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Special Issue "Data Analytics and Applications of the Wearable Sensors in Healthcare"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: 31 May 2019

Special Issue Editors

Guest Editor
Dr. Shabbir Syed Abdul

Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
Website | E-Mail
Interests: wearable devices; IoT in health; big data analytics; data visualization; long-term care; cancer management
Guest Editor
Dr. Luis Fernandez Luque

Qatar Computing Research Institute, Ar-Rayyan, Qatar
Website | E-Mail
Interests: mobile health; pervasive health; social media data analytics; patient empowerment
Guest Editor
Dr. Pei-Yun (Sabrina) Hsueh

IBM Thomas J. Watson Research Center, Yorktown Heights, NY USA
Website | E-Mail
Interests: behavioral analytics; cognitive learning and adaptation; multimdoal and text analytics; human computer interaction; consumer and pervasive health informatics
Guest Editor
Dr. Juan Miguel Garcia-Gomez

Biomedical Data Science Lab, Universitat Politecnica de Valencia, Valencia, Spain
Website | E-Mail
Interests: biomedical data; data analytics; data quality assessment; image analysis; companion diagnostics; predictive models; oncology; decision support systems
Guest Editor
Dr. Begoña Garcia-Zapirain

Head of eVIDA Lab, University of Deusto, Bilbao, Spain
Website | E-Mail
Phone: +34619967223
Interests: biomedical data analytics; biomedical signal processing; biofeedback; technological solutions for health and wellbeing

Special Issue Information

Dear Colleagues,

There is abundance of wearable devices for healthcare purposes in the market. Wearable devices aid in continuous monitoring of patients, better management of chronic diseases, reduce health care costs, prevent emergency situations, and improve care quality. The devices have a great deal of potential to generate healthcare data, with an approach to boost data analytics for research purposes [1]. However, surveys showed that 32% of users stop wearing these devices after six months, and 50% after one year. Lack of user experience and feedback, inadequate user engagement, data security and privacy concerns, are some of the reasons leading to paucity of data for research purposes, which creates hindrances in analysis [2]. This implies future concerns in the field of data analytics using wearable devices and further its application in health care.

In clinical settings, research on the devices can, not only aid patients, but also assist doctors to remotely track patients’ conditions. Use of data from the devices can help predict disease outcomes and also prevent complications, prolong lives of patients [1]. Data from wearable devices has a promising future in various healthcare domains such as oncology, geriatrics, rheumatology, cardiology, etc. Thus, it is important to generate insights on the use of these devices and awareness of the usefulness of the devices. The wide application of wearable devices in healthcare is on the rise and the immense data generated, has a huge scope and demand for research purposes. Artificial intelligence and computing technologies make use of the big data emerging as a result of use of the wearable devices [3,4]. The generated data has increasing scope in predictive analytics and improved patient care. Through this Special Issue, we aim to focus on the impediments in data collection, data analysis, application of the outcomes, and further, propose future research implications. We anticipate that this issue will develop new insights on healthcare and research applications of wearable devices and in turn aid researchers, physicians, and enhance the use of the devices among patients.

References:

[1] Gruessner, V. Key healthcare trends strengthen remote patient monitoring. 2015 [cited 2016 9 Jan 2016]; Available from: http://mhealthintelligence.com/news/key-healthcare-trends-strengthen-remote-patient-monitoring.

[2] Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The rise of consumer health wearables: promises and barriers. PLoS Medicine, 13(2), e1001953.

[3] Priyanka, A., Parimala, M., Sudheer, K., Kaluri, R., Lakshmanna, K., & Reddy, M. P. K. (2017, November). BIG data based on healthcare analysis using IOT devices. In IOP Conference Series: Materials Science and Engineering (Vol. 263, No. 4, p. 042059). IOP Publishing.

[4] O'Dowd, E. Growth of Medical IoT Devices Supports Healthcare Analytics. 2016 [cited 2018 12 April]; Available from: https://hitinfrastructure.com/news/growth-of-medical-iot-devices-supports-healthcare-analytics.

