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Data Processing and Wearable Systems for Effective Human Monitoring

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 92138

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Special Issue Editors

Dr. Antonio Lanatà

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Department of Information Engineering, School of Engineering, University of Pisa, Via Caruso 16, 56122 Pisa, Italy
Interests: biomedical system and signal processing; acoustics; UWB systems; artificial intelligence; affective computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Alberto Greco

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Department of Information Engineering and Research Centre “E. Piaggio”, School of Engineering, University of Pisa, Pisa, Italy
Interests: biomedical signal processing; physiological modeling; wearable monitoring systems; wearable sensors; Bayesian modeling; machine learning; electrodermal activity; cardiovascular modeling; neuroscience; embedded systems; human-machine interaction
Special Issues, Collections and Topics in MDPI journals

Dr. Nicola Vanello

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Guest Editor

Dipartimento di Ingegneria dell’Informazione, University of Pisa, 56126 Pisa, Italy
Interests: biomedical signal processing; fMRI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last few decades have seen an unrestrained development of wearable and mobile technologies, such as smartphones, smartwatches, lightweight sensors, displays, cameras and interfaces for monitoring physiological signs, human behavior, social interaction, cognitive skills, lifestyles, etc.

These devices, that are now equipped with a significant amount of computational power, low-energy wireless communication, long-life battery and large-memory storage, allow novel avenues in the human-centered concept of enlarging the single-user model to a collaborative and distributed network. However, with regard to the current proposal of new miniaturized hardware devices and systems, issues surrounding processing, visualization, and storage of such large amount of available data (including heterogeneous physiological signals) are associated with the challenging discovery of hidden information. In this context, ad-hoc methodologies for data processing and synthesis, as well as ad-hoc experimental paradigms, system design, and models, are strongly needed.

This Special Issue aims at bringing together the significant, cutting-edge research findings in the field of wearable and mobile sensor design and signal processing to manage with data acquisition and processing issues in lab-settings and real ecological scenarios.

Relevant topics may include, but are not limited to, the following:

Wearable and mobile systems for physiological signal monitoring,
Wearable and mobile sensor fusion paradigm,
Embedded signal processing,
Signal processing of physiological signals in a lab-setting and real-scenarios,
Collective experience,
Problem solving,
Pattern recognition techniques for big heterogeneous physiological data
Big data analysis,
Modeling and analysis of data in virtual reality,
Systems, modeling and analysis of data for e-learning,
Modeling and analysis of heterogeneous data for personalized health management,
Data analysis for affective computing,
Speech acquisition system and data processing,
Social signal processing.

Dr. Antonio Lanatà
Dr. Alberto Greco
Dr. Nicola Vanello
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. Electronics 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.

Published Papers (13 papers)

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Editorial

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4 pages, 154 KiB

Open AccessEditorial

Data Processing and Wearable Systems for Effective Human Monitoring

by Alberto Greco, Antonio Lanata and Nicola Vanello

Electronics 2019, 8(9), 1003; https://doi.org/10.3390/electronics8091003 - 08 Sep 2019

Cited by 1 | Viewed by 1695

Abstract

The last few decades have seen an unrestrained diffusion of smart-integrated technologies that are extremely pervasive and customized based on humans’ environments and habits [...] Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

Research

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29 pages, 13750 KiB

Open AccessArticle

Ontology-Based Model to Support Ubiquitous Healthcare Systems for COPD Patients

by Hicham Ajami and Hamid Mcheick

Electronics 2018, 7(12), 371; https://doi.org/10.3390/electronics7120371 - 02 Dec 2018

Cited by 29 | Viewed by 9273

Abstract

Over the past 30 years, information technology has gradually transformed the way health care is provisioned for patients. Chronic Obstructive Pulmonary Disease (COPD) is an incurable malady that threatens the lives of millions around the world. The huge amount of medical information in terms of complex interdependence between progression of health problems and various other factors makes the representation of data more challenging. This study investigated how formal semantic standards could be used for building an ontology knowledge repository to provide ubiquitous healthcare and medical recommendations for COPD patient to reduce preventable harm. The novel contribution of the suggested framework resides in the patient-centered monitoring approach, as we work to create dynamic adaptive protection services according to the current context of patient. This work executes a sequential modular approach consisting of patient, disease, location, devices, activities, environment and services to deliver personalized real-time medical care for COPD patients. The main benefits of this project are: (1) adhering to dynamic safe boundaries for the vital signs, which may vary depending on multiple factors; (2) assessing environmental risk factors; and (3) evaluating the patient’s daily activities through scheduled events to avoid potentially dangerous situations. This solution implements an interrelated set of ontologies with a logical base of Semantic Web Rule Language (SWRL) rules derived from the medical guidelines and expert pneumologists to handle all contextual situations. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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11 pages, 3056 KiB

