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

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 30699

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Prof. Dr. Isabel De La Torre Díez

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Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
Interests: artificial intelligence; medical images, systems and its variants; fault tolerance; distributed computing; Ad hoc Networks
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Special Issue Information

Dear Colleagues,

Internet of Things (IoT) technology has the potential to revolutionize the delivery of healthcare services. The corporal detection devices in networks, together with sensors in our life environment, allow the continuous and real-time collection of an individual’s health information and their related behavior. Captured in a continuous and aggregated manner, this information must be exploited effectively to allow monitoring, treatments, and interventions in real-time.

The healthcare industry remains one of fastest to adopt the IoT. The main reason for this trend is that the integration of IoT functions in medical devices greatly improves the quality and effectiveness of medical service, providing an especially high value for patients with chronic conditions.

Recent technologies such as IoT have improved the most conservative diagnostic tools of the past decade, such as magnetic resonance imaging, and epigenetic and neuropsychological tests. Bionics, for example, is improved by IoT sensors for the collection of patient data from around the world and big data analysis is used to efficiently diagnose different types of diseases.

The MDPI journal Sensors is soliciting paper submissions, and aims to bring together researchers and application developers working on the intersection of IoT sensors in eHealth such as sensors design and development, distributed, cloud, internet, mobile, ambient, real-time, secure, and privacy-preserving computing related to eHealth. We also aim to explore the application of novel IoT computing results in eHealth.

Topics of Interest

Research topics of interest include (but are not necessary limited to):

The development of smart IoT sensors in eHealth that provide information from and perform actions in the IoT cyber-physical ecosystem;
The discovery and integration of IoT devices in eHealth;
Real-time IoT medical and clinical data analysis on the cloud, at the edge, and on the move, including the localization, personalization, and contextualization of IoT data;
IoT Security and privacy for IoT devices in eHealth with limited computing resources and connectivity;
Wearable IoT devices and systems in eHealth;
Human performance monitoring, human/IoT integration, and IoT information visualization in eHealth.

Application topics of interest include all aspects of, but not limited to, the following:

Intelligent sensing technologies for eHealth;
Internet of Things and cyber-physical systems for eHealth;
Privacy, safety, and security for connectivity;
Data science and data analytics in eHealth;
Big data technologies for eHealth solutions;
Blockchain service for eHealth.

Prof. Dr. Isabel De La Torre Díez
Guest Editor

Manuscript Submission Information

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Keywords

Internet of Things (IoT)
eHealth
sensors
connected devices
security
blockchain in eHealth

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Published Papers (5 papers)

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Research

13 pages, 20166 KiB

Open AccessArticle

Long-Term Home-Monitoring Sensor Technology in Patients with Parkinson’s Disease—Acceptance and Adherence

by Angela Botros, Narayan Schütz, Martin Camenzind, Prabitha Urwyler, Daniel Bolliger, Tim Vanbellingen, Rolf Kistler, Stephan Bohlhalter, Rene M. Müri, Urs P. Mosimann and Tobias Nef

Sensors 2019, 19(23), 5169; https://doi.org/10.3390/s19235169 - 26 Nov 2019

Cited by 50 | Viewed by 4851

Abstract

Parkinson’s disease (PD) is characterized by a highly individual disease-profile as well as fluctuating symptoms. Consequently, 24-h home monitoring in a real-world environment would be an ideal solution for precise symptom diagnostics. In recent years, small lightweight sensors which have assisted in objective, reliable analysis of motor symptoms have attracted a lot of attention. While technical advances are important, patient acceptance of such new systems is just as crucial to increase long-term adherence. So far, there has been a lack of long-term evaluations of PD-patient sensor adherence and acceptance. In a pilot study of PD patients (N = 4), adherence (wearing time) and acceptance (questionnaires) of a multi-part sensor set was evaluated over a 4-week timespan. The evaluated sensor set consisted of 3 body-worn sensors and 7 at-home installed ambient sensors. After one month of continuous monitoring, the overall system usability scale (SUS)-questionnaire score was 71.5%, with an average acceptance score of 87% for the body-worn sensors and 100% for the ambient sensors. On average, sensors were worn 15 h and 4 min per day. All patients reported strong preferences of the sensor set over manual self-reporting methods. Our results coincide with measured high adherence and acceptance rate of similar short-term studies and extend them to long-term monitoring. Full article

(This article belongs to the Special Issue IoT Sensors in E-Health)

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31 pages, 1653 KiB

Open AccessArticle

A Model-Checking-Based Framework for Analyzing Ambient Assisted Living Solutions

by Ashalatha Kunnappilly, Raluca Marinescu and Cristina Seceleanu

Sensors 2019, 19(22), 5057; https://doi.org/10.3390/s19225057 - 19 Nov 2019

Cited by 7 | Viewed by 4074

Abstract

Since modern ambient assisted living solutions integrate a multitude of assisted-living functionalities, out of which some are safety critical, it is desirable that these systems are analyzed at their design stage to detect possible errors. To achieve this, one needs suitable architectures that support the seamless design of the integrated assisted-living functions, as well as capabilities for the formal modeling and analysis of the architecture. In this paper, we attempt to address this need, by proposing a generic integrated ambient assisted living system architecture, consisting of sensors, data collection, local and cloud processing schemes, and an intelligent decision support system, which can be easily extended to suit specific architecture categories. Our solution is customizable, therefore, we show three instantiations of the generic model, as simple, intermediate, and complex configurations, respectively, and show how to analyze the first and third categories by model checking. Our approach starts by specifying the architecture, using an architecture description language, in our case, the Architecture Analysis and Design Language, which can also account for the probabilistic behavior of such systems, and captures the possibility of component failure. To enable formal analysis, we describe the semantics of the simple and complex architectures within the framework of timed automata. We show that the simple architecture is amenable to exhaustive model checking by employing the UPPAAL tool, whereas for the complex architecture we resort to statistical model checking for scalability reasons. In this case, we apply the statistical extension of UPPAAL, namely UPPAAL SMC. Our work paves the way for the development of formally assured future ambient assisted living solutions. Full article

