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Smart Environment and Healthcare
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Smart Environment and Healthcare

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 45454

Special Issue Editors

Prof. Dr. Subhas Mukhopadhyay

E-Mail Website
Guest Editor
School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: drones; robots; swarm drones; swarm robotics; IoT; smart sensors; mechatronics
Special Issues, Collections and Topics in MDPI journals
Dr. Octavian Postolache

E-Mail Website
Guest Editor
Instituto de Telecomunicações, Universidade de Aveiro Campus Universitário de, R. Santiago, 3810-193 Aveiro, Portugal
Interests: smart sensors; IoT; digital twin; precision agriculture; digital physical therapy
Special Issues, Collections and Topics in MDPI journals
Dr. Nagender Kumar Suryadevara

E-Mail Website
Guest Editor
School of Computer and Information Sciences, University of Hyderabad, Telangana 500046, India
Interests: wireless sensor networks; Internet of Things; time series data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart environments are the fragmentations of smart cities under the topic of the Internet of things. Smart environments and health care contain interoperable thoughts, things, and organizations, which arrange new information and correspondence progressions to improve and update individual fulfillment for people at all stages of life. The smart environment for health care monitors the vital parameters of an individual under a monitoring environment. Smart environment frameworks may be all the more promptly embraced by occupants, if the checking frameworks were composed and created as a uniquely-crafted apparatus and provide suitable help to administer well-being.

Smart environments are an amalgamation of three critical elements: Firstly, the physical segments (sensors and electronic gadgets); secondly, the communication segment used to realize the systems; and third, data analytics via machine learning and data mining. The present progress in smart environments and health care systems are towards hazard-free protection and, in addition, agreeable genuine settings for private-environment conditions.

This Special Issue aims to publish original, significant and visionary papers describing scientific methods and technologies that improve the efficiency, productivity, quality and reliability of smart environments for health care. This Special Issue will provide a broad platform for publishing the many rapid advances that have been currently achieved in the area of assisted living. In this Special Issue, we would like to focus on understanding what should be done to improve sensing awareness in regards to humans for better well-being conditions. Submissions of scientific results from experts in academia and industry, worldwide, are strongly encouraged.

Prof. Dr. Subhas Chandra Mukhopadhyay
Prof. Dr. Nagender Kumar Suryadevara
Prof. Dr. Octavian Postolache
Guest Editors

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 submissions that pass pre-check are 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. Applied Sciences 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 2400 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

  • Smart Home

  • Smart Environment

  • Smart Sensors

  • Wireless Sensor Networks

  • Internet of Things

  • Healthcare

  • Eldercare

  • Independent Living

  • Smart Ageing

  • Healthy Living

  • Wellbeing

  • Wellness

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Editorial

Jump to: Research

3 pages, 171 KiB  
Editorial
Special Issue on Smart Environments and Healthcare
by Subhas C. Mukhopadhyay, Octavian Postolache and Nagender Kumar Suryadevara
Appl. Sci. 2019, 9(7), 1307; https://doi.org/10.3390/app9071307 - 29 Mar 2019
Cited by 1 | Viewed by 2459
Abstract
Smart environments are the fragmentations of smart cities under the topic of the Internet of Things [...] Full article
(This article belongs to the Special Issue Smart Environment and Healthcare)

