Special Issue "Personal Health and Wellbeing Intelligent Systems Based on Wearable and Mobile Technologies"

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)

Special Issue Editor

Guest Editor
Prof. Dr. Mario Munoz-Organero

Telematics Engineering Department, University Carlos III de Madrid, Av. Universidad, 30, 28911 Leganes, Madrid, Spain
Website | E-Mail
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 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 papers will be 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 quarterly 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 350 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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Open AccessEditorial Editorial for the Special Issue “Personal Health and Wellbeing Intelligent Systems Based on Wearable and Mobile Technologies”
Technologies 2018, 6(1), 29; https://doi.org/10.3390/technologies6010029
Received: 10 February 2018 / Revised: 10 February 2018 / Accepted: 26 February 2018 / Published: 1 March 2018
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Abstract
Wearable and mobile personal devices, from smart phones, bands, glasses, and watches to smart clothes and implants, are becoming increasingly ubiquitous [...]
Full article

Research

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Open AccessArticle Quantification of Feto-Maternal Heart Rate from Abdominal ECG Signal Using Empirical Mode Decomposition for Heart Rate Variability Analysis
Technologies 2017, 5(4), 68; https://doi.org/10.3390/technologies5040068
Received: 12 September 2017 / Revised: 10 October 2017 / Accepted: 18 October 2017 / Published: 21 October 2017
Cited by 2 | PDF Full-text (3019 KB) | HTML Full-text | XML Full-text
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
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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
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Open AccessArticle Assessing Operator Wellbeing through Physiological Measurements in Real-Time—Towards Industrial Application
Technologies 2017, 5(4), 61; https://doi.org/10.3390/technologies5040061
Received: 2 July 2017 / Revised: 13 September 2017 / Accepted: 20 September 2017 / Published: 22 September 2017
Cited by 2 | PDF Full-text (2560 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
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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
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Open AccessArticle Sample Entropy Identifies Differences in Spontaneous Leg Movement Behavior between Infants with Typical Development and Infants at Risk of Developmental Delay
Technologies 2017, 5(3), 55; https://doi.org/10.3390/technologies5030055
Received: 10 July 2017 / Revised: 30 August 2017 / Accepted: 30 August 2017 / Published: 2 September 2017
Cited by 3 | PDF Full-text (667 KB) | HTML Full-text | XML Full-text
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)
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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
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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
Technologies 2017, 5(3), 39; https://doi.org/10.3390/technologies5030039
Received: 21 May 2017 / Revised: 16 June 2017 / Accepted: 20 June 2017 / Published: 23 June 2017
Cited by 7 | PDF Full-text (2524 KB) | HTML Full-text | XML Full-text
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
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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
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