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Personal Health and Wellbeing Intelligent Systems Based on Wearable and Mobile Technologies

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A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: closed (20 November 2017) | Viewed by 32699

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Special Issue Editor

Prof. Dr. Mario Munoz-Organero

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Guest Editor

Department of Telematic Engineering, Universidad Carlos III de Madrid, 28911 Madrid, Spain
Interests: wearable technologies for health and wellbeing applications; mobile and pervasive computing for assistive living; Internet of Things and assistive technologies; machine learning algorithms for physiological; inertial and location sensors; personal assistants and coaching for health self-management; activity detection and prediction methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable and mobile personal devices are becoming more ubiquitous, from smart phones, bands, glasses and watches to smart clothes and implants. These wearable sensing technologies can provide 24/7 physiological and movement data that enhance the knowledge base for the user or groups of users. They constitute the internal fabric of an Internet of Smart Things that provides the basis to better understand the user, what the user does, when, how and even why. Both physical and mental health related information can be extracted or inferred from the diverse nature of the data. This Special Issue aims to publish up-to-date research in developing personal applications, methods and algorithms based on information extracted or inferred from wearable and mobile sensor devices. This wealth of information facilitates users to better self-manage their health and wellbeing. Both theoretical models to process sensor data, proof of concept and user ready applications are welcome.

Some of the topics of interest for this Special Issue include:

Data gathering from users and patients based on wearable technology and sensor devices
Feature extraction from wearable sensors
Probabilistic models and inference
Machine learning techniques applied to wearable sensor data.
Multi-sensor data fusion
Learning from wearable data
Optimization techniques and model training based on wearable sensor data
Health and wellbeing models based on wearable sensor data
Personal recommender systems for self-management for long term conditions
Personal recommender systems for self-management in rehabilitation
User interfaces and personalized feedback
User experiments
User applications
Novel wearable sensor devices
Big data in health from wearable devices
Privacy, security and ethical aspects
Adoption of wearable sensors by the national health systems in Europe
Future trends
Regulations in different countries

Prof. Dr. Mario Munoz-Organero
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. Technologies is an international peer-reviewed open access monthly 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 1600 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

wearable sensors
mobile devices and applications
personal health and wellbeing
intelligent systems and machine learning algorithms
Internet of Things

Published Papers (5 papers)

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Editorial

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2 pages, 148 KiB

Open AccessEditorial

Editorial for the Special Issue “Personal Health and Wellbeing Intelligent Systems Based on Wearable and Mobile Technologies”

by Mario Munoz-Organero

Technologies 2018, 6(1), 29; https://doi.org/10.3390/technologies6010029 - 01 Mar 2018

Cited by 1 | Viewed by 3733

Abstract

Wearable and mobile personal devices, from smart phones, bands, glasses, and watches to smart clothes and implants, are becoming increasingly ubiquitous [...]
Full article

(This article belongs to the Special Issue Personal Health and Wellbeing Intelligent Systems Based on Wearable and Mobile Technologies)

Research

Jump to: Editorial

3019 KiB

Open AccessArticle

Quantification of Feto-Maternal Heart Rate from Abdominal ECG Signal Using Empirical Mode Decomposition for Heart Rate Variability Analysis

by Abdullah Bin Queyam, Sharvan Kumar Pahuja and Dilbag Singh

Technologies 2017, 5(4), 68; https://doi.org/10.3390/technologies5040068 - 21 Oct 2017

Cited by 20 | Viewed by 10184

Abstract

In this paper, a robust method of feto-maternal heart rate extraction from the non-invasive composite abdominal Electrocardiogram (aECG) signal is presented. The proposed method is based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, in which a composite aECG signal is decomposed into its constituent frequency components called Intrinsic Mode Functions (IMFs) or simply “modes”, with better spectral separation. Decomposed IMFs are then selected manually according to probable maternal and fetal heart rate information and are processed further for quantification of maternal and fetal heart rate and variability analysis. The proposed method was applied to aECG recordings collected from three different sources: (i) the PhysioNet (adfecgdb) database; (ii) the PhysioNet (nifecgdb) database; and (iii) synthetic aECG signal generated from mathematical modeling in the LabVIEW software environment. An overall sensitivity of 98.83%, positive diagnostic value of 97.97%, accuracy of 96.93% and performance index of 96.75% were obtained in the case of Maternal Heart Rate (MHR) quantification, and an overall sensitivity of 98.13%, positive diagnostic value of 97.62%, accuracy of 95.91% and performance index of 95.69% were obtained in case of Fetal Heart Rate (FHR) quantification. The obtained results confirm that CEEMDAN is a very robust and accurate method for extraction of feto-maternal heart rate components from aECG signals. We also conclude that non-invasive aECG is an effective and reliable method for long-term FHR and MHR monitoring during pregnancy and labor. The requirement of manual intervention while selecting the probable maternal and fetal components from “n” number of decomposed modes limits the real-time application of the proposed methodology. This is due to the fact that the number of modes “n” produced by the CEEMDAN decomposition is unpredictable. However, the proposed methodology is well suited for applications where a small time-delay or offset in feto-maternal monitoring can be acceptable. In future, application-specific modification of the CEEMDAN algorithm can be implemented to eliminate manual intervention completely and will be suitable for long-term feto-maternal monitoring. Full article

(This article belongs to the Special Issue Personal Health and Wellbeing Intelligent Systems Based on Wearable and Mobile Technologies)

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2560 KiB

Open AccessArticle

Assessing Operator Wellbeing through Physiological Measurements in Real-Time—Towards Industrial Application

by Sandra Mattsson, Åsa Fast-Berglund and Magnus Åkerman

Technologies 2017, 5(4), 61; https://doi.org/10.3390/technologies5040061 - 22 Sep 2017

Cited by 15 | Viewed by 6721

Abstract

This article focuses on how operator wellbeing can be assessed to ensure social sustainability and operator performance at assembly stations. Rapid technological advances provide possibilities for assessing wellbeing in real-time, and from an assembly system perspective, this could enable the assessment of physiological data in real-time. While technology is available, it has not been implemented or tested in industry. The aim of this paper was to investigate empirically how concurrent physiological measurement technologies can be integrated into an industrial application, in order to increase operator wellbeing and operator performance. A mixed method approach was used, which included a literature study, two laboratory tests, two case studies and a workshop. The results indicated that operator wellbeing could be assessed through electro-dermal activity, but that the data is perceived as difficult to interpret. For an industrial application, operator perception and data presentation are important and risks connected to personal integrity and IT-support need to be addressed. Future work includes testing how a combination of physiological measures and self-assessments can be used to assess operator wellbeing in an industrial context. Full article

(This article belongs to the Special Issue Personal Health and Wellbeing Intelligent Systems Based on Wearable and Mobile Technologies)

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Figure 1

667 KiB

Open AccessArticle

Sample Entropy Identifies Differences in Spontaneous Leg Movement Behavior between Infants with Typical Development and Infants at Risk of Developmental Delay

by Beth A. Smith, Douglas L. Vanderbilt, Bryon Applequist and Anastasia Kyvelidou

Technologies 2017, 5(3), 55; https://doi.org/10.3390/technologies5030055 - 02 Sep 2017

Cited by 22 | Viewed by 5114

Abstract

We are interested in using wearable sensor data to analyze detailed characteristics of movement, such as repeatability and variability of movement patterns, over days and months to accurately capture real-world infant behavior. The purpose of this study was to explore Sample Entropy (SampEn) from wearable sensor data as a measure of variability of spontaneous infant leg movement and as a potential marker of the development of neuromotor control. We hypothesized that infants at risk (AR) of developmental delay would present significantly lower SampEn values than infants with typical development (TD). Participants were 11 infants with TD and 20 infants AR. We calculated SampEn from 1–4 periods of data of 7200 samples in length when the infants were actively playing across the day. The infants AR demonstrated smaller SampEn values (median 0.21) than the infants with TD (median 1.20). Lower values of SampEn indicate more similarity in patterns across time, and may indicate more repetitive, less exploratory behavior in infants AR compared to infants with TD. In future studies, we would like to expand to analyze longer periods of wearable sensor data and/or determine how to optimally sample representative periods across days and months. Full article

(This article belongs to the Special Issue Personal Health and Wellbeing Intelligent Systems Based on Wearable and Mobile Technologies)

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2524 KiB

Open AccessArticle

Development of a Wearable Sensor Algorithm to Detect the Quantity and Kinematic Characteristics of Infant Arm Movement Bouts Produced across a Full Day in the Natural Environment

by Ivan A. Trujillo-Priego, Christianne J. Lane, Douglas L. Vanderbilt, Weiyang Deng, Gerald E. Loeb, Joanne Shida and Beth A. Smith

Technologies 2017, 5(3), 39; https://doi.org/10.3390/technologies5030039 - 23 Jun 2017

Cited by 34 | Viewed by 6386

Abstract

We developed a wearable sensor algorithm to determine the number of arm movement bouts an infant produces across a full day in the natural environment. Full-day infant arm movement was recorded from 33 infants (22 infants with typical development and 11 infants at risk of atypical development) across multiple days and months by placing wearable sensors on each wrist. Twenty second sections of synchronized video data were used to compare the algorithm against visual observation as the gold standard for counting the number of arm movement bouts. Overall, the algorithm counted 173 bouts and the observer identified 180, resulting in a sensitivity of 90%. For each bout produced across the day, we then calculated the following kinematic characteristics: duration, average and peak acceleration, average and peak angular velocity, and type of movement (one arm only, both arms for some portion of the bout, or both arms for the entire bout). As the first step toward developing norms, we present average values of full-day arm movement kinematic characteristics across the first months of infancy for infants with typical development. Identifying and quantifying infant arm movement characteristics produced across a full day has potential application in early identification of developmental delays and the provision of early intervention therapies to support optimal infant development. Full article

(This article belongs to the Special Issue Personal Health and Wellbeing Intelligent Systems Based on Wearable and Mobile Technologies)

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