Wearable technologies have become widely used in the health sector with applications ranging from clinical research to fitness applications. In multiple research studies wearable technologies are combined with mobile devices to collect patient reported outcomes. Furthermore, health coaching applications are widely interfacing with fitness wearable devices or smart watches.The availability of those wearable technologies are paving the wide to many disruptive data-driven applications across the whole healthcare domain. This special issue will cover a wide range of topics related to health wearable data analytics and applications such as:

  • Data analytics of the wearable sensors
  • Clinical applications of the wearable sensors
  • Visualization of wearables health data.
  • Linking digital assessment of mobility to clinical endpoints
  • Actigraphy patterns
  • Intrinsic variability of the actigraphy signals
  • Common Representation of sensors data
  • Health Recommender Systems based on Wearable Health Data.
  • Sleep analysis through wearable devices
  • Health Recommender Systems based on Wearable Health Data.
  • Human factors, human computer interaction and usability of health wearable applications. keyword

Dr. Shabbir Syed Abdul
Dr. Luis Fernandez Luque
Dr. Pei-Yun (Sabrina) Hsueh
Dr. Juan M Garcia-Gomez
Dr. Begoña García Zapirain
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 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. 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 1800 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.

Published Papers (19 papers)

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Open AccessArticle P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing
Sensors 2019, 19(5), 1225; https://doi.org/10.3390/s19051225
Received: 14 February 2019 / Revised: 1 March 2019 / Accepted: 6 March 2019 / Published: 11 March 2019
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Abstract
Preventive healthcare has attracted much attention recently. Improving people’s lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health problems. Ergonomists already assess [...] Read more.
Preventive healthcare has attracted much attention recently. Improving people’s lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health problems. Ergonomists already assess MSD risk factors and suggest changes in workplaces. However, existing methods are mainly based on visual observations, which have a relatively low reliability and cover only part of the workday. These suggestions concern the overall workplace and the organization of work, but rarely includes individuals’ work techniques. In this work, we propose a precise and pervasive ergonomic platform for continuous risk assessment. The system collects data from wearable sensors, which are synchronized and processed by a mobile computing layer, from which exposure statistics and risk assessments may be drawn, and finally, are stored at the server layer for further analyses at both individual and group levels. The platform also enables continuous feedback to the worker to support behavioral changes. The deployed cloud platform in Amazon Web Services instances showed sufficient system flexibility to affordably fulfill requirements of small to medium enterprises, while it is expandable for larger corporations. The system usability scale of 76.6 indicates an acceptable grade of usability. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Wearable Sensor-Based Exercise Biofeedback for Orthopaedic Rehabilitation: A Mixed Methods User Evaluation of a Prototype System
Sensors 2019, 19(2), 432; https://doi.org/10.3390/s19020432
Received: 12 December 2018 / Revised: 17 January 2019 / Accepted: 18 January 2019 / Published: 21 January 2019
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Abstract
The majority of wearable sensor-based biofeedback systems used in exercise rehabilitation lack end-user evaluation as part of the development process. This study sought to evaluate an exemplar sensor-based biofeedback system, investigating the feasibility, usability, perceived impact and user experience of using the platform. [...] Read more.
The majority of wearable sensor-based biofeedback systems used in exercise rehabilitation lack end-user evaluation as part of the development process. This study sought to evaluate an exemplar sensor-based biofeedback system, investigating the feasibility, usability, perceived impact and user experience of using the platform. Fifteen patients participated in the study having recently undergone knee replacement surgery. Participants were provided with the system for two weeks at home, completing a semi-structured interview alongside the System Usability Scale (SUS) and user version of the Mobile Application Rating Scale (uMARS). The analysis from the SUS (mean = 90.8 [SD = 7.8]) suggests a high degree of usability, supported by qualitative findings. The mean adherence rate was 79% with participants reporting a largely positive user experience, suggesting it offers additional support with the rehabilitation regime. Overall quality from the mean uMARS score was 4.1 out of 5 (SD = 0.39), however a number of bugs and inaccuracies were highlighted along with suggestions for additional features to enhance engagement. This study has shown that patients perceive value in the use of wearable sensor-based biofeedback systems and has highlighted the benefit of user-evaluation during the design process, illustrated the need for real-world accuracy validation, and supports the ongoing development of such systems. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Assessment of Breathing Parameters Using an Inertial Measurement Unit (IMU)-Based System
Sensors 2019, 19(1), 88; https://doi.org/10.3390/s19010088
Received: 24 October 2018 / Revised: 10 December 2018 / Accepted: 20 December 2018 / Published: 27 December 2018
Cited by 1 | PDF Full-text (3649 KB) | HTML Full-text | XML Full-text
Abstract
Breathing frequency (fB) is an important vital sign that—if appropriately monitored—may help to predict clinical adverse events. Inertial sensors open the door to the development of low-cost, wearable, and easy-to-use breathing-monitoring systems. The present paper proposes a new posture-independent processing algorithm [...] Read more.
Breathing frequency (fB) is an important vital sign that—if appropriately monitored—may help to predict clinical adverse events. Inertial sensors open the door to the development of low-cost, wearable, and easy-to-use breathing-monitoring systems. The present paper proposes a new posture-independent processing algorithm for breath-by-breath extraction of breathing temporal parameters from chest-wall inclination change signals measured using inertial measurement units. An important step of the processing algorithm is dimension reduction (DR) that allows the extraction of a single respiratory signal starting from 4-component quaternion data. Three different DR methods are proposed and compared in terms of accuracy of breathing temporal parameter estimation, in a group of healthy subjects, considering different breathing patterns and different postures; optoelectronic plethysmography was used as reference system. In this study, we found that the method based on PCA-fusion of the four quaternion components provided the best fB estimation performance in terms of mean absolute errors (<2 breaths/min), correlation (r > 0.963) and Bland–Altman Analysis, outperforming the other two methods, based on the selection of a single quaternion component, identified on the basis of spectral analysis; particularly, in supine position, results provided by PCA-based method were even better than those obtained with the ideal quaternion component, determined a posteriori as the one providing the minimum estimation error. The proposed algorithm and system were able to successfully reconstruct the respiration-induced movement, and to accurately determine the respiratory rate in an automatic, position-independent manner. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Data Analytics of a Wearable Device for Heat Stroke Detection
Sensors 2018, 18(12), 4347; https://doi.org/10.3390/s18124347
Received: 31 October 2018 / Revised: 29 November 2018 / Accepted: 6 December 2018 / Published: 9 December 2018
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Abstract
When exercising in a high-temperature environment, heat stroke can cause great harm to the human body. However, runners may ignore important physiological warnings and are not usually aware that a heat stroke is occurring. To solve this problem, this study evaluates a runner’s [...] Read more.
When exercising in a high-temperature environment, heat stroke can cause great harm to the human body. However, runners may ignore important physiological warnings and are not usually aware that a heat stroke is occurring. To solve this problem, this study evaluates a runner’s risk of heat stroke injury by using a wearable heat stroke detection device (WHDD), which we developed previously. Furthermore, some filtering algorithms are designed to correct the physiological parameters acquired by the WHDD. To verify the effectiveness of the WHDD and investigate the features of these physiological parameters, several people were chosen to wear the WHDD while conducting the exercise experiment. The experimental results show that the WHDD can identify high-risk trends for heat stroke successfully from runner feedback of the uncomfortable statute and can effectively predict the occurrence of a heat stroke, thus ensuring safety. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Feature Extraction and Similarity of Movement Detection during Sleep, Based on Higher Order Spectra and Entropy of the Actigraphy Signal: Results of the Hispanic Community Health Study/Study of Latinos
Sensors 2018, 18(12), 4310; https://doi.org/10.3390/s18124310
Received: 19 October 2018 / Revised: 24 November 2018 / Accepted: 30 November 2018 / Published: 6 December 2018
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Abstract
The aim of this work was to develop a new unsupervised exploratory method of characterizing feature extraction and detecting similarity of movement during sleep through actigraphy signals. We here propose some algorithms, based on signal bispectrum and bispectral entropy, to determine the unique [...] Read more.
The aim of this work was to develop a new unsupervised exploratory method of characterizing feature extraction and detecting similarity of movement during sleep through actigraphy signals. We here propose some algorithms, based on signal bispectrum and bispectral entropy, to determine the unique features of independent actigraphy signals. Experiments were carried out on 20 randomly chosen actigraphy samples of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) database, with no information other than their aperiodicity. The Pearson correlation coefficient matrix and the histogram correlation matrix were computed to study the similarity of movements during sleep. The results obtained allowed us to explore the connections between certain sleep actigraphy patterns and certain pathologies. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors
Sensors 2018, 18(12), 4189; https://doi.org/10.3390/s18124189
Received: 29 October 2018 / Revised: 22 November 2018 / Accepted: 27 November 2018 / Published: 29 November 2018
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Abstract
Human Activity Recognition (HAR) refers to an emerging area of interest for medical, military, and security applications. However, the identification of the features to be used for activity classification and recognition is still an open point. The aim of this study was to [...] Read more.
Human Activity Recognition (HAR) refers to an emerging area of interest for medical, military, and security applications. However, the identification of the features to be used for activity classification and recognition is still an open point. The aim of this study was to compare two different feature sets for HAR. Particularly, we compared a set including time, frequency, and time-frequency domain features widely used in literature (FeatSet_A) with a set of time-domain features derived by considering the physical meaning of the acquired signals (FeatSet_B). The comparison of the two sets were based on the performances obtained using four machine learning classifiers. Sixty-one healthy subjects were asked to perform seven different daily activities wearing a MIMU-based device. Each signal was segmented using a 5-s window and for each window, 222 and 221 variables were extracted for the FeatSet_A and FeatSet_B respectively. Each set was reduced using a Genetic Algorithm (GA) simultaneously performing feature selection and classifier optimization. Our results showed that Support Vector Machine achieved the highest performances using both sets (97.1% and 96.7% for FeatSet_A and FeatSet_B respectively). However, FeatSet_B allows to better understand alterations of the biomechanical behavior in more complex situations, such as when applied to pathological subjects. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle LSTM-Guided Coaching Assistant for Table Tennis Practice
Sensors 2018, 18(12), 4112; https://doi.org/10.3390/s18124112
Received: 28 October 2018 / Revised: 16 November 2018 / Accepted: 21 November 2018 / Published: 23 November 2018
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Abstract
Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be [...] Read more.
Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Identifying Free-Living Physical Activities Using Lab-Based Models with Wearable Accelerometers
Sensors 2018, 18(11), 3893; https://doi.org/10.3390/s18113893
Received: 11 October 2018 / Revised: 5 November 2018 / Accepted: 6 November 2018 / Published: 12 November 2018
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Abstract
The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from [...] Read more.
The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from ( N = 152 ) adult participants; age 18–64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then used to identify physical activities performed by 20 participants, each recorded for two free-living sessions of approximately six hours each. The free-living activity intensities were estimated with 80% accuracy and showed the dominance of stationary and light intensity activities in 36 out of 40 recorded sessions. This work proposes a novel activity recognition process to identify unsupervised free-living activities using lab-based classification models. In summary, this study contributes to the use of wearable sensors to identify physical activities and estimate energy expenditure in free-living settings. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG
Sensors 2018, 18(10), 3538; https://doi.org/10.3390/s18103538
Received: 9 September 2018 / Revised: 16 October 2018 / Accepted: 17 October 2018 / Published: 19 October 2018
Cited by 1 | PDF Full-text (5278 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the [...] Read more.
In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the realization of the core of this system, i.e., the Cardiac Health Assessment and Monitoring Platform (CHAMP), with respect to its hardware, firmware, and wireless design features, is presented. The feature values from the CHAMP system have been correlated with myo-cardiac functions obtained from actual heart failure (HF) patients. The usability of this MCG-based cardiac health monitoring smart clothing system has also been evaluated with technology acceptance model (TAM) analysis and the results indicate that the subject shows a positive attitude toward using this wearable MCG-based cardiac health monitoring and early warning system. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Fusion of Heart Rate, Respiration and Motion Measurements from a Wearable Sensor System to Enhance Energy Expenditure Estimation
Sensors 2018, 18(9), 3092; https://doi.org/10.3390/s18093092
Received: 17 August 2018 / Revised: 7 September 2018 / Accepted: 11 September 2018 / Published: 14 September 2018
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Abstract
This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model [...] Read more.
This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21–65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R2 = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R2 = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R2 = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Quantile Coarsening Analysis of High-Volume Wearable Activity Data in a Longitudinal Observational Study
Sensors 2018, 18(9), 3056; https://doi.org/10.3390/s18093056
Received: 13 August 2018 / Revised: 4 September 2018 / Accepted: 6 September 2018 / Published: 12 September 2018
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Abstract
Owing to advances in sensor technologies on wearable devices, it is feasible to measure physical activity of an individual continuously over a long period. These devices afford opportunities to understand individual behaviors, which may then provide a basis for tailored behavior interventions. The [...] Read more.
Owing to advances in sensor technologies on wearable devices, it is feasible to measure physical activity of an individual continuously over a long period. These devices afford opportunities to understand individual behaviors, which may then provide a basis for tailored behavior interventions. The large volume of data however poses challenges in data management and analysis. We propose a novel quantile coarsening analysis (QCA) of daily physical activity data, with a goal to reduce the volume of data while preserving key information. We applied QCA to a longitudinal study of 79 healthy participants whose step counts were monitored for up to 1 year by a Fitbit device, performed cluster analysis of daily activity, and identified individual activity signature or pattern in terms of the clusters identified. Using 21,393 time series of daily physical activity, we identified eight clusters. Employment and partner status were each associated with 5 of the 8 clusters. Using less than 2% of the original data, QCA provides accurate approximation of the mean physical activity, forms meaningful activity patterns associated with individual characteristics, and is a versatile tool for dimension reduction of densely sampled data. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health
Sensors 2018, 18(9), 2966; https://doi.org/10.3390/s18092966
Received: 12 July 2018 / Revised: 28 August 2018 / Accepted: 28 August 2018 / Published: 6 September 2018
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Abstract
Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution [...] Read more.
Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Towards an Efficient One-Class Classifier for Mobile Devices and Wearable Sensors on the Context of Personal Risk Detection
Sensors 2018, 18(9), 2857; https://doi.org/10.3390/s18092857
