Special Issue "Sensing and Signal Processing in Smart Healthcare"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2019).

Special Issue Editors

Prof. Dr. Wenbing Zhao
Website
Guest Editor
Department of Electrical Engineering and Computer Science, Cleveland State University, Ohio, 44011, USA
Interests: human computer interaction; rehabilitation; computer vision; distributed systems
Special Issues and Collections in MDPI journals
Prof. Dr. Srinivas Sampalli
Website
Guest Editor
Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 1W5, Canada
Interests: applications of emerging wireless technologies in healthcare; mobile computing; cyber security; radio frequency identification (RFID); near field communication (NFC)

Special Issue Information

Dear Colleagues,

In the last decade, we have seen the rapid development of electronic technologies that are transforming our daily lives. Such technologies often integrate with various sensors that facilitate the collection of human motion and physiological data, and are equipped with wireless communication modules such as Bluetooth, RFID, and NFC. In this Special Issue we welcome contributions that report research and development towards a smarter healthcare based on various emerging sensing and communication techniques. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial, because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal/data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high confidence level in order for the applications to be useful for clinicians to take diagnosis and treatment decisions.

Prof. Dr. Wenbing Zhao
Prof. Dr. Srinivas Sampalli
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 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

  • Sensing, signal processing
  • machine learning
  • human–computer interaction
  • NFC
  • Bluetooth
  • RFID
  • computer vision
  • image processing
  • data analytics for healthcare
  • healthcare user studies

Published Papers (12 papers)