Open AccessArticle

Wireless LAN-Based CSI Monitoring System for Object Detection

by Tomoki Murakami, Masahiko Miyazaki, Shigemi Ishida and Akira Fukuda

Electronics 2018, 7(11), 290; https://doi.org/10.3390/electronics7110290 - 01 Nov 2018

Cited by 18 | Viewed by 7280

Abstract

Sensing services for the detection of humans and animals by analyzing the environmental changes of wireless local area network (WLAN) signals have attracted attention in recent years. In object detection using WLAN signals, a widely known technique is the use of time changes in received signal strength indicators that are easily measured between WLAN devices. Utilizing channel response, including power and phase values per subcarrier on multiple input multiple output (MIMO), the orthogonal frequency division multiplexing transmission was researched as channel state information (CSI) to further improve detection accuracy. This paper describes a WLAN-based CSI monitoring system that efficiently acquires the CSI of multiple links in a target area where multiple CSI measuring stations are distributed. In the system, a novel CSI monitoring station captures wireless packets sent within the area and extracts CSI by analyzing the packets on the sounding protocol, specified by IEEE 802.11ac. The paper also describes the system configuration and shows that indoor experimental measurements confirmed the system’s feasibility. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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14 pages, 1735 KiB

Open AccessArticle

Automatic Detection of Atrial Fibrillation and Other Arrhythmias in ECG Recordings Acquired by a Smartphone Device

by Lucia Billeci, Magda Costi, David Lombardi, Franco Chiarugi and Maurizio Varanini

Electronics 2018, 7(9), 199; https://doi.org/10.3390/electronics7090199 - 16 Sep 2018

Cited by 16 | Viewed by 5105 | Correction

Abstract

Atrial fibrillation (AF) is the most common cardiac disease and is associated with other cardiac complications. Few attempts have been made for discriminating AF from other arrhythmias and noise. The aim of this study is to present a novel approach for such a classification in short ECG recordings acquired using a smartphone device. The implemented algorithm was tested on the Physionet Computing in Cardiology Challenge 2017 Database and, for the purpose of comparison, on the MIT-BH AF database. After feature extraction, the stepwise linear discriminant analysis for feature selection was used. The Least Square Support Vector Machine classifier was trained and cross-validated on the available dataset of the Challenge 2017. The best performance was obtained with a total of 30 features. The algorithm produced the following performance: F₁ Normal rhythm = 0.92; F₁ AF rhythm: 0.82; F₁ Other rhythm = 0.75; Global F₁ = 0.83, obtaining the third best result in the follow-up phase of the Physionet Challenge. On the MIT-BH ADF database the algorithm gave the following performance: F₁ Normal rhythm = 0.98; F₁ AF rhythm: 0.99; Global F₁ = 0.98. Since the algorithm reliably detect AF and other rhythms in smartphone ECG recordings, it could be applied for personal health monitoring systems. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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13 pages, 999 KiB

Open AccessFeature PaperArticle

Cost-Effective eHealth System Based on a Multi-Sensor System-on-Chip Platform and Data Fusion in Cloud for Sport Activity Monitoring

by Jaume Segura-Garcia, Miguel Garcia-Pineda, Marta Tamarit-Tronch, Rosa M. Cibrian and Rosario Salvador-Palmer

Electronics 2018, 7(9), 183; https://doi.org/10.3390/electronics7090183 - 09 Sep 2018

Cited by 9 | Viewed by 4044

Abstract

eHealth systems provide medical support to users and contribute to the development of mobile and quality health care. They also provide results on the prevention and follow-up of diseases by monitoring health-status indicators and methodical data gathering in patients. Telematic management of health services by means of the Internet of Things provides immediate support and it is cheaper than conventional physical presence methods. Currently, wireless communications and sensor networks allow a person or group to be monitored remotely. The aim of this paper is to develop and assess a system for monitoring physiological parameters to be applied in different scenarios, such as health or sports. This system is based on a distributed architecture, where physiological data of a person are collected by several sensors; next, a Raspberry Pi joins the information and makes a standardization process; then, these data are sent to the Cloud to be processed. Our Cloud system stores the received data and makes a data fusion process in order to indicate the athlete’s fatigue status at every moment. This system has been tested in collaboration with a small group of voluntary tri-athletes. A network simulation has been performed to plan a monitoring network for a bigger group of athletes. Finally, we have found that this system is useful for medium-term monitoring of the sports activities. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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24 pages, 2128 KiB