Received: 27 June 2018 / Revised: 20 August 2018 / Accepted: 23 August 2018 / Published: 30 August 2018
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Abstract
In this work, we present a first step towards an efficient one-class classifier well suited for mobile devices to be implemented as part of a user application coupled with wearable sensors in the context of personal risk detection. We compared one-class Support Vector [...] Read more.
In this work, we present a first step towards an efficient one-class classifier well suited for mobile devices to be implemented as part of a user application coupled with wearable sensors in the context of personal risk detection. We compared one-class Support Vector Machine (ocSVM) and OCKRA (One-Class K-means with Randomly-projected features Algorithm). Both classifiers were tested using four versions of the publicly available PRIDE (Personal RIsk DEtection) dataset. The first version is the original PRIDE dataset, which is based only on time-domain features. We created a second version that is simply an extension of the original dataset with new attributes in the frequency domain. The other two datasets are a subset of these two versions, after a feature selection procedure based on a correlation matrix analysis followed by a Principal Component Analysis. All experiments were focused on the performance of the classifiers as well as on the execution time during the training and classification processes. Therefore, our goal in this work is twofold: we aim at reducing execution time but at the same time maintaining a good classification performance. Our results show that OCKRA achieved on average, 89.1% of Area Under the Curve (AUC) using the full set of features and 83.7% when trained using a subset of them. Furthermore, regarding execution time, OCKRA reports in the best case a 33.1% gain when using a subset of the feature vector, instead of the full set of features. These results are better than those reported by ocSVM, in which case, even though the AUCs are very close to each other, execution times are significantly higher in all cases, for example, more than 20 h versus less than an hour in the worst-case scenario. Having in mind the trade-off between classification performance and efficiency, our results support the choice of OCKRA as our best candidate so far for a mobile implementation where less processing and memory resources are at hand. OCKRA reports a very encouraging speed-up without sacrificing the classifier performance when using the PRIDE dataset based only on time-domain attributes after a feature selection procedure. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data
Sensors 2018, 18(9), 2833; https://doi.org/10.3390/s18092833
Received: 3 July 2018 / Revised: 8 August 2018 / Accepted: 23 August 2018 / Published: 27 August 2018
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Abstract
Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for [...] Read more.
Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for a period of 23 weeks during their HD sessions. During these 23 weeks, a total number of 3237 HD sessions were observed. Out of 109 patients enrolled in the study, 78 patients reported clinical events such as muscle spasms, inpatient stays, emergency visits or even death during the study period. We analyzed the sensor data of these two groups of patients, namely an event and no-event group. We found a statistically significant difference in the heart rates, respiration rates, and some heart rate variability parameters among the two groups of patients when their means were compared using an independent sample t-test. We further developed a supervised machine-learning-based prediction model to predict event or no-event based on the sensor data and demographic information. A mean area under curve (ROC AUC) of 90.16% with 96.21% mean precision, and 88.47% mean recall was achieved. Our findings point towards the novel use of non-contact sensors in clinical settings to monitor the vital parameters of patients and the further development of early warning solutions using artificial intelligence (AI) for the prediction of clinical events. These models could assist healthcare professionals in taking decisions and designing better care plans for patients by early detecting changes to vital parameters. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessFeature PaperArticle Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions
Sensors 2018, 18(8), 2725; https://doi.org/10.3390/s18082725
Received: 5 July 2018 / Revised: 10 August 2018 / Accepted: 15 August 2018 / Published: 19 August 2018
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Abstract
Wearable motion sensors are assumed to be correctly positioned and oriented in most of the existing studies. However, generic wireless sensor units, patient health and state monitoring sensors, and smart phones and watches that contain sensors can be differently oriented on the body. [...] Read more.
Wearable motion sensors are assumed to be correctly positioned and oriented in most of the existing studies. However, generic wireless sensor units, patient health and state monitoring sensors, and smart phones and watches that contain sensors can be differently oriented on the body. The vast majority of the existing algorithms are not robust against placing the sensor units at variable orientations. We propose a method that transforms the recorded motion sensor sequences invariantly to sensor unit orientation. The method is based on estimating the sensor unit orientation and representing the sensor data with respect to the Earth frame. We also calculate the sensor rotations between consecutive time samples and represent them by quaternions in the Earth frame. We incorporate our method in the pre-processing stage of the standard activity recognition scheme and provide a comparative evaluation with the existing methods based on seven state-of-the-art classifiers and a publicly available dataset. The standard system with fixed sensor unit orientations cannot handle incorrectly oriented sensors, resulting in an average accuracy reduction of 31.8%. Our method results in an accuracy drop of only 4.7% on average compared to the standard system, outperforming the existing approaches that cause an accuracy degradation between 8.4 and 18.8%. We also consider stationary and non-stationary activities separately and evaluate the performance of each method for these two groups of activities. All of the methods perform significantly better in distinguishing non-stationary activities, our method resulting in an accuracy drop of 2.1% in this case. Our method clearly surpasses the remaining methods in classifying stationary activities where some of the methods noticeably fail. The proposed method is applicable to a wide range of wearable systems to make them robust against variable sensor unit orientations by transforming the sensor data at the pre-processing stage. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors
Sensors 2018, 18(8), 2474; https://doi.org/10.3390/s18082474
Received: 27 June 2018 / Revised: 20 July 2018 / Accepted: 25 July 2018 / Published: 31 July 2018
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Abstract
Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction rely on self-reports. The goal of this study was to investigate if wearable textile-based sensors can be used to accurately detect [...] Read more.
Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction rely on self-reports. The goal of this study was to investigate if wearable textile-based sensors can be used to accurately detect if the user is talking as a future indicator of social interaction. In a laboratory study, fifteen healthy young participants were asked to talk while performing daily activities such as sitting, standing and walking. It is known that the breathing pattern differs significantly between normal and speech breathing (i.e., talking). We integrated resistive stretch sensors into wearable elastic bands, with a future integration into clothing in mind, to record the expansion and contraction of the chest and abdomen while breathing. We developed an algorithm incorporating machine learning and evaluated its performance in distinguishing between periods of talking and non-talking. In an intra-subject analysis, our algorithm detected talking with an average accuracy of 85%. The highest accuracy of 88% was achieved during sitting and the lowest accuracy of 80.6% during walking. Complete segments of talking were correctly identified with 96% accuracy. From the evaluated machine learning algorithms, the random forest classifier performed best on our dataset. We demonstrate that wearable textile-based sensors in combination with machine learning can be used to detect when the user is talking. In the future, this approach may be used as an indicator of social interaction to prevent social isolation and loneliness. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessFeature PaperArticle Complexity of Daily Physical Activity Is More Sensitive Than Conventional Metrics to Assess Functional Change in Younger Older Adults
Sensors 2018, 18(7), 2032; https://doi.org/10.3390/s18072032
Received: 18 May 2018 / Revised: 19 June 2018 / Accepted: 21 June 2018 / Published: 25 June 2018
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Abstract
The emerging mHealth applications, incorporating wearable sensors, enables continuous monitoring of physical activity (PA). This study aimed at analyzing the relevance of a multivariate complexity metric in assessment of functional change in younger older adults. Thirty individuals (60–70 years old) participated in a [...] Read more.
The emerging mHealth applications, incorporating wearable sensors, enables continuous monitoring of physical activity (PA). This study aimed at analyzing the relevance of a multivariate complexity metric in assessment of functional change in younger older adults. Thirty individuals (60–70 years old) participated in a 4-week home-based exercise intervention. The Community Balance and Mobility Scale (CBMS) was used for clinical assessment of the participants’ functional balance and mobility performance pre- and post- intervention. Accelerometers worn on the low back were used to register PA of one week before and in the third week of the intervention. Changes in conventional univariate PA metrics (percentage of walking and sedentary time, step counts, mean cadence) and complexity were compared to the change as measured by the CBMS. Statistical analyses (21 participants) showed significant rank correlation between the change as measured by complexity and CBMS (ρ = 0.47, p = 0.03). Smoothing the activity output improved the correlation (ρ = 0.58, p = 0.01). In contrast, change in univariate PA metrics did not show correlations. These findings demonstrate the high potential of the complexity metric being useful and more sensitive than conventional PA metrics for assessing functional changes in younger older adults. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Open AccessArticle Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care
Sensors 2018, 18(6), 1851; https://doi.org/10.3390/s18061851
Received: 15 May 2018 / Revised: 1 June 2018 / Accepted: 4 June 2018 / Published: 6 June 2018
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Abstract
Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health [...] Read more.
Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine and health sensors stand as indispensable tools for promoting patient engagement, self-management of diseases and assist doctors to remotely follow up patients. In this paper, we evaluate a rapid prototyping solution for information merging based on five health sensors and two low-cost ubiquitous computing components: Arduino and Raspberry Pi. Our study, which is entirely described with the purpose of reproducibility, aimed to evaluate the extent to which portable technologies are capable of integrating wearable sensors by comparing two deployment scenarios: Raspberry Pi 3 and Personal Computer. The integration is implemented using a choreography engine to transmit data from sensors to a display unit using web services and a simple communication protocol with two modes of data retrieval. Performance of the two set-ups is compared by means of the latency in the wearable data transmission and data loss. PC has a delay of 0.051 ± 0.0035 s (max = 0.2504 s), whereas the Raspberry Pi yields a delay of 0.0175 ± 0.149 s (max = 0.294 s) for N = 300. Our analysis confirms that portable devices ( p < < 0 . 01 ) are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Review

