusing dispensed medicines may help reduce waste, [...] Read more.
Medicinal waste due to improper handling of unwanted medicines creates health and environmental risks. However, the re-dispensing of unused prescribed medicines from patients seems to be accepted by stakeholders when quality and safety requirements are met. Reusing dispensed medicines may help reduce waste, but a comprehensive validation method is not generally available. The design of a novel digital time temperature and humidity indicator based on an Internet of Pharmaceutical Things concept is proposed to facilitate the validation, and a prototype is presented using smart sensors with cloud connectivity acting as the key technology for verifying and enabling the reuse of returned medicines. Deficiency of existing technologies is evaluated based on the results of this development, and recommendations for future research are suggested. Full article
(This article belongs to the Special Issue Smart Sensors for eHealth Applications)
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Open AccessArticle
Sensor-Based Smart Clothing for Women’s Menopause Transition Monitoring
Sensors 2020, 20(4), 1093; https://doi.org/10.3390/s20041093 - 17 Feb 2020
Cited by 1
Abstract
Aging women usually experience menopause and currently there is no single diagnosing highly-sensitive and -specific test for recognizing menopause. For most employed women at their perimenopause age it is not convenient to visit a clinic for the hormone test, which lasts for consecutive [...] Read more.
Aging women usually experience menopause and currently there is no single diagnosing highly-sensitive and -specific test for recognizing menopause. For most employed women at their perimenopause age it is not convenient to visit a clinic for the hormone test, which lasts for consecutive days. This paper develops a suit of sensor-based smart clothing used for home-based and ambulatory health monitoring for women’s menopause transition. Firstly, a survey analysis is conducted to determine the biological signals measured by sensors for indicating the symptoms of menopausal transition and also the body areas with salient symptoms to implant the sensors on the clothing. Then, the smart clothing is designed with a set of temperature and relative humidity sensors on different locations and with a microcontroller to transmit the measured data to the computer. With the smoothed data as input, a new detection algorithm for hot flashes is proposed by recognition of the concurrent occurrence of heat and sweating rise/down, and can figure out the frequency, intensity, and duration—triple dimension information of a hot flash, which is helpful to achieve precise diagnosis for menopausal transition. The smart clothing and the detection algorithm are verified by involving a group of women subjects to participate in a hot flash monitoring experiment. The experimental results show that this smart clothing monitoring system can effectively measure the skin temperature and relative humidity data and work out the frequency, duration, and intensity information of a hot flash pertaining in different body areas for individuals, which are accordant with the practice reported by the subjects. Full article
(This article belongs to the Special Issue Smart Sensors for eHealth Applications)
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Open AccessArticle
Sensor-Based Daily Physical Activity: Towards Prediction of the Level of Concern about Falling in Peripheral Neuropathy
Sensors 2020, 20(2), 505; https://doi.org/10.3390/s20020505 - 16 Jan 2020
Cited by 5
Abstract
Concern about falling is prevalent and increases the risk of falling in people with peripheral neuropathy (PN). However, the assessment of concern about falling relies on self-report surveys, and thus continuous monitoring has not been possible. We investigated the influence of concern about [...] Read more.
Concern about falling is prevalent and increases the risk of falling in people with peripheral neuropathy (PN). However, the assessment of concern about falling relies on self-report surveys, and thus continuous monitoring has not been possible. We investigated the influence of concern about falling on sensor-based daily physical activity among people with PN. Forty-nine people with PN and various levels of concern about falling participated in this study. Physical activity outcomes were measured over a period of 48 hours using a validated chest-worn sensor. The level of concern about falling was assessed using the falls efficacy scale-international (FES-I). The low concern group spent approximately 80 min more in walking and approximately 100 min less in sitting/lying compared to the high concern group. In addition, the low concern group had approximately 50% more walking bouts and step counts compared to the high concern group. Across all participants, the duration of walking bouts and total step counts was significantly correlated with FES-I scores. The duration of walking bouts and total step counts may serve as eHealth targets and strategies for fall risk assessment among people with PN. Full article
(This article belongs to the Special Issue Smart Sensors for eHealth Applications)
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Open AccessArticle
Prediction of 30-Day Readmission for COPD Patients Using Accelerometer-Based Activity Monitoring
Sensors 2020, 20(1), 217; https://doi.org/10.3390/s20010217 - 30 Dec 2019
Cited by 3
Abstract
Chronic obstructive pulmonary disease (COPD) claimed 3.0 million lives in 2016 and ranked 3rd among the top 10 global causes of death. Moreover, once diagnosed and discharged from the hospital, the 30-day readmission risk in COPD patients is found to be the highest [...] Read more.