Open AccessArticle

Closing the Wearable Gap: Mobile Systems for Kinematic Signal Monitoring of the Foot and Ankle

by Tony Luczak, David Saucier, Reuben F. Burch V., John E. Ball, Harish Chander, Adam Knight, Pan Wei and Tashfin Iftekhar

Electronics 2018, 7(7), 117; https://doi.org/10.3390/electronics7070117 - 18 Jul 2018

Cited by 28 | Viewed by 6342

Abstract

Interviews from strength and conditioning coaches across all levels of athletic competition identified their two biggest concerns with the current state of wearable technology: (a) the lack of solutions that accurately capture data “from the ground up” and (b) the lack of trust due to inconsistent measurements. The purpose of this research is to investigate the use of liquid metal sensors, specifically Liquid Wire sensors, as a potential solution for accurately capturing ankle complex movements such as plantar flexion, dorsiflexion, inversion, and eversion. Sensor stretch linearity was validated using a Micro-Ohm Meter and a Wheatstone bridge circuit. Sensors made from different substrates were also tested and discovered to be linear at multiple temperatures. An ankle complex model and computing unit for measuring resistance values were developed to determine sensor output based on simulated plantar flexion movement. The sensors were found to have a significant relationship between the positional change and the resistance values for plantar flexion movement. The results of the study ultimately confirm the researchers’ hypothesis that liquid metal sensors, and Liquid Wire sensors specifically, can serve as a mitigating substitute for inertial measurement unit (IMU) based solutions that attempt to capture specific joint angles and movements. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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9 pages, 2493 KiB

Open AccessArticle

Method of Estimating Human Orientation Using Array Antenna

by Nobuyuki Shiraki, Zhixiong Chen, Dai Sasakawa, Naoki Honma, Takeshi Nakayama and Shoichi Iizuka

Electronics 2018, 7(6), 92; https://doi.org/10.3390/electronics7060092 - 07 Jun 2018

Cited by 3 | Viewed by 3698

Abstract

This paper presents a method that uses microwaves to estimate human body orientation. The antennas are arranged to surround the human and observe vital signs such as respiration and heart beat from the microwaves reflected from the human. Since the signal reflected from the front of the human will fluctuate the most, mainly due to respiration, human body orientation is estimated by finding the antenna that captures the largest rhythmic fluctuation. In experiments with three subjects, the median value of angular error of human orientation was 9.01∼23.35°. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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17 pages, 54912 KiB

Open AccessArticle

Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People

by Mete Yağanoğlu and Cemal Köse

Electronics 2018, 7(4), 50; https://doi.org/10.3390/electronics7040050 - 06 Apr 2018

Cited by 22 | Viewed by 10053

Abstract

Hearing-impaired people do not hear indoor and outdoor environment sounds, which are important for them both at home and outside. By means of a wearable device that we have developed, a hearing-impaired person will be informed of important sounds through vibrations, thereby understanding what kind of sound it is. Our system, which operates in real time, can achieve a success rate of 98% when estimating a door bell ringing sound, 99% success identifying an alarm sound, 99% success identifying a phone ringing, 91% success identifying honking, 93% success identifying brake sounds, 96% success identifying dog sounds, 97% success identifying human voice, and 96% success identifying other sounds using the audio fingerprint method. Audio fingerprint is a brief summary of an audio file, perceptively summarizing a piece of audio content. In this study, our wearable device is tested 100 times a day for 100 days on five deaf persons and 50 persons with normal hearing whose ears were covered by earphones that provided wind sounds. This study aims to improve the quality of life of deaf persons, and provide them a more prosperous life. In the questionnaire performed, deaf people rate the clarity of the system at 90%, usefulness at 97%, and the likelihood of using this device again at 100%. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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13 pages, 1516 KiB

Open AccessArticle

Human Posture Identification Using a MIMO Array

by Dai Sasakawa, Naoki Honma, Takeshi Nakayama and Shoichi Iizuka

Electronics 2018, 7(3), 37; https://doi.org/10.3390/electronics7030037 - 08 Mar 2018

