Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Mobile Sensors for Healthcare

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (15 August 2021) | Viewed by 51305

Share This Special Issue

Special Issue Editor

Prof. Dr. Bert Arnrich

E-Mail Website
Guest Editor

Connected Healthcare, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany
Interests: pervasive health; Internet-of-Health-Things; wearable computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mobile healthcare aims to pave the way for transforming healthcare systems from purely managing illness to maintaining wellness. Ubiquitous sensing and computing technologies are foreseen as the key enabler for pushing the paradigm shift from the established provider-centric healthcare model to a user-centered and preventive overall lifestyle health management that is available everywhere, anytime, and to anyone. In this Special Issue, we seek novel, innovative, and exciting work in areas including but not limited to:

Sensor-based decision support systems;
Design and evaluation of patient and ambient-related sensors;
Internet-of-Health-Things;
Data fusion from sensors, electronic health records, and genetic profiles;
Challenges surrounding data quality;
Standards and interoperability in mobile healthcare;
Security and privacy issues;
Wellbeing, lifestyle support, and disease prevention;
Smart homes and hospitals, autonomous systems to support independent living;
Clinical applications, validation and evaluation studies;
Telemedicine and mHealth solutions;
Chronic disease and health risk management applications.

Prof. Dr. Bert Arnrich
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 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. 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 2600 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

Internet-of-Health-Things (IoHT)
Decision support
Data fusion
Data quality
Telemedicine
Mobile healthcare

Published Papers (6 papers)

Download All Papers

Research

37 pages, 6339 KiB

Open AccessArticle

Improving Stress Management and Sleep Hygiene in Intelligent Homes

by Asterios Leonidis, Maria Korozi, Eirini Sykianaki, Eleni Tsolakou, Vasilios Kouroumalis, Danai Ioannidi, Andreas Stavridakis, Margherita Antona and Constantine Stephanidis

Sensors 2021, 21(7), 2398; https://doi.org/10.3390/s21072398 - 30 Mar 2021

Cited by 8 | Viewed by 8335

Abstract

High stress levels and sleep deprivation may cause several mental or physical health issues, such as depression, impaired memory, decreased motivation, obesity, etc. The COVID-19 pandemic has produced unprecedented changes in our lives, generating significant stress, and worries about health, social isolation, employment, and finances. To this end, nowadays more than ever, it is crucial to deliver solutions that can help people to manage and control their stress, as well as to reduce sleep disturbances, so as to improve their health and overall quality of life. Technology, and in particular Ambient Intelligence Environments, can help towards that direction, when considering that they are able to understand the needs of their users, identify their behavior, learn their preferences, and act and react in their interest. This work presents two systems that have been designed and developed in the context of an Intelligent Home, namely CaLmi and HypnOS, which aim to assist users that struggle with stress and poor sleep quality, respectively. Both of the systems rely on real-time data collected by wearable devices, as well as contextual information retrieved from the ambient facilities of the Intelligent Home, so as to offer appropriate pervasive relaxation programs (CaLmi) or provide personalized insights regarding sleep hygiene (HypnOS) to the residents. This article will describe the design process that was followed, the functionality of both systems, the results of the user studies that were conducted for the evaluation of their end-user applications, and a discussion about future plans. Full article

(This article belongs to the Special Issue Mobile Sensors for Healthcare)

► Show Figures

Figure 1

15 pages, 6757 KiB

Open AccessArticle

Camera-Based Monitoring of Neck Movements for Cervical Rehabilitation Mobile Applications

by Iosune Salinas-Bueno, Maria Francesca Roig-Maimó, Pau Martínez-Bueso, Katia San-Sebastián-Fernández, Javier Varona and Ramon Mas-Sansó

Sensors 2021, 21(6), 2237; https://doi.org/10.3390/s21062237 - 23 Mar 2021

Cited by 5 | Viewed by 3971

Abstract

Vision-based interfaces are used for monitoring human motion. In particular, camera-based head-trackers interpret the movement of the user’s head for interacting with devices. Neck pain is one of the most important musculoskeletal conditions in prevalence and years lived with disability. A common treatment is therapeutic exercise, which requires high motivation and adherence to treatment. In this work, we conduct an exploratory experiment to validate the use of a non-invasive camera-based head-tracker monitoring neck movements. We do it by means of an exergame for performing the rehabilitation exercises using a mobile device. The experiments performed in order to explore its feasibility were: (1) validate neck’s range of motion (ROM) that the camera-based head-tracker was able to detect; (2) ensure safety application in terms of neck ROM solicitation by the mobile application. Results not only confirmed safety, in terms of ROM requirements for different preset patient profiles, according with the safety parameters previously established, but also determined the effectiveness of the camera-based head-tracker to monitor the neck movements for rehabilitation purposes. Full article

