1,240 research outputs found
How 5G wireless (and concomitant technologies) will revolutionize healthcare?
The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to âensure healthy lives and promote well-being for all at all agesâ. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution
FIT A Fog Computing Device for Speech TeleTreatments
There is an increasing demand for smart fogcomputing gateways as the size of
cloud data is growing. This paper presents a Fog computing interface (FIT) for
processing clinical speech data. FIT builds upon our previous work on EchoWear,
a wearable technology that validated the use of smartwatches for collecting
clinical speech data from patients with Parkinson's disease (PD). The fog
interface is a low-power embedded system that acts as a smart interface between
the smartwatch and the cloud. It collects, stores, and processes the speech
data before sending speech features to secure cloud storage. We developed and
validated a working prototype of FIT that enabled remote processing of clinical
speech data to get speech clinical features such as loudness, short-time
energy, zero-crossing rate, and spectral centroid. We used speech data from six
patients with PD in their homes for validating FIT. Our results showed the
efficacy of FIT as a Fog interface to translate the clinical speech processing
chain (CLIP) from a cloud-based backend to a fog-based smart gateway.Comment: 3 pages, 5 figures, 1 table, 2nd IEEE International Conference on
Smart Computing SMARTCOMP 2016, Missouri, USA, 201
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Technology and Caregiving: Emerging Interventions and Directions for Research.
An array of technology-based interventions has increasingly become available to support family caregivers, primarily focusing on health and well-being, social isolation, financial, and psychological support. More recently the emergence of new technologies such as mobile and cloud, robotics, connected sensors, virtual/augmented/mixed reality, voice, and the evermore ubiquitous tools supported by advanced data analytics, coupled with the integration of multiple technologies through platform solutions, have opened a new era of technology-enabled interventions that can empower and support family caregivers. This paper proposes a conceptual framework for identifying and addressing the challenges that may need to be overcome to effectively apply technology-enabled solutions for family caregivers. The paper identifies a number of challenges that either moderate or mediate the full use of technologies for the benefit of caregivers. The challenges include issues related to equity, inclusion, and access; ethical concerns related to privacy and security; political and regulatory factors affecting interoperability and lack of standards; inclusive/human-centric design and issues; and inherent economic and distribution channel difficulties. The paper concludes with a summary of research questions and issues that form a framework for global research priorities
RFID Localisation For Internet Of Things Smart Homes: A Survey
The Internet of Things (IoT) enables numerous business opportunities in
fields as diverse as e-health, smart cities, smart homes, among many others.
The IoT incorporates multiple long-range, short-range, and personal area
wireless networks and technologies into the designs of IoT applications.
Localisation in indoor positioning systems plays an important role in the IoT.
Location Based IoT applications range from tracking objects and people in
real-time, assets management, agriculture, assisted monitoring technologies for
healthcare, and smart homes, to name a few. Radio Frequency based systems for
indoor positioning such as Radio Frequency Identification (RFID) is a key
enabler technology for the IoT due to its costeffective, high readability
rates, automatic identification and, importantly, its energy efficiency
characteristic. This paper reviews the state-of-the-art RFID technologies in
IoT Smart Homes applications. It presents several comparable studies of RFID
based projects in smart homes and discusses the applications, techniques,
algorithms, and challenges of adopting RFID technologies in IoT smart home
systems.Comment: 18 pages, 2 figures, 3 table
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Smart Healthcare solutions in China and Europe, an international business perspective
The thesis is part of the Marie Curie Fellowship project addressing health related challenges with IoT solutions. The author tries to address the challenge for the implementation of telehealth solutions by finding out the demand of the telehealth solution in selected European economies and in China (chapter 1), analyzing the emerging business models for telehealth solution ecosystems in China (chapter 2), how to integrate telehealth solutions with institutional stakeholders (chapter 3) and why are elderly users willing to use telehealth solutions in China.
Chapter 1 and chapter 2 form the theoretical background for empirical work in chapter 3 and chapter 4. The thesis addressed four research questions, namely âWhich societal and social-economics unmet needs that Internet of Healthcare Things can help to resolve?â, âWhat are the business model innovation for tech companies in China for the smart health industry?â, âWhat are the facilitators and hurdles for implementing telehealth solutionsâ, âAre elderly users willing to use telehealth solutions in China?â.
Both qualitative study and quantitative analysis has been made based on data collected by in depth interviews with stakeholders, focus group study work with urban and rural residents in China.
The digital platform framework was used in chapter 2 as the theoretical framework where as the stakeholder power mapping framework was used in chapter 3. The discretion choice experiment was used in chapter 4 to design questionnaire study while ordered logit regression was used to analyze the data.
Telehealth solutions have great potential to fill in the gap for lack of community healthcare and ensuring health continuity between home care setting, community healthcare and hospitals. There is strong demand for such solutions if they can prove the medical value in managing chronic disease by raising health awareness and lowering health risks by changing the patientsâ lifestyle. Analyzing how to realize the value for preventive healthcare by proving the health-economic value of digital health solutions (telehealth solutions) is the focus of research.
There remain hurdles to build trust for telehealth solutions and the use of AI in healthcare. Next step of research can also be extended to addressing such challenges by analyzing how to improve the transparency of algorithms by disclosing the data source, and how the algorithms were built. Further research can be done on data interoperability between the EHR systems and telehealth solutions. The medical value of telehealth solutions can improve if doctors could interpret data collected from telehealth solutions; furthermore, if doctors could make diagnosis and provide treatment, adjust healthcare management plans based on such data, telehealth solutions then can be included in insurance packages, making them more accessible
Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption
People are living longer than ever due to advances in healthcare, and this
has prompted many healthcare providers to look towards remote patient care as a
means to meet the needs of the future. It is now a priority to enable people to
reside in their own homes rather than in overburdened facilities whenever
possible. The increasing maturity of IoT technologies and the falling costs of
connected sensors has made the deployment of remote healthcare at scale an
increasingly attractive prospect. In this work we demonstrate that we can
measure the consistency and regularity of the behaviour of a household using
sensor readings generated from interaction with the home environment. We show
that we can track changes in this behaviour regularity longitudinally and
detect changes that may be related to significant life events or trends that
may be medically significant. We achieve this using periodicity analysis on
water usage readings sampled from the main household water meter every 15
minutes for over 8 months. We utilise an IoT Application Enablement Platform in
conjunction with low cost LoRa-enabled sensors and a Low Power Wide Area
Network in order to validate a data collection methodology that could be
deployed at large scale in future. We envision the statistical methods
described here being applied to data streams from the homes of elderly and
at-risk groups, both as a means of early illness detection and for monitoring
the well-being of those with known illnesses.Comment: 2019 2nd International Conference on Sensors, Signal and Image
Processin
Internet of Things (IoT): Cybersecurity Risks in Healthcare
The rapid growth and investment in the Internet of Things (IoT) has significantly impacted how individuals and industries operate. The Internet of Things (IoT) refers to a network of physical, technology-embedded objects that communicate, detect, and interact with their external environment or internal state (Hung, 2017). According to Tankovska (2020), IoT devices are estimated to reach 21.5 billion units by 2025. This technological boom is leading various industrial sectors to notice a quick increase in cybersecurity risks and threats. One industrial sector has been particularly vulnerable to numerous cyber threats across the globe: healthcare. Oliver Noble (2020), a data encryption specialist at NordLocker, suggests that cybercriminals target healthcare institutions because they store an overwhelming amount of patient information that is private, personal, and unchangeable. Healthcare organizations have a difficult time securing their cybersecurity infrastructure and the reasons for this will be further discussed in this paper
Internet of Things (IoT) enabled assistive care services: Designing for value and trust
The rising elderly demographic, often with long-term conditions, represents a significant challenge globally in terms of planning for the efficient use of increasingly expensive and constrained health care resources. The internet of things (IoT) emerged as a disruptive and transformative new technology that could potentially stimulate development of new innovative assisted living health and care services. In this paper, we argue that as the human agency and relationship intrinsically associated with care get transferred to the material agency of smart technology, value and trust should be a vital consideration for designing such services.Drawing on interdisciplinary perspectives from the literature on services innovation, design science and trust in relation to healthcare technologies, we present a conceptual framework that articulates various levels of trust among the concerned stakeholders in the service ecosystem and suggests value-sensitive design considerations, anchored on the principles of trust, for future IoT-enabled assistive care services
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