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Dear Colleagues,

Low-cost wearable and ubiquitous sensing technologies may provide a pragmatic key to unlocking the next level of diagnosis support systems. They could provide scalable solutions that provide insightful habitual data of an individual beyond the confines of a clinic. Such data are complex given the heterogeneity of possible testing environments and how individuals perform the activities of daily living. Intelligent systems with smart algorithms must interpret the data in a robust manner to ensure that the derived outcomes are valid and reliable. Moreover, the outcomes must be sensitive to the research hypothesis/question and therefore grounded in their clinical application.

Today, various algorithms involving a plethora of approaches, such as feature extraction and/or machine learning, have been applied. The application of such novel methods to the healthcare sector can aid clinicians in making an accurate and timely diagnosis as well as in providing individualised management of the patient from data gathered beyond the clinic. Thus, this Special Issue, entitled “Wearable and Ubiquitous Technology-Based Algorithms for Analytics in Healthcare”, focuses on the application of advanced methodologies and algorithms in habitual environments for personalised health management.

Dr. Alan Godfrey
Dr. Jessilyn Dunn
Guest Editors

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Keywords

wearable and ubiquitous sensing technology
analytics
healthcare
personalised health management

Published Papers (2 papers)

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Research

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10 pages, 962 KiB

Open AccessArticle

Reliability of IMU-Derived Static Balance Parameters in Neurological Diseases

by Clint Hansen, Maximilian Beckbauer, Robbin Romijnders, Elke Warmerdam, Julius Welzel, Johanna Geritz, Kirsten Emmert and Walter Maetzler

Int. J. Environ. Res. Public Health 2021, 18(7), 3644; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073644 - 31 Mar 2021

Cited by 12 | Viewed by 2724

Abstract

Static balance is a commonly used health measure in clinical practice. Usually, static balance parameters are assessed via force plates or, more recently, with inertial measurement units (IMUs). Multiple parameters have been developed over the years to compare patient groups and understand changes over time. However, the day-to-day variability of these parameters using IMUs has not yet been tested in a neurogeriatric cohort. The aim of the study was to examine day-to-day variability of static balance parameters of five experimental conditions in a cohort of neurogeriatric patients using data extracted from a lower back-worn IMU. A group of 41 neurogeriatric participants (age: 78 ± 5 years) underwent static balance assessment on two occasions 12–24 h apart. Participants performed a side-by-side stance, a semi-tandem stance, a tandem stance on hard ground with eyes open, and a semi-tandem assessment on a soft surface with eyes open and closed for 30 s each. The intra-class correlation coefficient (two-way random, average of the k raters’ measurements, ICC2, k) and minimal detectable change at a 95% confidence level (MDC95%) were calculated for the sway area, velocity, acceleration, jerk, and frequency. Velocity, acceleration, and jerk were calculated in both anterior-posterior (AP) and medio-lateral (ML) directions. Nine to 41 participants could successfully perform the respective balance tasks. Considering all conditions, acceleration-related parameters in the AP and ML directions gave the highest ICC results. The MDC95% values for all parameters ranged from 39% to 220%, with frequency being the most consistent with values of 39–57%, followed by acceleration in the ML (43–55%) and AP direction (54–77%). The present results show moderate to poor ICC and MDC values for IMU-based static balance assessment in neurogeriatric patients. This suggests a limited reliability of these tasks and parameters, which should induce a careful selection of potential clinically relevant parameters. Full article

(This article belongs to the Special Issue Wearable and Ubiquitous Technology-Based Algorithms for Analytics in Healthcare)

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Review

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25 pages, 1965 KiB

Open AccessReview

A Scoping Review of Technological Approaches to Environmental Monitoring

by Graham Coulby, Adrian Clear, Oliver Jones and Alan Godfrey

Int. J. Environ. Res. Public Health 2020, 17(11), 3995; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17113995 - 04 Jun 2020

Cited by 41 | Viewed by 6106

Abstract

Indoor environment quality (IEQ) can negatively affect occupant health and wellbeing. Air quality, as well as thermal, visual and auditory conditions, can determine how comfortable occupants feel within buildings. Some can be measured objectively, but many are assessed by interpreting qualitative responses. Continuous monitoring by passive sensors may be useful to identify links between environmental and physiological changes. Few studies localise measurements to an occupant level perhaps due to many environmental monitoring solutions being large and expensive. Traditional models for occupant comfort analysis often exacerbate this by not differentiating between individual building occupants. This scoping review aims to understand IEQ and explore approaches as to how it is measured with various sensing technologies, identifying trends for monitoring occupant health and wellbeing. Twenty-seven studies were reviewed, and more than 60 state-of-the-art and low-cost IEQ sensors identified. Studies were found to focus on the home or workplace, but not both. This review also found how wearable technology could be used to augment IEQ measurements, creating personalised approaches to health and wellbeing. Opportunities exist to make individuals the primary unit of analysis. Future research should explore holistic personalised approaches to health monitoring in buildings that analyse the individual as they move between environments. Full article

(This article belongs to the Special Issue Wearable and Ubiquitous Technology-Based Algorithms for Analytics in Healthcare)

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