(This article belongs to the Special Issue Mobile Sensors for Healthcare)

► Show Figures

Figure 1

22 pages, 15245 KiB

Open AccessArticle

Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study

by Justin Amadeus Albert, Victor Owolabi, Arnd Gebel, Clemens Markus Brahms, Urs Granacher and Bert Arnrich

Sensors 2020, 20(18), 5104; https://doi.org/10.3390/s20185104 - 08 Sep 2020

Cited by 151 | Viewed by 13328

Abstract

Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people. Full article

(This article belongs to the Special Issue Mobile Sensors for Healthcare)

► Show Figures

Figure 1

28 pages, 7072 KiB

Open AccessArticle

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications

by Lin Zhou, Eric Fischer, Can Tunca, Clemens Markus Brahms, Cem Ersoy, Urs Granacher and Bert Arnrich

Sensors 2020, 20(15), 4090; https://doi.org/10.3390/s20154090 - 22 Jul 2020

Cited by 45 | Viewed by 11406

Abstract

Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data. Full article

(This article belongs to the Special Issue Mobile Sensors for Healthcare)

► Show Figures

Figure 1

22 pages, 1728 KiB

Open AccessArticle

Choosing the Best Sensor Fusion Method: A Machine-Learning Approach

by Ramon F. Brena, Antonio A. Aguileta, Luis A. Trejo, Erik Molino-Minero-Re and Oscar Mayora

Sensors 2020, 20(8), 2350; https://doi.org/10.3390/s20082350 - 20 Apr 2020

Cited by 29 | Viewed by 7038

Abstract

Multi-sensor fusion refers to methods used for combining information coming from several sensors (in some cases, different ones) with the aim to make one sensor compensate for the weaknesses of others or to improve the overall accuracy or the reliability of a decision-making process. Indeed, this area has made progress, and the combined use of several sensors has been so successful that many authors proposed variants of fusion methods, to the point that it is now hard to tell which of them is the best for a given set of sensors and a given application context. To address the issue of choosing an adequate fusion method, we recently proposed a machine-learning data-driven approach able to predict the best merging strategy. This approach uses a meta-data set with the Statistical signatures extracted from data sets of a particular domain, from which we train a prediction model. However, the mentioned work is restricted to the recognition of human activities. In this paper, we propose to extend our previous work to other very different contexts, such as gas detection and grammatical face expression identification, in order to test its generality. The extensions of the method are presented in this paper. Our experimental results show that our extended model predicts the best fusion method well for a given data set, making us able to claim a broad generality for our sensor fusion method. Full article

(This article belongs to the Special Issue Mobile Sensors for Healthcare)

► Show Figures

Figure 1

21 pages, 7075 KiB

Open AccessArticle

Vital Block and Vital Sign Server for ECG and Vital Sign Monitoring in a Portable u-Vital System

by Tae Wuk Bae, Kee Koo Kwon and Kyu Hyung Kim

Sensors 2020, 20(4), 1089; https://doi.org/10.3390/s20041089 - 17 Feb 2020

Cited by 9 | Viewed by 6163

Abstract

An important function in the future healthcare system involves measuring a patient’s vital signs, transmitting the measured vital signs to a smart device or a management server, analyzing it in real-time, and informing the patient or medical staff. Internet of Medical Things (IoMT) incorporates information technology (IT) into patient monitoring device (PMD) and is developing traditional measurement devices into healthcare information systems. In the study, a portable ubiquitous-Vital (u-Vital) system is developed and consists of a Vital Block (VB), a small PMD, and Vital Sign Server (VSS), which stores and manages measured vital signs. Specifically, VBs collect a patient’s electrocardiogram (ECG), blood oxygen saturation (SpO2), non-invasive blood pressure (NiBP), body temperature (BT) in real-time, and the collected vital signs are transmitted to a VSS via wireless protocols such as WiFi and Bluetooth. Additionally, an efficient R-point detection algorithm was also proposed for real-time processing and long-term ECG analysis. Experiments demonstrated the effectiveness of measurement, transmission, and analysis of vital signs in the proposed portable u-Vital system. Full article

(This article belongs to the Special Issue Mobile Sensors for Healthcare)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Mobile Sensors for Healthcare

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (6 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI