1,072 research outputs found
Network-assisted Smart Access Point Selection for Pervasive Real-time mHealth Applications
AbstractDue to the fast evolution of wireless access networks and high-performance mobile devices together with the spreading of wearable medical sensors, electronic healthcare (eHealth) services have recently started to receive more and more attention, especially in the mobile Health (mHealth) domain. The vast majority of mHealth services require strict medical level Quality of Service (QoS) and Quality of Experience (QoE) provision. Emergency use-cases, remote patient monitoring, tele-consultation and guided surgical intervention require real-time communication and appropriate connection quality. The increasing significance of different overlapping wireless accesses makes possible to provide the required network resources for ubiquitous and pervasive mHealth applications. Aiming to support such use-cases in a heterogeneous network environment, we propose a network-assisted intelligent access point selection scheme for ubiquitous applications of Future Internet architectures focusing on real-time mobile telemedicine services. Our solution is able to discover nearby base stations that cover the current location of the mobile device efficiently and to trigger heterogeneous handovers based on the state and quality of the current access network. The solution is empirically evaluated in Wi-Fi networks used by real-life Android mobile devices and we observed that the scheme can improve the quality of mHealth applications and enhance traffic load balancing capabilities of wireless architectures
Sensor-based datasets for human activity recognition - a systematic review of literature
The research area of ambient assisted living has led to the development of activity recognition
systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and
the health care of the elderly and dependent people. However, before making them available to end users, it is
necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks
in experimental scenarios. For that reason, the scientific community has developed and provided a huge
amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which
techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and
is key to further progress in this area of research. This work presents a systematic review of the literature
of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables
taken from indexed publications related to this field was performed. The sources of information are journals,
proceedings, and books located in specialized databases. The analyzed variables characterize publications
by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed
identification of the data set most used by researchers. On the other hand, the descriptive and functional
variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation,
representation, feature selection, balancing and addition of instances, and classifier used for recognition.
This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most
appropriate dataset to evaluate ARS and the classification techniques that generate better results
Sensor-based datasets for human activity recognition - a systematic review of literature
The research area of ambient assisted living has led to the development of activity recognition
systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and
the health care of the elderly and dependent people. However, before making them available to end users, it is
necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks
in experimental scenarios. For that reason, the scientific community has developed and provided a huge
amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which
techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and
is key to further progress in this area of research. This work presents a systematic review of the literature
of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables
taken from indexed publications related to this field was performed. The sources of information are journals,
proceedings, and books located in specialized databases. The analyzed variables characterize publications
by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed
identification of the data set most used by researchers. On the other hand, the descriptive and functional
variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation,
representation, feature selection, balancing and addition of instances, and classifier used for recognition.
This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most
appropriate dataset to evaluate ARS and the classification techniques that generate better results
Smart and Pervasive Healthcare
Smart and pervasive healthcare aims at facilitating better healthcare access, provision, and delivery by overcoming spatial and temporal barriers. It represents a shift toward understanding what patients and clinicians really need when placed within a specific context, where traditional face-to-face encounters may not be possible or sufficient. As such, technological innovation is a necessary facilitating conduit. This book is a collection of chapters written by prominent researchers and academics worldwide that provide insights into the design and adoption of new platforms in smart and pervasive healthcare. With the COVID-19 pandemic necessitating changes to the traditional model of healthcare access and its delivery around the world, this book is a timely contribution
Internet of things in health: Requirements, issues, and gaps
Background and objectives: The Internet of Things (IoT) paradigm has been extensively applied to several sectors in the last years, ranging from industry to smart cities. In the health domain, IoT makes possible new scenarios of healthcare delivery as well as collecting and processing health data in real time from sensors in order to make informed decisions. However, this domain is complex and presents several tech- nological challenges. Despite the extensive literature about this topic, the application of IoT in healthcare scarcely covers requirements of this sector. Methods: A literature review from January 2010 to February 2021 was performed resulting in 12,108 articles. After filtering by title, abstract, and content, 86 were eligible and examined according to three requirement themes: data lifecycle; trust, security, and privacy; and human-related issues. Results: The analysis of the reviewed literature shows that most approaches consider IoT application in healthcare merely as in any other domain (industry, smart cities…), with no regard of the specific requirements of this domain. Conclusions: Future effort s in this matter should be aligned with the specific requirements and needs of the health domain, so that exploiting the capabilities of the IoT paradigm may represent a meaningful step forward in the application of this technology in healthcare.ConsejerĂa de Conocimiento, InvestigaciĂłn y Universidad, Junta de AndalucĂa P18-TPJ - 307
Designing Human-Centered Collective Intelligence
Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence
Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition
Today, there are standard and well established procedures within the Human
Activity Recognition (HAR) pipeline. However, some of these conventional
approaches lead to accuracy overestimation. In particular, sliding windows for
data segmentation followed by standard random k-fold cross validation, produce
biased results. An analysis of previous literature and present-day studies,
surprisingly, shows that these are common approaches in state-of-the-art
studies on HAR. It is important to raise awareness in the scientific community
about this problem, whose negative effects are being overlooked. Otherwise,
publications of biased results lead to papers that report lower accuracies,
with correct unbiased methods, harder to publish. Several experiments with
different types of datasets and different types of classification models allow
us to exhibit the problem and show it persists independently of the method or
dataset
Performance evaluation of cooperation strategies for m-health services and applications
Health telematics are becoming a major improvement for patients’ lives, especially for
disabled, elderly, and chronically ill people. Information and communication technologies have
rapidly grown along with the mobile Internet concept of anywhere and anytime connection.
