Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Wearable Sensors for Digital Health
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Wearable Sensors for Digital Health

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 6990

Special Issue Editors

Prof. Dr. Ali Sunyaev

E-Mail Website
Guest Editor
Institute of Applied Computer Science, Karlsruhe Institute of Technology, Germany
Interests: critical information infrastructures; software and information systems; health IT; cloud computing; DLT
Dr. Ekaterina Kutafina

E-Mail Website
Guest Editor
Uniklinik RWTH Aachen, Aachen, Germany
Interests: mHealth; data analysis; EEG; EMG; machine learning
Prof. Dr. Stephan Jonas

E-Mail Website
Guest Editor
Digital Health Group, Department of Informatics, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
Interests: digital health; wearable sensors; data integration; health prevention

Special Issue Information

Dear Colleagues,

Digitalization is one of the key drivers to improve efficiency, sustainability, and cost-effectiveness in the medical and health sector. Modern sensors—implanted, wearable or ambient—do not only improve data quality during diagnostics, therapy or rehabilitation, but they also allow a shift in the core mechanisms of modern medicine, from clinic-centered to patient-centered medicine. By taking the tools needed for diagnosis from the clinic to homes, patients are empowered to understand their own health status and act accordingly. This means moving away from a symptom-first approach before restoring health toward a comprehensive and personalized prevention and maintenance of good health status. The prerequisites for this shift are miniaturization of sensors, fast and energy-efficient communication, ubiquitous availability of sensors and computational power, as well as advanced data processing and artificial intelligence. All of these fields have made significant progress over the last couple of years, yet overarching projects connecting all components and bringing actual benefit to the patients are still rare.

Therefore, this Special Issue aims to highlight research in the interdisciplinary field of digital health with a focus on wearable sensors and their application covering the whole patient journey, from prevention to clinical care.We look forward to contributions from research groups active in this field.

Prof. Dr. Ali Sunyaev
Dr. Ekaterina Kutafina
Prof. Dr. Stephan Jonas
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. 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

  • digital health
  • wearable sensors
  • personalized prevention
  • mobile health (m-health)
  • e-health
  • home monitoring
  • healthy living
  • healthy aging

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 2727 KiB  
Article
Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor
by Youngsun Kong, Hugo F. Posada-Quintero and Ki H. Chon
Sensors 2021, 21(12), 3956; https://doi.org/10.3390/s21123956 - 08 Jun 2021
Cited by 23 | Viewed by 3169
Abstract
The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We [...] Read more.
The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection. Full article
(This article belongs to the Special Issue Wearable Sensors for Digital Health)
►▼ Show Figures

Figure 1

17 pages, 1596 KiB  
Article
Association between Self-Reported and Accelerometer-Based Estimates of Physical Activity in Portuguese Older Adults
by Célia Domingos, Nadine Correia Santos and José Miguel Pêgo
Sensors 2021, 21(7), 2258; https://doi.org/10.3390/s21072258 - 24 Mar 2021
Cited by 11 | Viewed by 3001
Abstract
Accurate assessment of physical activity (PA) is crucial in interventions promoting it and in studies exploring its association with health status. Currently, there is a wide range of assessment tools available, including subjective and objective measures. This study compared accelerometer-based estimates of PA [...] Read more.
Accurate assessment of physical activity (PA) is crucial in interventions promoting it and in studies exploring its association with health status. Currently, there is a wide range of assessment tools available, including subjective and objective measures. This study compared accelerometer-based estimates of PA with self-report PA data in older adults. Additionally, the associations between PA and health outcomes and PA profiles were analyzed. Participants (n = 110) wore a Xiaomi Mi Band 2® for fifteen consecutive days. Self-reported PA was assessed using the International Physical Activity Questionnaire (IPAQ) and the Yale Physical Activity Survey (YPAS). The Spearman correlation coefficient was used to compare self-reported and accelerometer-measured PA and associations between PA and health. Bland–Altman plots were performed to assess the agreement between methods. Results highlight a large variation between self-reported and Xiaomi Mi Band 2® estimates, with poor general agreement. The highest difference was found for sedentary time. Low positive correlations were observed for IPAQ estimates (sedentary, vigorous, and total PA) and moderate for YPAS vigorous estimates. Finally, self-reported and objectively measured PA associated differently with health outcomes. Summarily, although accelerometry has the advantage of being an accurate method, self-report questionnaires could provide valuable information about the context of the activity. Full article
(This article belongs to the Special Issue Wearable Sensors for Digital Health)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop