Special Issue "Intelligent Health Services Based on Biomedical Smart Sensors"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2019).

Special Issue Editors

Prof. Dr. Ricardo Colomo-Palacios
Website
Guest Editor
Department of Computer Science, Østfold University College, Halden, Norway
Interests: information systems; human factors in computing; project management in information-systems development; global and distributed software-engineering, systems, services, and software process improvement and innovation; management information systems; business software; innovation in IT
Special Issues and Collections in MDPI journals
Prof. Dr. Juan A. Gómez-Pulido
Website
Guest Editor
Department of Technologies of Computers and Communications, Universidad de Extremadura, Cáceres, Spain
Interests: optimization and computational intelligence; machine learning; reconfigurable computing and FPGAs; wireless communications; bioinformatics
Special Issues and Collections in MDPI journals
Dr. Alfredo J. Pérez
Website
Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: privacy; mobile/ubiquitous computing and sensing; human-activity recognition; multiobjective optimization and its application to computer networks and scheduling; CS education

Special Issue Information

Dear Colleagues,

Advances in computer technologies are driving significant changes in medical care by shifting the hospital-centered paradigm to a patient-centered one. The focus on disease is replaced with the orientation to wellness. Monitoring and analyzing biomedical variables are key activities required for diagnosis and health care. The automation of these activities by means of computing systems allows processing massive volumes of data collected from biomedical sensors, leading to useful field applications to health personnel. This is particularly interesting to predict the diagnosis of certain diseases suffered by the most vulnerable groups, like the elderly. New systems, applications, developments, models, and research that make use of monitored medical data are envisaged to bring differentiated services to the society.

Big Data and Machine Learning provide significant potential for this purpose, leading to new applications, more effective operations, and more human approaches. These methodologies enable digging massive databases, enhancing the knowledge base and producing new data model-based applications and services for society. The most significant requirement for these technologies is the availability of databases to mine them or to train and test the models. However, medical information is hard to obtain for administrative issues, so other efficient alternatives to collect data are required. The use of smart sensors requires strategies to minimize interference with the work of health personnel and have a minimal impact on monitored patients. The implementation of health-care services with these technologies will probably save costs, but the benefits will be better perceived as an increment of the satisfaction of patients and personnel. Moreover, these technological implementations will expand accessibility to new areas.

This Special Issue provides a collection of papers of original advances in health applications and services propelled by artificial intelligence, big data, and machine learning, supported by the design of biosensor systems for the construction of trustable medical databases. Papers about advancements in pattern recognition techniques, intelligent algorithms, automated data analysis, sensor-fusion techniques, and smart sensing, which focus on existing issues in biomedical sensing and diagnostics, are welcomed.

Prof. Dr. Ricardo Colomo-Palacios
Prof. Dr. Juan A. Gómez-Pulido
Dr. Alfredo J. Pérez
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. Applied Sciences 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.

Keywords

  • extraction information from biomedical sensors
  • Internet of Things
  • biomedical data
  • cloud computing in health
  • data mining and big data analysis
  • intelligent systems for health
  • machine and deep learning
  • diagnostic and predictive analytics
  • health systems, healthcare, and wellness
  • activity recognition in health care
  • data authentication and security
  • privacy-preserving systems for healthcare

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks
Appl. Sci. 2019, 9(19), 3970; https://doi.org/10.3390/app9193970 - 22 Sep 2019
Cited by 1
Abstract
Abnormal foot postures during gait are common sources of pain and pathologies of the lower limbs. Measurements of foot plantar pressures in both dynamic and static conditions can detect these abnormal foot postures and prevent possible pathologies. In this work, a plantar pressure [...] Read more.
Abnormal foot postures during gait are common sources of pain and pathologies of the lower limbs. Measurements of foot plantar pressures in both dynamic and static conditions can detect these abnormal foot postures and prevent possible pathologies. In this work, a plantar pressure measurement system is developed to identify areas with higher or lower pressure load. This system is composed of an embedded system placed in the insole and a user application. The instrumented insole consists of a low-power microcontroller, seven pressure sensors and a low-energy bluetooth module. The user application receives and shows the insole pressure information in real-time and, finally, provides information about the foot posture. In order to identify the different pressure states and obtain the final information of the study with greater accuracy, a Deep Learning neural network system has been integrated into the user application. The neural network can be trained using a stored dataset in order to obtain the classification results in real-time. Results prove that this system provides an accuracy over 90% using a training dataset of 3000+ steps from 6 different users. Full article
(This article belongs to the Special Issue Intelligent Health Services Based on Biomedical Smart Sensors)
Show Figures

Figure 1

Open AccessArticle
PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control
Appl. Sci. 2019, 9(15), 3037; https://doi.org/10.3390/app9153037 - 28 Jul 2019
Cited by 1
Abstract
Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are [...] Read more.
Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables. Full article
(This article belongs to the Special Issue Intelligent Health Services Based on Biomedical Smart Sensors)
Show Figures

Graphical abstract

Open AccessArticle
Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases
Appl. Sci. 2019, 9(14), 2858; https://doi.org/10.3390/app9142858 - 18 Jul 2019
Abstract
Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the large volume of information requires the development of [...] Read more.
Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the large volume of information requires the development of algorithms that handle natural language efficiently. In the bibliography, it is possible to find different techniques to carry out these infodemiology studies. However, as far as our knowledge, there are no comprehensive studies that compare the accuracy of these techniques. Consequently, we conducted an infodemiology-based study to extract positive or negative utterances related to infectious diseases so that future syndromic surveillance systems can be improved. The contribution of this paper is two-fold. On the one hand, we use Twitter to compile and label a balanced corpus of infectious diseases with 6164 utterances written in Spanish and collected from Central America. On the other hand, we compare two statistical-models: word-grams and char-grams. The experimentation involved the analysis of different gram sizes, different partitions of the corpus, and two machine-learning classifiers: Random-Forest and Sequential Minimal Optimization. The results reach a 90.80% of accuracy applying the char-grams model with five-char-gram sequences. As a final contribution, the compiled corpus is released. Full article
(This article belongs to the Special Issue Intelligent Health Services Based on Biomedical Smart Sensors)
Show Figures

