17,362 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
A Mobile Healthcare Solution for Ambient Assisted Living Environments
Elderly people need regular healthcare services and, several times,
are dependent of physiciansâ personal attendance. This dependence raises
several issues to elders, such as, the need to travel and mobility support.
Ambient Assisted Living (AAL) and Mobile Health (m-Health) services and
applications offer good healthcare solutions that can be used both on
indoor and in mobility environments. This dissertation presents an ambient
assisted living (AAL) solution for mobile environments.
It includes elderly biofeedback monitoring using body sensors for data
collection offering support for remote monitoring. The used sensors are
attached to the human body (such as the electrocardiogram, blood
pressure, and temperature). They collect data providing comfort, mobility,
and guaranteeing efficiency and data confidentiality. Periodic collection of
patientsâ data is important to gather more accurate measurements and to
avoid common risky situations, like a physical fall may be considered
something natural in life span and it is more dangerous for senior people.
One fall can out a life in extreme cases or cause fractures, injuries, but
when it is early detected through an accelerometer, for example, it can
avoid a tragic outcome.
The presented proposal monitors elderly people, storing collected
data in a personal computer, tablet, or smartphone through Bluetooth. This
application allows an analysis of possible health condition warnings based
on the input of supporting charts, and real-time bio-signals monitoring and
is able to warn users and the caretakers. These mobile devices are also used to collect data, which allow data
storage and its possible consultation in the future. The proposed system is
evaluated, demonstrated and validated through a prototype and it is ready
for use. The watch Texas ez430-Chronos, which is capable to store
information for later analysis and the sensors Shimmer who allow the
creation of a personalized application that it is capable of measuring biosignals
of the patient in real time is described throughout this dissertation
Is the timed-up and go test feasible in mobile devices? A systematic review
The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinsonâs disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio
CoachAI: A Conversational Agent Assisted Health Coaching Platform
Poor lifestyle represents a health risk factor and is the leading cause of
morbidity and chronic conditions. The impact of poor lifestyle can be
significantly altered by individual behavior change. Although the current shift
in healthcare towards a long lasting modifiable behavior, however, with
increasing caregiver workload and individuals' continuous needs of care, there
is a need to ease caregiver's work while ensuring continuous interaction with
users. This paper describes the design and validation of CoachAI, a
conversational agent assisted health coaching system to support health
intervention delivery to individuals and groups. CoachAI instantiates a text
based healthcare chatbot system that bridges the remote human coach and the
users. This research provides three main contributions to the preventive
healthcare and healthy lifestyle promotion: (1) it presents the conversational
agent to aid the caregiver; (2) it aims to decrease caregiver's workload and
enhance care given to users, by handling (automating) repetitive caregiver
tasks; and (3) it presents a domain independent mobile health conversational
agent for health intervention delivery. We will discuss our approach and
analyze the results of a one month validation study on physical activity,
healthy diet and stress management
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