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Research

Open AccessArticle
BiometricAccessFilter: A Web Control Access System Based on Human Auditory Perception for Children Protection
Electronics 2020, 9(2), 361; https://doi.org/10.3390/electronics9020361 - 21 Feb 2020
Abstract
Along with internet growth, security issues come into existence. Efficient tools to control access and to filter undesirable web content are needed all the time. In this paper, a control access method for web security based on age estimation is proposed, where the [...] Read more.
Along with internet growth, security issues come into existence. Efficient tools to control access and to filter undesirable web content are needed all the time. In this paper, a control access method for web security based on age estimation is proposed, where the correlation between human age and auditory perception is taken into account. In particular, access is denied if a person’s age is not appropriate for the given web content. Unlike existing web access filters, our biometric approach offers greater security and protection to individual privacy. From a technical point of view, the machine-learning regression model is used to estimate the person’s age. The primary contributions of this paper include an age estimation module based on human auditory perception and provision of an open-source web filter to prevent adults from accessing children web applications. The proposed system can also be used to limit the access of children to a webpage specially designed for adults. Our system is evaluated with a dataset collected from 201 persons with different ages from 06 to 60 years old, where it considered 109 male and 82 female volunteers. Results indicate that our system can estimate the age of a person with an accuracy of 97.04% and a root mean square error (RMSE) of 4.2 years. It presents significant performances in the verification scenario with an Equal Error Rate (EER) of 1.4%. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessArticle
Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification
Electronics 2020, 9(1), 135; https://doi.org/10.3390/electronics9010135 - 10 Jan 2020
Cited by 2
Abstract
The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due [...] Read more.
The electrocardiogram (ECG) is a widely used, noninvasive test for analyzing arrhythmia. However, the ECG signal is prone to contamination by different kinds of noise. Such noise may cause deformation on the ECG heartbeat waveform, leading to cardiologists’ mislabeling or misinterpreting heartbeats due to varying types of artifacts and interference. To address this problem, some previous studies propose a computerized technique based on machine learning (ML) to distinguish between normal and abnormal heartbeats. Unfortunately, ML works on a handcrafted, feature-based approach and lacks feature representation. To overcome such drawbacks, deep learning (DL) is proposed in the pre-training and fine-tuning phases to produce an automated feature representation for multi-class classification of arrhythmia conditions. In the pre-training phase, stacked denoising autoencoders (DAEs) and autoencoders (AEs) are used for feature learning; in the fine-tuning phase, deep neural networks (DNNs) are implemented as a classifier. To the best of our knowledge, this research is the first to implement stacked autoencoders by using DAEs and AEs for feature learning in DL. Physionet’s well-known MIT-BIH Arrhythmia Database, as well as the MIT-BIH Noise Stress Test Database (NSTDB). Only four records are used from the NSTDB dataset: 118 24 dB, 118 −6 dB, 119 24 dB, and 119 −6 dB, with two levels of signal-to-noise ratio (SNRs) at 24 dB and −6 dB. In the validation process, six models are compared to select the best DL model. For all fine-tuned hyperparameters, the best model of ECG heartbeat classification achieves an accuracy, sensitivity, specificity, precision, and F1-score of 99.34%, 93.83%, 99.57%, 89.81%, and 91.44%, respectively. As the results demonstrate, the proposed DL model can extract high-level features not only from the training data but also from unseen data. Such a model has good application prospects in clinical practice. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessArticle
Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis
Electronics 2019, 8(7), 812; https://doi.org/10.3390/electronics8070812 - 20 Jul 2019
Cited by 2
Abstract
Sleep healthcare at home is a new research topic that needs to develop new sensors, hardware and algorithms with the consideration of convenience, portability and accuracy. Monitoring sleep behaviors by visual sensors represents one new unobtrusive approach to facilitating sleep monitoring and benefits [...] Read more.
Sleep healthcare at home is a new research topic that needs to develop new sensors, hardware and algorithms with the consideration of convenience, portability and accuracy. Monitoring sleep behaviors by visual sensors represents one new unobtrusive approach to facilitating sleep monitoring and benefits sleep quality. The challenge of video surveillance for sleep behavior analysis is that we have to tackle bad image illumination issue and large pose variations during sleeping. This paper proposes a robust method for sleep pose analysis with human joints model. The method first tackles the illumination variation issue of infrared videos to improve the image quality and help better feature extraction. Image matching by keypoint features is proposed to detect and track the positions of human joints and build a human model robust to occlusion. Sleep poses are then inferred from joint positions by probabilistic reasoning in order to tolerate occluded joints. Experiments are conducted on the video polysomnography data recorded in sleep laboratory. Sleep pose experiments are given to examine the accuracy of joint detection and tacking, and the accuracy of sleep poses. High accuracy of the experiments demonstrates the validity of the proposed method. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessArticle
Learning-Based Screening of Endothelial Dysfunction From Photoplethysmographic Signals
Electronics 2019, 8(3), 271; https://doi.org/10.3390/electronics8030271 - 01 Mar 2019
Cited by 1
Abstract
Endothelial-Dysfunction (ED) screening is of primary importance to early diagnosis cardiovascular diseases. Recently, approaches to ED screening are focusing more and more on photoplethysmography (PPG)-signal analysis, which is performed in a threshold-sensitive way and may not be suitable for tackling the high variability [...] Read more.