(This article belongs to the Special Issue IoT Sensors in E-Health)

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18 pages, 4144 KiB

Open AccessArticle

An Innovative AAL System Based on IoT Technologies for Patients with Sarcopenia

by Filomena Addante, Federico Gaetani, Luigi Patrono, Daniele Sancarlo, Ilaria Sergi and Giuseppe Vergari

Sensors 2019, 19(22), 4951; https://doi.org/10.3390/s19224951 - 14 Nov 2019

Cited by 37 | Viewed by 5266

Abstract

Sarcopenia is a highly prevalent, age-related muscle disorder associated with adverse outcomes. It is very important from a medical point of view to periodically monitor patients at risk of developing sarcopenia in order to early detect its onset or progression through objective and specific indicators. Today, the emerging Internet of Things (IoT)-enabling technologies allow us to create innovative, wearable, and non-invasive systems that can offer useful clinical support in this area. This work is focused on the use of combined hardware and software technologies, enabling the IoT, in order to monitor people suffering from sarcopenia by offering a high value-added service in the field of the Ambient Assisted Living (AAL). In addition to the description of the proposed system architecture, a validation of the entire system is also included, from both a performance and a functional point of view. Test beds have been carried out by using the independent replications method, and all measurements related to the identified sarcopenia parameters are characterized by a 95% confidence interval with a 5% maximum relative error. The implementation of these technologies as a supporting clinical tool used in a specific setting could significantly impact the life and independence of the sarcopenic frail elderly population. Full article

(This article belongs to the Special Issue IoT Sensors in E-Health)

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16 pages, 2586 KiB

Open AccessArticle

IoT Based Heart Activity Monitoring Using Inductive Sensors

by Adrian Brezulianu, Oana Geman, Marius Dan Zbancioc, Marius Hagan, Cristian Aghion, D. Jude Hemanth and Le Hoang Son

Sensors 2019, 19(15), 3284; https://doi.org/10.3390/s19153284 - 26 Jul 2019

Cited by 48 | Viewed by 7949

Abstract

This paper presents a system dedicated to monitoring the heart activity parameters using Electrocardiography (ECG) mobile devices and a Wearable Heart Monitoring Inductive Sensor (WHMIS) that represents a new method and device, developed by us as an experimental model, used to assess the mechanical activity of the hearth using inductive sensors that are inserted in the fabric of the clothes. Only one inductive sensor is incorporated in the clothes in front of the apex area and it is able to assess the cardiorespiratory activity while in the prior of the art are presented methods that predict sensors arrays which are distributed in more places of the body. The parameters that are assessed are heart data-rate and respiration. The results are considered preliminary in order to prove the feasibility of this method. The main goal of the study is to extract the respiration and the data-rate parameters from the same output signal generated by the inductance-to-number convertor using a proper algorithm. The conceived device is meant to be part of the “wear and forget” equipment dedicated to monitoring the vital signs continuously. Full article

(This article belongs to the Special Issue IoT Sensors in E-Health)

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18 pages, 1642 KiB

Open AccessArticle

IoT-Based Home Monitoring: Supporting Practitioners’ Assessment by Behavioral Analysis

by Niccolò Mora, Ferdinando Grossi, Dario Russo, Paolo Barsocchi, Rui Hu, Thomas Brunschwiler, Bruno Michel, Francesca Cocchi, Enrico Montanari, Stefano Nunziata, Guido Matrella and Paolo Ciampolini

Sensors 2019, 19(14), 3238; https://doi.org/10.3390/s19143238 - 23 Jul 2019

Cited by 32 | Viewed by 6359

Abstract

This paper introduces technical solutions devised to support the Deployment Site - Regione Emilia Romagna (DS-RER) of the ACTIVAGE project. The ACTIVAGE project aims at promoting IoT (Internet of Things)-based solutions for Active and Healthy ageing. DS-RER focuses on improving continuity of care for older adults (65+) suffering from aftereffects of a stroke event. A Wireless Sensor Kit based on Wi-Fi connectivity was suitably engineered and realized to monitor behavioral aspects, possibly relevant to health and wellbeing assessment. This includes bed/rests patterns, toilet usage, room presence and many others. Besides hardware design and validation, cloud-based analytics services are introduced, suitable for automatic extraction of relevant information (trends and anomalies) from raw sensor data streams. The approach is general and applicable to a wider range of use cases; however, for readability’s sake, two simple cases are analyzed, related to bed and toilet usage patterns. In particular, a regression framework is introduced, suitable for detecting trends (long and short-term) and labeling anomalies. A methodology for assessing multi-modal daily behavioral profiles is introduced, based on unsupervised clustering techniques. The proposed framework has been successfully deployed at several real-users’ homes, allowing for its functional validation. Clinical effectiveness will be assessed instead through a Randomized Control Trial study, currently being carried out. Full article

(This article belongs to the Special Issue IoT Sensors in E-Health)

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