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Ambient, Wearable Sensor, Gateway Support for Telemonitoring, Telehealth and Telemedicine Systems

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2654

Special Issue Editor


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Guest Editor
Department of Electrical and Computer Engineering, Aarhus University, 8200 Aarhus, Denmark
Interests: pervasive and biomedical systems engineering; telemonitoring; telehealth; telemedicine

Special Issue Information

Dear Colleagues,

Advances in science and technology and the resulting improvements in environmental and social conditions have increased life expectancy around the world. The result is rapid population aging, which has major implications for all facets of human life, including on the health and wellbeing of elderly people and their relatives. Most notably, as we age, the incidence and prevalence of chronic diseases increases, straining existing health and care organizations, as more staff and resources are needed to counter the growing numbers of chronic patients. Information technology in the fields of telemonitoring, telehealth, and telemedicine has already proven its potential to reduce the costs of health and care organizations through digitally driven optimization of workflows. However, many of the technologies reaching clinical validation studies rely on a limited level of data collection, often provided by the user manually, or from one or a few sensors only. Combining data from many sensors and gateways, both wearable and ambient, could allow telemonitoring, telehealth, and telemedicine systems to become more reliable, responsive, and user-friendly, as well as allowing us novel insights into the cause and effect of chronic conditions.

This Special Issue invites researchers to submit their work in the field, identifying existing and potentially new avenues for the use and/or development of relevant ambient and wearable sensor and gateway technologies and infrastructures, including work that utilizes several sensors, alone or combined, in a non-invasive way.

Dr. Stefan Wagner
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

  • telemonitoring 
  • telehealth 
  • telemedicine 
  • wearable 
  • ambient sensors
  • smart sensors 
  • sensor fusion 
  • wearable electronic devices 
  • wireless sensor for wearables 
  • body area network 
  • wearable devices and systems 
  • activity monitoring devices and systems 
  • wearable imaging 
  • health data acquisition 
  • Internet of Things (IoT) 
  • ambient assisted living 
  • active and healthy aging 
  • pervasive computing 
  • pervasive systems
  • clinical validation

Published Papers (2 papers)

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Research

21 pages, 4587 KiB  
Article
Heart Rate Variability and Pulse Rate Variability: Do Anatomical Location and Sampling Rate Matter?
by Joel S. Burma, James K. Griffiths, Andrew P. Lapointe, Ibukunoluwa K. Oni, Ateyeh Soroush, Joseph Carere, Jonathan D. Smirl and Jeff F. Dunn
Sensors 2024, 24(7), 2048; https://doi.org/10.3390/s24072048 - 23 Mar 2024
Viewed by 1374
Abstract
Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample [...] Read more.
Wearable technology and neuroimaging equipment using photoplethysmography (PPG) have become increasingly popularized in recent years. Several investigations deriving pulse rate variability (PRV) from PPG have demonstrated that a slight bias exists compared to concurrent heart rate variability (HRV) estimates. PPG devices commonly sample at ~20–100 Hz, where the minimum sampling frequency to derive valid PRV metrics is unknown. Further, due to different autonomic innervation, it is unknown if PRV metrics are harmonious between the cerebral and peripheral vasculature. Cardiac activity via electrocardiography (ECG) and PPG were obtained concurrently in 54 participants (29 females) in an upright orthostatic position. PPG data were collected at three anatomical locations: left third phalanx, middle cerebral artery, and posterior cerebral artery using a Finapres NOVA device and transcranial Doppler ultrasound. Data were sampled for five minutes at 1000 Hz and downsampled to frequencies ranging from 20 to 500 Hz. HRV (via ECG) and PRV (via PPG) were quantified and compared at 1000 Hz using Bland–Altman plots and coefficient of variation (CoV). A sampling frequency of ~100–200 Hz was required to produce PRV metrics with a bias of less than 2%, while a sampling rate of ~40–50 Hz elicited a bias smaller than 20%. At 1000 Hz, time- and frequency-domain PRV measures were slightly elevated compared to those derived from HRV (mean bias: ~1–8%). In conjunction with previous reports, PRV and HRV were not surrogate biomarkers due to the different nature of the collected waveforms. Nevertheless, PRV estimates displayed greater validity at a lower sampling rate compared to HRV estimates. Full article
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12 pages, 1858 KiB  
Article
Sensor-Integrated Chairs for Lower Body Strength and Endurance Assessment
by Alexander W. Lee, Melissa S. Lee, Daniel P. Yeh and Hsi-Jen J. Yeh
Sensors 2024, 24(3), 788; https://doi.org/10.3390/s24030788 - 25 Jan 2024
Viewed by 899
Abstract
This paper describes an automated method and device to conduct the Chair Stand Tests of the Fullerton Functional Test Battery. The Fullerton Functional Test is a suite of physical tests designed to assess the physical fitness of older adults. The Chair Stand Tests, [...] Read more.
This paper describes an automated method and device to conduct the Chair Stand Tests of the Fullerton Functional Test Battery. The Fullerton Functional Test is a suite of physical tests designed to assess the physical fitness of older adults. The Chair Stand Tests, which include the Five Times Sit-to-Stand Test (5xSST) and the 30 Second Sit-to-Stand Test (30CST), are the standard for measuring lower-body strength in older adults. However, these tests are performed manually, which can be labor-intensive and prone to error. We developed a sensor-integrated chair that automatically captures the dynamic weight and distribution on the chair. The collected time series weight–sensor data is automatically uploaded for immediate determination of the sit-to-stand timing and counts, as well as providing a record for future comparison of lower-body strength progression. The automatic test administration can provide significant labor savings for medical personnel and deliver much more accurate data. Data from 10 patients showed good agreement between the manually collected and sensor-collected 30CST data (M = 0.5, SD = 1.58, 95% CI = 1.13). Additional data processing will be able to yield measurements of fatigue and balance and evaluate the mechanisms of failed standing attempts. Full article
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