Research

Jump to: Editorial

16 pages, 1597 KiB  
Article
Relay-Enabled Task Offloading Management for Wireless Body Area Networks
by Yangzhe Liao, Quan Yu, Yi Han and Mark S. Leeson
Appl. Sci. 2018, 8(8), 1409; https://doi.org/10.3390/app8081409 - 20 Aug 2018
Cited by 14 | Viewed by 4364
Abstract
Inspired by the recent developments of the Internet of Things (IoT) relay and mobile edge computing (MEC), a hospital/home-based medical monitoring framework is proposed, in which the intensive computing tasks from the implanted sensors can be efficiently executed by on-body wearable devices or [...] Read more.
Inspired by the recent developments of the Internet of Things (IoT) relay and mobile edge computing (MEC), a hospital/home-based medical monitoring framework is proposed, in which the intensive computing tasks from the implanted sensors can be efficiently executed by on-body wearable devices or a coordinator-based MEC (C-MEC). In this paper, we first propose a wireless relay-enabled task offloading mechanism that consists of a network model and a computation model. Moreover, to manage the computation resources among all relays, a task offloading decision model and the best task offloading recipient selection function is given. The performance evaluation considers different computation schemes under the predetermined link quality condition regarding the selected vital quality of service (QoS) metrics. After demonstrating the channel characterization and network topology, the performance evaluation is implemented under different scenarios regarding the network lifetime of all relays, network residual energy status, total number of locally executed packets, path loss (PL), and service delay. The results show that data transmission without the offloading scheme outperforms the offload-based technique regarding network lifetime. Moreover, the high computation capacity scenario achieves better performance regarding PL and the total number of locally executed packets. Full article
(This article belongs to the Special Issue Smart Environment and Healthcare)
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20 pages, 3261 KiB  
Article
An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
by Shaobo Li, Yong Yao, Jie Hu, Guokai Liu, Xuemei Yao and Jianjun Hu
Appl. Sci. 2018, 8(7), 1152; https://doi.org/10.3390/app8071152 - 15 Jul 2018
Cited by 100 | Viewed by 9101
Abstract
Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this [...] Read more.
Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms. Full article
(This article belongs to the Special Issue Smart Environment and Healthcare)
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11 pages, 4191 KiB  
Article
Development of an E-Health App for Lower Limb Postoperative Rehabilitation Based on Plantar Pressure Analysis
by Xiao Cheng, Xin Mei, Yue Hu, Yinfang Fang, Shuai Wu, Fengxiang You and Shaolong Kuang
Appl. Sci. 2018, 8(5), 766; https://doi.org/10.3390/app8050766 - 11 May 2018
Cited by 7 | Viewed by 3572
Abstract
The traditional postoperative rehabilitation training mode of lower limbs is mostly confined to hospitals or nursing sites. With the increase of postoperative patients, the current shortage of medical resources is obviously not satisfactory, and the medical costs are high, thus it is difficult [...] Read more.
The traditional postoperative rehabilitation training mode of lower limbs is mostly confined to hospitals or nursing sites. With the increase of postoperative patients, the current shortage of medical resources is obviously not satisfactory, and the medical costs are high, thus it is difficult to apply widely. A new mobile phone application (app) based on plantar pressure analysis is developed to fulfill the requirements of remote postoperative rehabilitation. It is designed, implemented, tested, and used for pilot experiment in conjunction with the system design methodology of the waterfall model. Preliminary testing and a pilot experiment showed that the app has realized basic functions and can achieve patient rehabilitation out of hospitals. The development of the app can shorten the hospitalization time of patients, reduce medical costs, and make up for the current shortage of medical resources. In the future, more experiments will be done to verify the effectiveness of the app. Full article
(This article belongs to the Special Issue Smart Environment and Healthcare)
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12 pages, 20061 KiB  
Article
Fabrication and Characterization of Medical Mesh-Nebulizer for Aerosol Drug Delivery
by Kyong-Hoon Choi, Sang-Hyub Moon, Su-Kang Park, Guangsup Cho, Ki Chang Nam and Bong Joo Park
Appl. Sci. 2018, 8(4), 604; https://doi.org/10.3390/app8040604 - 11 Apr 2018
Cited by 14 | Viewed by 11540
Abstract
In the field of drug delivery, a nebulizer is a device used to convert liquid drugs into tiny airborne droplets, such as aerosol or a mist form. These fine droplets are delivered to a patient’s lungs and airways and then spread throughout the [...] Read more.
