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Special Issue "Sensors and Analytics for Precision Medicine"

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

Deadline for manuscript submissions: closed (15 November 2017)

Special Issue Editors

Guest Editor
Dr. Wendong Xiao

School of Automation and Electrical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China
Website | E-Mail
Interests: wireless localization and tracking; energy harvesting based network resource management; distributed machine learning for big data; wireless sensor networks; internet of things
Guest Editor
Dr. Zhiqiang Zhang

School of Electronic and Electrical Engineering, School of Mechanical Engineering, University of Leeds
Website | E-Mail
Interests: wearable sensing; wearable robotics; sensor fusion; big data and machine learning; biomedical signal processing
Guest Editor
Prof. Le Zhang

College of Computer and Information Science, Southwest University, Chongqing, China
Website | E-Mail
Interests: computational biology; bioinformatics

Special Issue Information

Dear Colleagues,

There are no proven means of prevention or effective treatment for so many diseases, and insights into the biological, environmental, and behavioural influences on these diseases should be gained for better understanding of these diseases. Precision medicine is an emerging approach to disease treatment and prevention, which takes into account individual variability in genes, environment, and lifestyle for each person and tailors medical decisions, practices, and/or products to the individual patient.

Sensor technologies from motion and vital sign sensing, to environmental, behavioural and context monitoring have shown great potential for healthcare. In combination with recent advances in big data analytics for life sciences, healthcare and genomics, sensors can further improve healthcare by moving towards one-off prevention and treatment plans. Replacing state-of-the-art, one-fits-all approaches, personalized prevention and treatment is often employed for selecting appropriate and optimal therapies based on the context of a patient’s genetic content or other environmental and behavioural analysis.

The purpose of this Special Issue is to invite novel contributions that couple sensor design, integration with signal processing and data analytics to acquire and interpret sensor data continuously from spot measurements. Topics of the special issue include, but are not limited to:

  • Novel sensor design and smart embodiment for pervasive healthcare;
  • New platforms for self-tracking or measurement of physical activity, sleep quality, emotion, gait, kinematics, kinetics and other factors related to personal well-being;
  • Information processing for localization and tracking for healthcare applications
  • Resource constrained network information processing for healthcare applications
  • Sensor data analytics, information integration, pattern mining/recognition, behaviour profiling, data visualisation and user feedback related to personal well-being;
  • Sensor fusion in health systems for improved clinical diagnosis and decision making;
  • Big data analytics for improving management of healthcare to enhance efficiency, effectiveness and equity;
  • High throughput data analytics in genome, transcriptome, proteome, metabolome and/or lipidome for sensing and modelling multiple-level biological and physiological systems;
  • Mobile/wearable sensor-based/remote health monitoring systems, with illustrative case studies on use of innovative information processing approaches.
  • Disease focused exemplars and case studies (e.g. neurological disorders, cardiovascular diseases, diabetes and obesity, neuro-rehabilitation);

Dr. Wendong Xiao,
Dr. Zhiqiang Zhang,
Dr. Le Zhang
Guest Editors

Manuscript Submission Information

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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 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 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 (10 papers)

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Research

Open AccessArticle Chronotropic Competence Indices Extracted from Wearable Sensors for Cardiovascular Diseases Management
Sensors 2017, 17(11), 2441; doi:10.3390/s17112441
Received: 30 August 2017 / Revised: 7 October 2017 / Accepted: 19 October 2017 / Published: 25 October 2017
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Abstract
Chronotropic incompetence (CI) has been proven to be an important factor in the diagnosis and management of cardiovascular diseases. In this paper, we extend the existing CI parameters and propose chronotropic competence indices (CCI) to describe the exercise response of the cardiopulmonary system.
[...] Read more.
Chronotropic incompetence (CI) has been proven to be an important factor in the diagnosis and management of cardiovascular diseases. In this paper, we extend the existing CI parameters and propose chronotropic competence indices (CCI) to describe the exercise response of the cardiopulmonary system. A cardiac chronotropic competence Test (3CT), dedicated to CCI measurement using a wearable device, is also presented. Preliminary clinical trials are presented for the validation of 3CT measurement accuracy, and to show the potential of CCI in the prevention and rehabilitation of cardiovascular diseases. Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Open AccessArticle UWB Monitoring System for AAL Applications
Sensors 2017, 17(9), 2092; doi:10.3390/s17092092
Received: 27 July 2017 / Revised: 31 August 2017 / Accepted: 8 September 2017 / Published: 12 September 2017
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Abstract
Independent living of elderly persons in their homes requires support that can be provided with modern assistive technologies. Monitoring of elderly persons behaviour delivers valuable information that can be used for diagnosis and detection of health problems as well as triggering alerts in
[...] Read more.
Independent living of elderly persons in their homes requires support that can be provided with modern assistive technologies. Monitoring of elderly persons behaviour delivers valuable information that can be used for diagnosis and detection of health problems as well as triggering alerts in emergency situations. The paper includes a description of the ultra wideband system developed within Networked InfrasTructure for Innovative home Care Solutions (NITICS) Active and Assisted Living (AAL) project. The system can be used as a component of AAL platforms. It delivers data on users localization and has a fall detector functionality. The system also provides access to raw measurement results from Microelectromechanical Systems (MEMS) sensors embedded in the device worn by the monitored person. These data can be used in solutions intended for elderly person’s behaviour investigation. The system was investigated under laboratory conditions as well as in home environment. The detailed system description and results of performed tests are included in the article. Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Open AccessArticle SNORAP: A Device for the Correction of Impaired Sleep Health by Using Tactile Stimulation for Individuals with Mild and Moderate Sleep Disordered Breathing
Sensors 2017, 17(9), 2006; doi:10.3390/s17092006
Received: 29 June 2017 / Revised: 30 August 2017 / Accepted: 31 August 2017 / Published: 1 September 2017
PDF Full-text (3523 KB) | HTML Full-text | XML Full-text
Abstract
Sleep physiology and sleep hygiene play significant roles in maintaining the daily lives of individuals given that sleep is an important physiological need to protect the functions of the human brain. Sleep disordered breathing (SDB) is an important disease that disturbs this need.
[...] Read more.
Sleep physiology and sleep hygiene play significant roles in maintaining the daily lives of individuals given that sleep is an important physiological need to protect the functions of the human brain. Sleep disordered breathing (SDB) is an important disease that disturbs this need. Snoring and Obstructive Sleep Apnea Syndrome (OSAS) are clinical conditions that affect all body organs and systems that intermittently, repeatedly, with at least 10 s or more breathing stops that decrease throughout the night and disturb sleep integrity. The aim of this study was to produce a new device for the treatment of patients especially with position and rapid eye movement (REM)-dependent mild and moderate OSAS. For this purpose, the main components of the device (the microphone (snore sensor), the heart rate sensor, and the vibration motor, which we named SNORAP) were applied to five volunteer patients (male, mean age: 33.2, body mass index mean: 29.3). After receiving the sound in real time with the microphone, the snoring sound was detected by using the Audio Fingerprint method with a success rate of 98.9%. According to the results obtained, the severity and the number of the snoring of the patients using SNORAP were found to be significantly lower than in the experimental conditions in the apnea hypopnea index (AHI), apnea index, hypopnea index, in supine position’s AHI, and REM position’s AHI before using SNORAP (Paired Sample Test, p < 0.05). REM sleep duration and nocturnal oxygen saturation were significantly higher when compared to the group not using the SNORAP (Paired Sample Test, p < 0.05). Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Open AccessArticle USEQ: A Short Questionnaire for Satisfaction Evaluation of Virtual Rehabilitation Systems
Sensors 2017, 17(7), 1589; doi:10.3390/s17071589
Received: 28 April 2017 / Revised: 3 July 2017 / Accepted: 4 July 2017 / Published: 7 July 2017
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Abstract
New emerging technologies have proven their efficacy in aiding people in their rehabilitation. The tests that are usually used to evaluate usability (in general) or user satisfaction (in particular) of this technology are not specifically focused on virtual rehabilitation and patients. The objective
[...] Read more.
New emerging technologies have proven their efficacy in aiding people in their rehabilitation. The tests that are usually used to evaluate usability (in general) or user satisfaction (in particular) of this technology are not specifically focused on virtual rehabilitation and patients. The objective of this contribution is to present and evaluate the USEQ (User Satisfaction Evaluation Questionnaire). The USEQ is a questionnaire that is designed to properly evaluate the satisfaction of the user (which constitutes part of usability) in virtual rehabilitation systems. Forty patients with balance disorders completed the USEQ after their first session with ABAR (Active Balance Rehabilitation), which is a virtual rehabilitation system that is designed for the rehabilitation of balance disorders. Internal consistency analysis and exploratory factor analysis were carried out to identify the factor structure of the USEQ. The six items of USEQ were significantly associated with each other, and the Cronbach alpha coefficient for the questionnaire was 0.716. In an analysis of the principal components, a one-factor solution was considered to be appropriate. The findings of the study suggest that the USEQ is a reliable questionnaire with adequate internal consistency. With regard to patient perception, the patients found the USEQ to be an easy-to-understand questionnaire with a convenient number of questions. Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Open AccessArticle A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction
Sensors 2017, 17(7), 1552; doi:10.3390/s17071552
Received: 7 April 2017 / Revised: 19 June 2017 / Accepted: 28 June 2017 / Published: 3 July 2017
PDF Full-text (11908 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient’s upcoming movements using electroencephalography (EEG) or electromyography (EMG). However,
[...] Read more.
A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient’s upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision. Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Open AccessArticle A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG)-Based Emotion Recognition
Sensors 2017, 17(5), 1014; doi:10.3390/s17051014
Received: 20 March 2017 / Revised: 23 April 2017 / Accepted: 26 April 2017 / Published: 3 May 2017
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Abstract
Electroencephalography (EEG)-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain
[...] Read more.
Electroencephalography (EEG)-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM) is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects). Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR) can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46% and 6.84%, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE), which achieves values of 77.88% and 7.33% on average, respectively. For the online analysis, the average classification accuracy and standard deviation of ASFM in the subject-to-subject evaluation for all the 15 subjects in a dataset was 75.11% and 7.65%, respectively, gaining a significant performance improvement compared to the best baseline LR which achieves 56.38% and 7.48%, respectively. The experimental results confirm the effectiveness of the proposed method relative to state-of-the-art methods. Moreover, computational efficiency of the proposed ASFM method is much better than standard domain adaptation; if the numbers of training samples and test samples are controlled within certain range, it is suitable for real-time classification. It can be concluded that ASFM is a useful and effective tool for decreasing domain discrepancy and reducing performance degradation across subjects and sessions in the field of EEG-based emotion recognition. Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Open AccessArticle Support System to Improve Reading Activity in Parkinson’s Disease and Essential Tremor Patients
Sensors 2017, 17(5), 1006; doi:10.3390/s17051006
Received: 15 February 2017 / Revised: 19 April 2017 / Accepted: 25 April 2017 / Published: 3 May 2017
PDF Full-text (3665 KB) | HTML Full-text | XML Full-text
Abstract
The use of information and communication technologies (ICTs) to improve the quality of life of people with chronic and degenerative diseases is a topic receiving much attention nowadays. We can observe that new technologies have driven numerous scientific projects in e-Health, encompassing Smart
[...] Read more.
The use of information and communication technologies (ICTs) to improve the quality of life of people with chronic and degenerative diseases is a topic receiving much attention nowadays. We can observe that new technologies have driven numerous scientific projects in e-Health, encompassing Smart and Mobile Health, in order to address all the matters related to data processing and health. Our work focuses on helping to improve the quality of life of people with Parkinson’s Disease (PD) and Essential Tremor (ET) by means of a low-cost platform that enables them to read books in an easy manner. Our system is composed of two robotic arms and a graphical interface developed for Android platforms. After several tests, our proposal has achieved a 96.5% accuracy for A4 80 gr non-glossy paper. Moreover, our system has outperformed the state-of-the-art platforms considering different types of paper and inclined surfaces. The feedback from ET and PD patients was collected at “La Princesa” University Hospital in Madrid and was used to study the user experience. Several features such as ease of use, speed, correct behavior or confidence were measured via patient feedback, and a high level of satisfaction was awarded to most of them. According to the patients, our system is a promising tool for facilitating the activity of reading. Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Open AccessArticle Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction
Sensors 2017, 17(4), 879; doi:10.3390/s17040879
Received: 22 December 2016 / Revised: 6 April 2017 / Accepted: 7 April 2017 / Published: 17 April 2017
Cited by 3 | PDF Full-text (13789 KB) | HTML Full-text | XML Full-text
Abstract
Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs)
[...] Read more.
Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach. Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Open AccessArticle Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking
Sensors 2017, 17(2), 340; doi:10.3390/s17020340
Received: 21 November 2016 / Revised: 5 February 2017 / Accepted: 7 February 2017 / Published: 10 February 2017
Cited by 1 | PDF Full-text (1869 KB) | HTML Full-text | XML Full-text
Abstract
The wearable inertial/magnetic sensor based human motion analysis plays an important role in many biomedical applications, such as physical therapy, gait analysis and rehabilitation. One of the main challenges for the lower body bio-motion analysis is how to reliably provide position estimations of
[...] Read more.
The wearable inertial/magnetic sensor based human motion analysis plays an important role in many biomedical applications, such as physical therapy, gait analysis and rehabilitation. One of the main challenges for the lower body bio-motion analysis is how to reliably provide position estimations of human subject during walking. In this paper, we propose a particle filter based human position estimation method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity update (ZUPT), but also applies map information to further correct the acceleration double integration drift and thus improve estimation accuracy. In the proposed method, a simple stance phase detector is designed to identify the stance phase of a gait cycle based on gyroscope measurements. For the non-stance phase during a gait cycle, an acceleration control variable derived from ZUPT information is introduced in the process model, while vector map information is taken as binary pseudo-measurements to further enhance position estimation accuracy and reduce uncertainty of walking trajectories. A particle filter is then designed to fuse ZUPT information and binary pseudo-measurements together. The proposed human position estimation method has been evaluated with closed-loop walking experiments in indoor and outdoor environments. Results of comparison study have illustrated the effectiveness of the proposed method for application scenarios with useful map information. Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Open AccessArticle A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field
Sensors 2016, 16(12), 2018; doi:10.3390/s16122018
Received: 11 October 2016 / Revised: 17 November 2016 / Accepted: 25 November 2016 / Published: 29 November 2016
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Abstract
This paper proposes a neuromusculoskeletal (NMS) model to predict individual muscle force during elbow flexion and extension. Four male subjects were asked to do voluntary elbow flexion and extension. An inertial sensor and surface electromyography (sEMG) sensors were attached to subject's forearm. Joint
[...] Read more.
This paper proposes a neuromusculoskeletal (NMS) model to predict individual muscle force during elbow flexion and extension. Four male subjects were asked to do voluntary elbow flexion and extension. An inertial sensor and surface electromyography (sEMG) sensors were attached to subject's forearm. Joint angle calculated by fusion of acceleration and angular rate using an extended Kalman filter (EKF) and muscle activations obtained from the sEMG signals were taken as the inputs of the proposed NMS model to determine individual muscle force. The result shows that our NMS model can predict individual muscle force accurately, with the ability to reflect subject-specific joint dynamics and neural control solutions. Our method incorporates sEMG and motion data, making it possible to get a deeper understanding of neurological, physiological, and anatomical characteristics of human dynamic movement. We demonstrate the potential of the proposed NMS model for evaluating the function of upper limb movements in the field of neurorehabilitation. Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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