Endothelial-Dysfunction (ED) screening is of primary importance to early diagnosis cardiovascular diseases. Recently, approaches to ED screening are focusing more and more on photoplethysmography (PPG)-signal analysis, which is performed in a threshold-sensitive way and may not be suitable for tackling the high variability of PPG signals. The goal of this work was to present an innovative machine-learning (ML) approach to ED screening that could tackle such variability. Two research hypotheses guided this work: (H1) ML can support ED screening by classifying PPG features; and (H2) classification performance can be improved when including also anthropometric features. To investigate H1 and H2, a new dataset was built from 59 subject. The dataset is balanced in terms of subjects with and without ED. Support vector machine (SVM), random forest (RF) and k-nearest neighbors (KNN) classifiers were investigated for feature classification. With the leave-one-out evaluation protocol, the best classification results for H1 were obtained with SVM (accuracy = 71%, recall = 59%). When testing H2, the recall was further improved to 67%. Such results are a promising step for developing a novel and intelligent PPG device to assist clinicians in performing large scale and low cost ED screening. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessArticle
Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits
Electronics 2019, 8(2), 142; https://doi.org/10.3390/electronics8020142 - 29 Jan 2019
Cited by 6
Abstract
Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors [...] Read more.
Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér–Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessFeature PaperArticle
Implementation and Assessment of an Intelligent Motor Tele-Rehabilitation Platform
Electronics 2019, 8(1), 58; https://doi.org/10.3390/electronics8010058 - 04 Jan 2019
Cited by 5
Abstract
Over the past few years, software applications for medical assistance, including tele-rehabilitation, have known an increasing presence in the health arena. Despite the several therapeutic and economic advantages of this new paradigm, it is important to follow certain guidelines, in order to build [...] Read more.
Over the past few years, software applications for medical assistance, including tele-rehabilitation, have known an increasing presence in the health arena. Despite the several therapeutic and economic advantages of this new paradigm, it is important to follow certain guidelines, in order to build a safe, useful, scalable, and ergonomic tool. This work proposes to address all these points, through the case study of a physical tele-rehabilitation platform for patients after hip replacement surgery. The scalability and versatility of the system is handled by the implementation of a modular architecture. The safeness and effectiveness of the tool is ensured by an artificial intelligence module that assesses the quality of the movements performed by the user. The usability of the application is evaluated by a cognitive walkthrough method. Results show that the system (i) is able to properly assess the correctness of the human’s motion through two possible methods (Dynamic Time Warping and Hidden Markov Model), and (ii) provides a good user experience. The discussion addresses (i) the advantages and disadvantages of the main approaches for a gesture recognition of therapeutic movements, and (ii) critical aspects to provide the patient with the best usability of a tele-rehabilitation platform. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessFeature PaperArticle
Spline Function Simulation Data Generation for Walking Motion Using Foot-Mounted Inertial Sensors
Electronics 2019, 8(1), 18; https://doi.org/10.3390/electronics8010018 - 23 Dec 2018
Cited by 1
Abstract
This paper investigates the generation of simulation data for motion estimation using inertial sensors. The smoothing algorithm with waypoint-based map matching is proposed using foot-mounted inertial sensors to estimate position and attitude. The simulation data are generated using spline functions, where the estimated [...] Read more.
This paper investigates the generation of simulation data for motion estimation using inertial sensors. The smoothing algorithm with waypoint-based map matching is proposed using foot-mounted inertial sensors to estimate position and attitude. The simulation data are generated using spline functions, where the estimated position and attitude are used as control points. The attitude is represented using B-spline quaternion and the position is represented by eighth-order algebraic splines. The simulation data can be generated using inertial sensors (accelerometer and gyroscope) without using any additional sensors. Through indoor experiments, two scenarios were examined include 2D walking path (rectangular) and 3D walking path (corridor and stairs) for simulation data generation. The proposed simulation data is used to evaluate the estimation performance with different parameters such as different noise levels and sampling periods. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessFeature PaperArticle
Data-Adaptive Coherent Demodulator for High Dynamics Pulse-Wave Ultrasound Applications
Electronics 2018, 7(12), 434; https://doi.org/10.3390/electronics7120434 - 14 Dec 2018
Cited by 11
Abstract
Pulse-Wave Doppler (PWD) ultrasound has been applied to the detection of blood flow for a long time; recently the same method was also proven effective in the monitoring of industrial fluids and suspensions flowing in pipes. In a PWD investigation, bursts of ultrasounds [...] Read more.
Pulse-Wave Doppler (PWD) ultrasound has been applied to the detection of blood flow for a long time; recently the same method was also proven effective in the monitoring of industrial fluids and suspensions flowing in pipes. In a PWD investigation, bursts of ultrasounds at 0.5–10 MHz are periodically transmitted in the medium under test. The received signal is amplified, sampled at tens of MHz, and digitally processed in a Field Programmable Gate Array (FPGA). First processing step is a coherent demodulation. Unfortunately, the weak echoes reflected from the fluid particles are received together with the echoes from the high-reflective pipe walls, whose amplitude can be 30–40 dB higher. This represents a challenge for the input dynamics of the system and the demodulator, which should clearly detect the weak fluid signal while not saturating at the pipe wall components. In this paper, a numerical demodulator architecture is presented capable of auto-tuning its internal dynamics to adapt to the feature of the actual input signal. The proposed demodulator is integrated into a system for the detection of the velocity profile of fluids flowing in pipes. Simulations and experiments with the system connected to a flow-rig show that the data-adaptive demodulator produces a noise reduction of at least of 20 dB with respect to different approaches, and recovers a correct velocity profile even when the input data are sampled at 8 bits only instead of the typical 12–16 bits. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessFeature PaperArticle
Nonlinearities Associated with Impaired Sensors in a Typical SHM Experimental Set-Up
Electronics 2018, 7(11), 303; https://doi.org/10.3390/electronics7110303 - 06 Nov 2018
Cited by 2
Abstract
Structural Health Monitoring (SHM) gives a diagnosis of a structure assessing the structural integrity and predicting the residual life through appropriate data processing and interpretation. A structure must remain in the design domain, although it can be subjected to normal aging due to [...] Read more.
Structural Health Monitoring (SHM) gives a diagnosis of a structure assessing the structural integrity and predicting the residual life through appropriate data processing and interpretation. A structure must remain in the design domain, although it can be subjected to normal aging due to usage, action of the environment, and accidental events. SHM involves the integration of electronic devices in the inspected structure that sometimes are Piezoelectric Transducers (PZT). These are lightweight and small and can be produced in different geometries. They are used both in guided wave-based and electromechanical impedance-based methods. The PZT bonding requires essential steps such as preparation of the surfaces, application of the adhesive, and assembly that make the bonding process not so easy to be realised. Furthermore, adhesives are susceptible to environmental degradation. Transducer debonding or non-uniform distributed glue underneath the sensor causes the reduction of the performance and can affect the reliability of the SHM system. In this paper, a sensor diagnostic method for the monitoring of the PZT operational status is proposed in order to detect bonding defect/damage between a PZT patch and a host structure. The authors propose a method based on the nonlinear behaviour of the contact PZT/structure that allows the identification of the damaged PZT and the geometrical characterization of the debonding. The feasibility of the diagnostic procedure is demonstrated by numerical studies and experiments, where disbonds were created by inhibiting the adhesive action on a part of the interface through Teflon film. The proposed method can be used to evaluate the sensor functionality after an extreme loading event or over a long period of service time. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessFeature PaperArticle
Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images
Electronics 2018, 7(11), 283; https://doi.org/10.3390/electronics7110283 - 30 Oct 2018
Cited by 8
Abstract
The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and [...] Read more.
The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of ~ 150 × with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessArticle
i-Light—Intelligent Luminaire Based Platform for Home Monitoring and Assisted Living
Electronics 2018, 7(10), 220; https://doi.org/10.3390/electronics7100220 - 28 Sep 2018
Cited by 4
Abstract
We present i-Light, a cyber-physical platform that aims to help older adults to live safely within their own homes. The system is the result of an international research project funded by the European Union and is comprised of a custom developed wireless sensor [...] Read more.
We present i-Light, a cyber-physical platform that aims to help older adults to live safely within their own homes. The system is the result of an international research project funded by the European Union and is comprised of a custom developed wireless sensor network together with software services that provide continuous monitoring, reporting and real-time alerting capabilities. The principal innovation proposed within the project regards implementation of the hardware components in the form of intelligent luminaires with inbuilt sensing and communication capabilities. Custom luminaires provide indoor localisation and environment sensing, are cost-effective and are designed to replace the lighting infrastructure of the deployment location without prior mapping or fingerprinting. We evaluate the system within a home and show that it achieves localisation accuracy sufficient for room-level detection. We present the communication infrastructure, and detail how the software services can be configured and used for visualisation, reporting and real-time alerting. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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Open AccessArticle
A Plug and Play IoT Wi-Fi Smart Home System for Human Monitoring
Electronics 2018, 7(9), 200; https://doi.org/10.3390/electronics7090200 - 16 Sep 2018
Cited by 16
Abstract
The trend toward technology ubiquity in human life is constantly increasing and the same tendency is clear in all technologies aimed at human monitoring. In this framework, several smart home system architectures have been presented in literature, realized by combining sensors, home servers, [...] Read more.
The trend toward technology ubiquity in human life is constantly increasing and the same tendency is clear in all technologies aimed at human monitoring. In this framework, several smart home system architectures have been presented in literature, realized by combining sensors, home servers, and online platforms. In this paper, a new system architecture suitable for human monitoring based on Wi-Fi connectivity is introduced. The proposed solution lowers costs and implementation burden by using the Internet connection that leans on standard home modem-routers, already present normally in the homes, and reducing the need for range extenders thanks to the long range of the Wi-Fi signal. Since the main drawback of the Wi-Fi implementation is the high energy drain, low power design strategies have been considered to provide each battery-powered sensor with a lifetime suitable for a consumer application. Moreover, in order to consider the higher consumption arising in the case of the Wi-Fi/Internet connectivity loss, dedicated operating cycles have been introduced obtaining an energy savings of up to 91%. Performance was evaluated: in order to validate the use of the system as a hardware platform for behavioral services, an activity profile of a user for two months in a real context has been extracted. Full article
(This article belongs to the Special Issue Sensing and Signal Processing in Smart Healthcare)
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