Chronic obstructive pulmonary disease (COPD) claimed 3.0 million lives in 2016 and ranked 3rd among the top 10 global causes of death. Moreover, once diagnosed and discharged from the hospital, the 30-day readmission risk in COPD patients is found to be the highest among all chronic diseases. The existing diagnosis methods, such as Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2019, Body-mass index, airflow Obstruction, Dyspnea, and Exercise (BODE) index, modified Medical Research Council (mMRC), COPD assessment test (CAT), 6-minute walking distance, which are adopted currently by physicians cannot predict the potential readmission of COPD patients, especially within the 30 days after discharge from the hospital. In this paper, a statistical model was proposed to predict the readmission risk of COPD patients within 30-days by monitoring their physical activity (PA) in daily living with accelerometer-based wrist-worn wearable devices. This proposed model was based on our previously reported PA models for activity index (AI) and regularity index (RI) and it introduced a new parameter, quality of activity (QoA), which incorporates previously proposed parameters, such as AI and RI, with other activity-based indices to predict the readmission risk. Data were collected from continuous PA monitoring of 16 COPD patients after hospital discharge as test subjects and readmission prediction criteria were proposed, with a 63% sensitivity and a 37.78% positive prediction rate. Compared to other clinical assessment, diagnosis, and prevention methods, the proposed model showed significant improvement in predicting the 30-day readmission risk. Full article
(This article belongs to the Special Issue Smart Sensors for eHealth Applications)
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Open AccessArticle
Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method
Sensors 2020, 20(1), 167; https://doi.org/10.3390/s20010167 - 27 Dec 2019
Abstract
Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm [...] Read more.
Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm to the bone and cartilage of the joints, weakens the joints’ muscles and tendons, eventually causing joint destruction. Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. In this paper, the classification of medical disorders based on RA and orthopaedics datasets using Ensemble methods are discussed. The RA dataset was gathered from the analysis of white blood cell classification using features extracted from the image of lymphocytes acquired from a digital microscope with an electronic image sensor. The orthopaedic dataset is a benchmark dataset for this study, as it posed a similar classification problem with several numerical features. Three ensemble algorithms such as bagging, Adaboost, and random subspace were used in the study. These ensemble classifiers use k-NN (K-nearest neighbours) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is accessed using holdout and 10-fold cross-validation evaluation methods. The assessment was based on set of performance measures such as precision, recall, F-measure, and receiver operating characteristic (ROC) curve. The performance was also measured based on the comparison of the overall classification accuracy rate between different ensembles classifiers and the base learners. Overall, it was found that for Dataset 1, random subspace classifier with k-NN shows the best results in terms of overall accuracy rate of 97.50% and for Dataset 2, bagging-RF shows the highest overall accuracy rate of 94.84% over different ensemble classifiers. The findings indicate that the efficiency of the base classifiers with ensemble classifier have substantially improved. Full article
(This article belongs to the Special Issue Smart Sensors for eHealth Applications)
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Open AccessArticle
Effect of Closed-Loop Vibration Stimulation on Heart Rhythm during Naps
Sensors 2019, 19(19), 4136; https://doi.org/10.3390/s19194136 - 24 Sep 2019
Cited by 1
Abstract
Sleep plays a primary function for health and sustains physical and cognitive performance. Although various stimulation systems for enhancing sleep have been developed, they are difficult to use on a long-term basis. This paper proposes a novel stimulation system and confirms its feasibility [...] Read more.
Sleep plays a primary function for health and sustains physical and cognitive performance. Although various stimulation systems for enhancing sleep have been developed, they are difficult to use on a long-term basis. This paper proposes a novel stimulation system and confirms its feasibility for sleep. Specifically, in this study, a closed-loop vibration stimulation system that detects the heart rate (HR) and applies −n% stimulus beats per minute (BPM) computed on the basis of the previous 5 min of HR data was developed. Ten subjects participated in the evaluation experiment, in which they took a nap for approximately 90 min. The experiment comprised one baseline and three stimulation conditions. HR variability analysis showed that the normalized low frequency (LF) and LF/high frequency (HF) parameters significantly decreased compared to the baseline condition, while the normalized HF parameter significantly increased under the −3% stimulation condition. In addition, the HR density around the stimulus BPM significantly increased under the −3% stimulation condition. The results confirm that the proposed stimulation system could influence heart rhythm and stabilize the autonomic nervous system. This study thus provides a new stimulation approach to enhance the quality of sleep and has the potential for enhancing health levels through sleep manipulation. Full article
(This article belongs to the Special Issue Smart Sensors for eHealth Applications)
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Open AccessArticle
Classification for Human Balance Capacity Based on Visual Stimulation under a Virtual Reality Environment
Sensors 2019, 19(12), 2738; https://doi.org/10.3390/s19122738 - 18 Jun 2019
Abstract
The normal and disordered people balance ability classification is a key premise for rehabilitation training. This paper proposes a multi-barycentric area model (MBAM), which can be applied for accurate video analysis based classification. First, we have invited fifty-three subjects to wear an HTC [...] Read more.
