115 research outputs found
IoT Platform for COVID-19 Prevention and Control: A Survey
As a result of the worldwide transmission of severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has
evolved into an unprecedented pandemic. Currently, with unavailable
pharmaceutical treatments and vaccines, this novel coronavirus results in a
great impact on public health, human society, and global economy, which is
likely to last for many years. One of the lessons learned from the COVID-19
pandemic is that a long-term system with non-pharmaceutical interventions for
preventing and controlling new infectious diseases is desirable to be
implemented. Internet of things (IoT) platform is preferred to be utilized to
achieve this goal, due to its ubiquitous sensing ability and seamless
connectivity. IoT technology is changing our lives through smart healthcare,
smart home, and smart city, which aims to build a more convenient and
intelligent community. This paper presents how the IoT could be incorporated
into the epidemic prevention and control system. Specifically, we demonstrate a
potential fog-cloud combined IoT platform that can be used in the systematic
and intelligent COVID-19 prevention and control, which involves five
interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring,
Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and
SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art
literatures of these five interventions to present the capabilities of IoT in
countering against the current COVID-19 pandemic or future infectious disease
epidemics.Comment: 12 pages; Submitted to IEEE Internet of Things Journa
Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research
Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders
Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions
With the advent of Digital Therapeutics (DTx), the development of software as
a medical device (SaMD) for mobile and wearable devices has gained significant
attention in recent years. Existing DTx evaluations, such as randomized
clinical trials, mostly focus on verifying the effectiveness of DTx products.
To acquire a deeper understanding of DTx engagement and behavioral adherence,
beyond efficacy, a large amount of contextual and interaction data from mobile
and wearable devices during field deployment would be required for analysis. In
this work, the overall flow of the data-driven DTx analytics is reviewed to
help researchers and practitioners to explore DTx datasets, to investigate
contextual patterns associated with DTx usage, and to establish the (causal)
relationship of DTx engagement and behavioral adherence. This review of the key
components of data-driven analytics provides novel research directions in the
analysis of mobile sensor and interaction datasets, which helps to iteratively
improve the receptivity of existing DTx.Comment: This paper has been accepted by the IEEE/CAA Journal of Automatica
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Personalized data analytics for internet-of-things-based health monitoring
The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months
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Artificial Intelligence Augmented Mechatronic Design for Health Sensing Applications
Mobile/wearable health sensing system has attracted more attention in recent years due to the emergence of the telehealth industry. Especially in the last two years, the world has witnessed the most chaotic time caused by the COVID-19 virus. Medical staff is exhausted with the workloads that they have not been in before. Working in extreme conditions while facing the shortage of Personal Protective Equipment has put them over their limit. As a result, the majority number of patients are set to be self-treatment at home, where they cannot receive proper care and treatments when things get worst. Consequently, there is a massive jump in the fatality rate. In this case, the role of a personalized health monitoring and telehealth system with a clinical grade evaluation is essential to maintain an appropriate number of patients admitted to the hospitals while still provides adequate treatment for those staying at home. Nonetheless, current telehealth solutions primarily concentrate on delivering recommendations, reminders, and interacting with patients. What’s missing is an emphasis on mobile clinical-grade health sensing devices, which are essential for remote monitoring but get little attention. To achieve these goals, it is necessary to combine state-of-the-art Artificial Intelligence (AI) with appropriate mechatronic design to create a novel health-sensing modality that will allow for both augmentation and downsizing. I present a corpus of work that explores three applications based on the foundations of AI-augmented mechatronic design: (1) optical sensing for oxygen saturation measurement, (2) optical sensing with an actuator for in-ear blood pressure monitoring, and (3) capacitive sensing-based moisture vapor for measuring lung function indicators. It is anticipated that the effort will result in the establishment of a preliminary AI-augmented mechatronic framework for improving sensor design. This technique is not limited to healthcare platforms; rather, it may be adapted to work with various applications.</p
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition
Human activity recognition (HAR) in wearable computing is typically based on
direct processing of sensor data. Sensor readings are translated into
representations, either derived through dedicated preprocessing, or integrated
into end-to-end learning. Independent of their origin, for the vast majority of
contemporary HAR, those representations are typically continuous in nature.
That has not always been the case. In the early days of HAR, discretization
approaches have been explored - primarily motivated by the desire to minimize
computational requirements, but also with a view on applications beyond mere
recognition, such as, activity discovery, fingerprinting, or large-scale
search. Those traditional discretization approaches, however, suffer from
substantial loss in precision and resolution in the resulting representations
with detrimental effects on downstream tasks. Times have changed and in this
paper we propose a return to discretized representations. We adopt and apply
recent advancements in Vector Quantization (VQ) to wearables applications,
which enables us to directly learn a mapping between short spans of sensor data
and a codebook of vectors, resulting in recognition performance that is
generally on par with their contemporary, continuous counterparts - sometimes
surpassing them. Therefore, this work presents a proof-of-concept for
demonstrating how effective discrete representations can be derived, enabling
applications beyond mere activity classification but also opening up the field
to advanced tools for the analysis of symbolic sequences, as they are known,
for example, from domains such as natural language processing. Based on an
extensive experimental evaluation on a suite of wearables-based benchmark HAR
tasks, we demonstrate the potential of our learned discretization scheme and
discuss how discretized sensor data analysis can lead to substantial changes in
HAR
SleepGuard:capturing rich sleep information using smartwatch sensing data
Sleep is an important part of our daily routine – we spend about one-third of our time doing it. By tracking sleep-related events and activities, sleep monitoring provides decision support to help us understand sleep quality and causes of poor sleep. Wearable devices provide a new way for sleep monitoring, allowing us to monitor sleep from the comfort of our own home. However, existing solutions do not take full advantage of the rich sensor data provided by these devices. In this paper, we present the design and development of SleepGuard, a novel approach to track a wide range of sleep-related events using smartwatches. We show that using merely a single smartwatch, it is possible to capture a rich amount of information about sleep events and sleeping context, including body posture and movements, acoustic events, and illumination conditions. We demonstrate that through these events it is possible to estimate sleep quality and identify factors affecting it most. We evaluate our approach by conducting extensive experiments involved fifteen users across a 2-week period. Our experimental results show that our approach can track a richer set of sleep events, provide better decision support for evaluating sleep quality, and help to identify causes for sleep problems compared to prior work
Digital Twins for Health: Opportunities, Barriers and a Path Forward
The concept of precision medicine involves tailoring medical interventions to each patient’s specific needs, considering factors such as their genetic makeup, lifestyle, environment and response to therapies. The emergence of digital twin (DT) technology is anticipated to enable such customization. The healthcare field is, thus, increasingly exploring the use of digital twins (DTs), benefiting from successful proof of concept demonstrated in various industries. If their full potential is realized, DTs have the capability to revolutionize connected care and reshape the management of lifestyle, health, wellness and chronic diseases in the future. However, the realization of DTs’ full potential in healthcare is currently impeded by technical, regulatory and ethical challenges. In this chapter, we map the current applications of DTs in healthcare, with a primary focus on precision medicine. We also explore their potential applications in clinical trial design and hospital operations. We identify the key enablers of DTs in healthcare and discuss the opportunities and barriers that foster or hinder their larger and faster diffusion. By providing a comprehensive view of the current landscape, opportunities and challenges, we aim to contribute to DTs’ ongoing development and help policymakers facilitate the growth of DTs’ application in healthcare
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