Cited by 15 | Viewed by 5077

Abstract

The elderly are constantly in danger of falling and injuring themselves without anyone realizing it. A safety-monitoring system based on microwaves can ease these concerns. The authors have proposed safety-monitoring systems that use multiple-input multiple-output (MIMO) radar to localize persons by capturing their biological activities such as respiration. However, our studies to date have focused on localization, which is easier to achieve than an estimation of human postures. This paper proposes a human posture identification scheme based on height and a Doppler radar cross section (RCS) as estimated by a MIMO array. This scheme allows smart home applications to dispense with contact and wearable devices. Experiments demonstrate that this method can identify the supine position (i.e., after a fall) with 100% accuracy, and the average identification rate is 95.0%. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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2671 KiB

Open AccessFeature PaperArticle

Correction of the Unobtrusive ECG Using System Identification

by Anna Boehm, Xinchi Yu, Steffen Leonhardt and Daniel Teichmann

Electronics 2017, 6(4), 94; https://doi.org/10.3390/electronics6040094 - 07 Nov 2017

Cited by 1 | Viewed by 5815

Abstract

Unobtrusively acquired electrocardiograms (ECG) could substantially improve the comfort of patients. However, such ECGs are not used in clinical practice because (among other reasons) signal deformations impede correct diagnosis of the ECG. Here, methods are proposed for correction of the unobtrusive ECG, based on system identification. Knowing the reference ECG, models were developed to correct the unobtrusively acquired ECG. A finite impulse response (FIR) model, a state space model and an autoregressive model were developed. The models were trained and evaluated on the Goldberger leads recorded from an ECG T-shirt with dry electrodes, and from a gold standard ECG. It was found that the FIR model corrects the unobtrusive ECG with good agreement (

ρ_{aVR}

= 0.84 ± 0.10,

ρ_{aVL}

= 0.65 ± 0.24,

ρ_{aVF}

= 0.88 ± 0.04), while the other models do not yield significant improvements, or become unstable. The R-peaks were also accurately corrected by the FIR model (

{MSE}_{aVR}

= 0.10 ± 0.10,

{MSE}_{aVL}

= 0.14 ± 0.27,

{MSE}_{aVF}

= 0.03 ± 0.02). To conclude, the proposed FIR method succeeded in significantly correcting the unobtrusive ECG signal. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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956 KiB

Open AccessFeature PaperArticle

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

by Giacomo Cappon, Giada Acciaroli, Martina Vettoretti, Andrea Facchinetti and Giovanni Sparacino

Electronics 2017, 6(3), 65; https://doi.org/10.3390/electronics6030065 - 05 Sep 2017

Cited by 163 | Viewed by 24013

Abstract

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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494 KiB

Open AccessArticle

On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition

by Kimiaki Shirahama and Marcin Grzegorzek

Electronics 2017, 6(2), 44; https://doi.org/10.3390/electronics6020044 - 01 Jun 2017

Cited by 20 | Viewed by 6144

Abstract

With the recent spread of mobile devices equipped with different sensors, it is possible to continuously recognise and monitor activities in daily life. This sensor-based human activity recognition is formulated as sequence classification to categorise sequences of sensor values into appropriate activity classes. One crucial problem is how to model features that can precisely represent characteristics of each sequence and lead to accurate recognition. It is laborious and/or difficult to hand-craft such features based on prior knowledge and manual investigation about sensor data. To overcome this, we focus on a feature learning approach that extracts useful features from a large amount of data. In particular, we adopt a simple but effective one, called codebook approach, which groups numerous subsequences collected from sequences into clusters. Each cluster centre is called a codeword and represents a statistically distinctive subsequence. Then, a sequence is encoded as a feature expressing the distribution of codewords. The extensive experiments on different recognition tasks for physical, mental and eye-based activities validate the effectiveness, generality and usability of the codebook approach. Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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Other

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2 pages, 174 KiB

Open AccessCorrection

Correction: Billeci, L.; et al. Automatic Detection of Atrial Fibrillation and Other Arrhythmias in ECG Recordings Acquired by a Smartphone Device. Electronics 2018, 7, 199

by Lucia Billeci, Magda Costi, David Lombardi, Franco Chiarugi and Maurizio Varanini

Electronics 2018, 7(10), 248; https://doi.org/10.3390/electronics7100248 - 12 Oct 2018

Viewed by 2240

Abstract

The authors wish to make the following corrections to our published paper [1].[...] Full article

(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)

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