In this context, Mobile Health (m-Health) proposes healthcare services delivering, overcoming
geographical, temporal and even organizational barriers. Pervasive and m-Health services aim
to respond several emerging problems in health services, including the increasing number of
chronic diseases related to lifestyle, high costs in existing national health services, the need
to empower patients and families to self-care and manage their own healthcare, and the need
to provide direct access to health services, regardless the time and place. Mobile Health (m-
Health) systems include the use of mobile devices and applications that interact with patients
and caretakers. However, mobile devices have several constraints (such as, processor, energy,
and storage resource limitations), affecting the quality of service and user experience. Architectures
based on mobile devices and wireless communications presents several challenged issues
and constraints, such as, battery and storage capacity, broadcast constraints, interferences, disconnections,
noises, limited bandwidths, and network delays. In this sense, cooperation-based
approaches are presented as a solution to solve such limitations, focusing on increasing network
connectivity, communication rates, and reliability. Cooperation is an important research topic
that has been growing in recent years. With the advent of wireless networks, several recent
studies present cooperation mechanisms and algorithms as a solution to improve wireless networks
performance. In the absence of a stable network infrastructure, mobile nodes cooperate
with each other performing all networking functionalities. For example, it can support intermediate
nodes forwarding packets between two distant nodes.
This Thesis proposes a novel cooperation strategy for m-Health services and applications.
This reputation-based scheme uses a Web-service to handle all the nodes reputation and networking
permissions. Its main goal is to provide Internet services to mobile devices without
network connectivity through cooperation with neighbor devices. Therefore resolving the above
mentioned network problems and resulting in a major improvement for m-Health network architectures
performances. A performance evaluation of this proposal through a real network
scenario demonstrating and validating this cooperative scheme using a real m-Health application
is presented. A cryptography solution for m-Health applications under cooperative environments,
called DE4MHA, is also proposed and evaluated using the same real network scenario and
the same m-Health application. Finally, this work proposes, a generalized cooperative application
framework, called MobiCoop, that extends the incentive-based cooperative scheme for
m-Health applications for all mobile applications. Its performance evaluation is also presented
through a real network scenario demonstrating and validating MobiCoop using different mobile
applications
Big data analytics and internet of things for personalised healthcare: opportunities and challenges
With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (AI) can provide useful methods and solutions in monitoring, diagnosis, and self-management of health issues for a better personalized healthcare. In this paper, we identify the current personalized healthcare trends and challenges. Then, propose an architecture to support big data analytics using POC test results of an individual. The proposed architecture can facilitate an integrated and self-managed healthcare as well as remote patient care by adapting three popular machine learning algorithms to leverage the current trends in IoT, big data infrastructures and data analytics for advancing personalized healthcare of the future
Mobile Personal Healthcare System for Non-Invasive, Pervasive and Continuous Blood Pressure Monitoring: A Feasibility Study
Background: Smartphone-based blood pressure (BP) monitor using photoplethysmogram (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control ofhypertension (HT).
Objective: This study aimed to develop a mobile personal healthcare system for non-invasive, pervasive, and continuous estimation of BP level and variability to be user-friendly to elderly.
Methods: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless and wearable PPG-only sensor, and a native purposely-designed smartphone application using multilayer perceptron machine learning techniques from raw signals. We performed a pilot study with three elder adults (mean age 61.3 ± 1.5 years; 66% women) to test usability and accuracy of the smartphone-based BP monitor.
Results: The employed artificial neural network (ANN) model performed with high accuracy in terms of predicting the reference BP values of our validation sample (n=150). On average, our approach predicted BP measures with accuracy \u3e90% and correlations \u3e0.90 (P \u3c .0001). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg.
Conclusions: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of healthcare, particularly in rural zones, areas lacking physicians, and solitary elderly populations
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