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Open AccessReview Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review
Sensors 2018, 18(7), 2060; https://doi.org/10.3390/s18072060
Received: 31 May 2018 / Revised: 18 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
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Abstract
Falls in older adults present a major growing healthcare challenge and reliable detection of falls is crucial to minimise their consequences. The majority of development and testing has used laboratory simulations. As simulations do not cover the wide range of real-world scenarios performance [...] Read more.
Falls in older adults present a major growing healthcare challenge and reliable detection of falls is crucial to minimise their consequences. The majority of development and testing has used laboratory simulations. As simulations do not cover the wide range of real-world scenarios performance is poor when retested using real-world data. There has been a move from the use of simulated falls towards the use of real-world data. This review aims to assess the current methods for real-world evaluation of fall detection systems, identify their limitations and propose improved robust methods of evaluation. Twenty-two articles met the inclusion criteria and were assessed with regard to the composition of the datasets, data processing methods and the measures of performance. Real-world tests of fall detection technology are inherently challenging and it is clear the field is in its infancy. Most studies used small datasets and studies differed on how to quantify the ability to avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and standardise. To increase robustness and make results comparable, larger standardised datasets are needed containing data from a range of participant groups. Measures that depend on the definition and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the most suitable robust measures for evaluating the real-world performance of fall detection systems. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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