The normal and disordered people balance ability classification is a key premise for rehabilitation training. This paper proposes a multi-barycentric area model (MBAM), which can be applied for accurate video analysis based classification. First, we have invited fifty-three subjects to wear an HTC (High Tech Computer Corporation) VIVE (Very Immersive Virtual Experience) helmet and to walk ten meters while seeing a virtual environment. The subjects’ motion behaviors are collected as our balance ability classification dataset. Secondly, we use background differential algorithm and bilateral filtering as the preprocessing to alleviate the video noise and motion blur. Inspired by the balance principle of a tumbler, we introduce a MBAM model to describe the body balancing condition by computing the gravity center of a triangle area, which is surrounded by the upper, middle and lower parts of the human body. Finally, we can obtain the projection coordinates according to the center of gravity of the triangle, and get the roadmap of the subjects by connecting those projection coordinates. In the experiments, we adopt four kinds of metrics (the MBAM, the area variance, the roadmap and the walking speed) innumerical analysis to verify the effect of the proposed method. Experimental results show that the proposed method can obtain a more accurate classification for human balance ability. The proposed research may provide potential theoretical support for the clinical diagnosis and treatment for balance dysfunction patients. Full article
(This article belongs to the Special Issue Smart Sensors for eHealth Applications)
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Open AccessArticle
Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning
Sensors 2019, 19(11), 2573; https://doi.org/10.3390/s19112573 - 06 Jun 2019
Cited by 2
Abstract
Magnetic resonance imaging (MRI) offers the most detailed brain structure image available today; it can identify tiny lesions or cerebral cortical abnormalities. The primary purpose of the procedure is to confirm whether there is structural variation that causes epilepsy, such as hippocampal sclerotherapy, [...] Read more.
Magnetic resonance imaging (MRI) offers the most detailed brain structure image available today; it can identify tiny lesions or cerebral cortical abnormalities. The primary purpose of the procedure is to confirm whether there is structural variation that causes epilepsy, such as hippocampal sclerotherapy, local cerebral cortical dysplasia, and cavernous hemangioma. Cerebrovascular disease, the second most common factor of death in the world, is also the fourth leading cause of death in Taiwan, with cerebrovascular disease having the highest rate of stroke. Among the most common are large vascular atherosclerotic lesions, small vascular lesions, and cardiac emboli. The purpose of this thesis is to establish a computer-aided diagnosis system based on small blood vessel lesions in MRI images, using the method of Convolutional Neural Network and deep learning to analyze brain vascular occlusion by analyzing brain MRI images. Blocks can help clinicians more quickly determine the probability and severity of stroke in patients. We analyzed MRI data from 50 patients, including 30 patients with stroke, 17 patients with occlusion but no stroke, and 3 patients with dementia. This system mainly helps doctors find out whether there are cerebral small vessel lesions in the brain MRI images, and to output the found results into labeled images. The marked contents include the position coordinates of the small blood vessel blockage, the block range, the area size, and if it may cause a stroke. Finally, all the MRI images of the patient are synthesized, showing a 3D display of the small blood vessels in the brain to assist the doctor in making a diagnosis or to provide accurate lesion location for the patient. Full article
(This article belongs to the Special Issue Smart Sensors for eHealth Applications)
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Review

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Open AccessReview
IoT Wearable Sensors and Devices in Elderly Care: A Literature Review
Sensors 2020, 20(10), 2826; https://doi.org/10.3390/s20102826 - 16 May 2020
Cited by 6
Abstract
The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer’s disease, frailty, Parkinson’s, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there [...] Read more.
The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer’s disease, frailty, Parkinson’s, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology. Full article
(This article belongs to the Special Issue Smart Sensors for eHealth Applications)
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