In the field of drug delivery, a nebulizer is a device used to convert liquid drugs into tiny airborne droplets, such as aerosol or a mist form. These fine droplets are delivered to a patient’s lungs and airways and then spread throughout the body via blood vessels. Therefore, nebulization therapy is a highly-effective method compared with existing drug delivery methods. To enhance the curative influence of a drug, this study suggests the use of a new micro-porous mesh nebulizer consisting of a controllable palladium–nickel (Pd–Ni) membrane filter, piezoelectric element, and a cavity in the micro-pump. In this research, we optimize a biocompatible Pd–Ni membrane filter, such that it generated the smallest aerosol particles of various drugs. The pore size of the filter outlet is 4.2 μm ± 0.15 μm and the thickness of the Pd-Ni membrane filter is approximately 41.5 μm. In addition, the Pd–Ni membrane filter has good biocompatibility with normal cells. The result of a spray test with deionized (DI) water indicated that the size of a standard liquid droplet is 4.53 μm. The device has an electrical requirement, with a low power consumption of 2.5 W, and an optimal operation frequency of 98.5 kHz. Full article
(This article belongs to the Special Issue Smart Environment and Healthcare)
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3375 KiB  
Article
Wearable Plasma Pads for Biomedical Applications
by Junggil Kim, Kyong-Hoon Choi, Yunjung Kim, Bong Joo Park and Guangsup Cho
Appl. Sci. 2017, 7(12), 1308; https://doi.org/10.3390/app7121308 - 17 Dec 2017
Cited by 26 | Viewed by 7658
Abstract
A plasma pad that can be attached to human skin was developed for aesthetic and dermatological treatment. A polyimide film was used for the dielectric layer of the flexible pad, and high-voltage and ground electrodes were placed on the film surface. Medical gauze [...] Read more.
A plasma pad that can be attached to human skin was developed for aesthetic and dermatological treatment. A polyimide film was used for the dielectric layer of the flexible pad, and high-voltage and ground electrodes were placed on the film surface. Medical gauze covered the ground electrodes and was placed facing the skin to act as a spacer; thus, the plasma floated between the gauze and ground electrodes. In vitro and in vivo biocompatibility tests of the pad showed no cytotoxicity to normal cells and no irritation of mouse skin. Antibacterial activity was shown against Staphylococcus aureus and clinical isolates of methicillin-resistant S. aureus. Furthermore, skin wound healing with increased hair growth resulting from increased exogenous nitric oxide and capillary tube formation induced by the plasma pad was also confirmed in vivo. The present study suggests that this flexible and wearable plasma pad can be used for biomedical applications such as treatment of wounds and bacterial infections. Full article
(This article belongs to the Special Issue Smart Environment and Healthcare)
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5077 KiB  
Article
ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method
by Woo-Hyuk Jung and Sang-Goog Lee
Appl. Sci. 2017, 7(11), 1205; https://doi.org/10.3390/app7111205 - 22 Nov 2017
Cited by 31 | Viewed by 5471
Abstract
This study proposes electrocardiogram (ECG) identification based on non-fiducial feature extraction using window removal method, nearest neighbor (NN), support vector machine (SVM), and linear discriminant analysis (LDA). In the pre-processing stage, Daubechies 4 is used to remove the baseline wander and noise of [...] Read more.
This study proposes electrocardiogram (ECG) identification based on non-fiducial feature extraction using window removal method, nearest neighbor (NN), support vector machine (SVM), and linear discriminant analysis (LDA). In the pre-processing stage, Daubechies 4 is used to remove the baseline wander and noise of the original signal. In the feature extraction and selection stage, windows are set at a time interval of 5 s in the preprocessed signal, while autocorrelation, scaling, and discrete cosine transform (DCT) are applied to extract and select features. Thereafter, the window removal method is applied to all of the generated windows to remove those that are unrecognizable. Lastly, in the classification stage, the NN, SVM, and LDA classifiers are used to perform individual identification. As a result, when the NN is used in the Normal Sinus Rhythm (NSR), PTB diagnostic, and QT database, the results indicate that the subject identification rates are 100%, 99.40% and 100%, while the window identification rates are 99.02%, 97.13% and 98.91%. When the SVM is used, all of the subject identification rates are 100%, while the window identification rates are 96.92%, 95.82% and 98.32%. When the LDA is used, all of the subject identification rates are 100%, while the window identification rates are 98.67%, 98.65% and 99.23%. The proposed method demonstrates good results with regard to data that not only includes normal signals, but also abnormal signals. In addition, the window removal method improves the individual identification accuracy by removing windows that cannot be recognized. Full article
(This article belongs to the Special Issue Smart Environment and Healthcare)
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