Figure 1

Open AccessArticle
Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation
Appl. Sci. 2019, 9(14), 2795; https://doi.org/10.3390/app9142795 - 12 Jul 2019
Abstract
Assessment of human locomotion using wearable sensors is an efficient way of getting useful information about human health status, and determining human locomotion abnormalities. Wearable sensors do not only provide the opportunity to assess the behavior of patients as it happens in their [...] Read more.
Assessment of human locomotion using wearable sensors is an efficient way of getting useful information about human health status, and determining human locomotion abnormalities. Wearable sensors do not only provide the opportunity to assess the behavior of patients as it happens in their daily life activities, but also provide quantitative, meaningful feedback data of patients to their therapists. This can pinpoint the cause of problems and help in maximizing their recovery rates. The popularity of using wearable sensors has received attention from a number of researchers from both the academic and industrial fields in the past few years. The different types of wearable sensors have given birth to the realization of a standard measurement model that can support different types of applications. Wireless body area networks (WBANs) are starting to replace traditional healthcare systems by enabling long-term monitoring of patients and tele-rehabilitation, especially those who suffer from chronic diseases. This paper investigates using wearable accelerometers and surface electromyography (EMG) in human locomotion monitoring for tele-rehabilitation. It proposes and investigates new positions for the proposed sensors, and compares the measured signals to similar techniques proposed in the literature. Realistic measurements show that the proposed positions of surface EMG sensors (on the forearm muscles) provide more reliable results in the classification of motion abnormality as compared to the sensor positions proposed in the literature (biceps muscles). Seven statistical features were extracted from accelerometer signals, and four time domain (TD) features are extracted from EMG signals. These features are used to construct six machine learning classifiers for automatic classification of Parkinson’s tremor. These models include; decision tree (DT), linear discriminant analysis analysis (LDA), k-nearest-neighbor (kNN), support vector machine (SVM), boosted tree and bagged tree classifiers. The performance of the applied classifiers is analyzed using accuracy, confusion matrix, and area under ROC (AUC) curve. The results are also compared to corresponding findings in the literature. The experimental results show that the highest classification accuracy is achieved when using the proposed measurement set and bagged tree classifier with a value of 99.6%. Full article
(This article belongs to the Special Issue Intelligent Health Services Based on Biomedical Smart Sensors)
Show Figures

Figure 1

Open AccessArticle
An Agile Approach to Improve the Usability of a Physical Telerehabilitation Platform
Appl. Sci. 2019, 9(3), 480; https://doi.org/10.3390/app9030480 - 30 Jan 2019
Cited by 3
Abstract
The goal of a telerehabilitation platform is to safely and securely facilitate the rehabilitation of patients through the use of telecommunication technologies complemented with the use of biomedical smart sensors. The purpose of this study was to perform a usability evaluation of a [...] Read more.
The goal of a telerehabilitation platform is to safely and securely facilitate the rehabilitation of patients through the use of telecommunication technologies complemented with the use of biomedical smart sensors. The purpose of this study was to perform a usability evaluation of a telerehabilitation platform. To improve the level of usability, the researchers developed and proposed an iterative process. The platform uses a digital representation of the patient which duplicates the therapeutic exercise being executed by the patient; this is detected by a Kinect camera and sensors in real time. This study used inspection methods to perform a usability evaluation of an exploratory prototype of a telerehabilitation platform. In addition, a cognitive workload assessment was performed to complement the usability evaluation. Users were involved through all the stages of the iterative refinement process. Usability issues were progressively reduced from the first iteration to the fourth iteration according to improvements which were developed and applied by the experts. Usability issues originally cataloged as catastrophic were reduced to zero, major usability problems were reduced to ten (2.75%) and minor usability problems were decreased to 141 (38.74%). This study also intends to serve as a guide to improve the usability of e-Health systems in alignment with the software development cycle. Full article
(This article belongs to the Special Issue Intelligent Health Services Based on Biomedical Smart Sensors)
Show Figures

Figure 1

Review

Jump to: Research

Open AccessReview
Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
Appl. Sci. 2020, 10(1), 234; https://doi.org/10.3390/app10010234 - 27 Dec 2019
Cited by 1
Abstract
More than 8.6 million people suffer from neurological disorders that affect their gait and balance. Physical therapists provide interventions to improve patient’s functional outcomes, yet balance and gait are often evaluated in a subjective and observational manner. The use of quantitative methods allows [...] Read more.
More than 8.6 million people suffer from neurological disorders that affect their gait and balance. Physical therapists provide interventions to improve patient’s functional outcomes, yet balance and gait are often evaluated in a subjective and observational manner. The use of quantitative methods allows for assessment and tracking of patient progress during and after rehabilitation or for early diagnosis of movement disorders. This paper surveys the state-of-the-art in wearable sensor technology in gait, balance, and range of motion research. It serves as a point of reference for future research, describing current solutions and challenges in the field. A two-level taxonomy of rehabilitation assessment is introduced with evaluation metrics and common algorithms utilized in wearable sensor systems. Full article
(This article belongs to the Special Issue Intelligent Health Services Based on Biomedical Smart Sensors)
Show Figures

Figure 1

Back to TopTop