259 research outputs found
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
A Wearable Fall Detection System based on LoRa LPWAN Technology
Several technological solutions now available in the
market offer the possibility of increasing the independent life
of people who by age or pathologies otherwise need assistance.
In particular, internet-connected wearable solutions are of considerable interest, as they allow continuous monitoring of the
user. However, their use poses different challenges, from the real
usability of a device that must still be worn to the performance
achievable in terms of radio connectivity and battery life. The
acceptability of a technology solution, by a user who would still
benefit from its use, is in fact often conditioned by practical
problems that impact the personâs normal lifestyle. The technological choices adopted in fact strongly determine the success
of the proposed solution, as they may imply limitations both
to the person who uses it and to the achievable performance.
In this document, targeting the case of a fall detection sensor
based on a pair of sensorized shoes, the effectiveness of a real
implementation of an Internet of Things technology is examined.
It is shown how alarming events, generated in a metropolitan
context, are effectively sent to a supervision system through
Low Power Wide Area Network technology without the need
for a portable gateway. The experimental results demonstrate
the effectiveness of the chosen technology, which allows the user
to take advantage of the support of a wearable sensor without
being forced to substantially change his lifestyle
Risk Assessment for Alzheimer Patients, using GPS and Accelerometers with a Machine Learning Approach
Alzheimer is a pathology with an increasing incidence as people age. The epidemiology
of the Alzheimerâs disease crosses every country at later stages in life and, for that motive,
has become a major concern as expectancy of life is raising in developed world.
The problem, as the disease progresses, becomes, the continuity of the personâs life as
normal as possible and the insurance of safety and security for that person. It is known
that Alzheimer patients tend to forget about important things such as their identity and
location and for that it is important to provide that they become aware of such relevant
information for them and for those who could provide them support. It is also known
that people with Alzheimer tend to wander and, when that happens, they can get lost and
become exposed to danger.
The aim of this work is to assess if the person is getting away from usual paths and to
monitor if the person falls which becomes riskier while wandering out of usual paths. The
usage of GPS makes it possible to keep track of routes and, with the detection of possible
deviations, it becomes possible to act accordingly, either issuing warnings for that person
and later to carers and family. On the other hand, using machine learning to evaluate
usual movements, it is possible to determine if a person falls or endures an excessively
quiet position. With those strategies working together it is aimed to ensure safety of a
person and request for assistance when high risk is assessed by the technological setup.
To address these cases, the proposded setup will be based on a smartphone, together with
a smartwatch, both carried by that person, as such devices already provide some needed
sensors and GPS receiver while providing processing capabilities
Adaptive Geolocation of IoT devices for Active and Assisted Living
Recent developments in IoT devices and communication systems, have brought to light new
solutions capable of offering advanced sensing of the surrounding environments. On the other
hand, during the last decades, the average life expectancy has increased, which translates into a
considerable rise in the number of elderly people. Consequently, in view of all these factors, there
is currently a constant demand for solutions to support an Active and Assisted Living (AAL) of
such people.
The presented thesis intends to propose a solution to help to know the location of IoT devices
that may be assisting people. The proposed solution should take into consideration the risk factors
of the target group at each moment, as well as the technical constraints of the device, such as its
available power energy and means of communications. Thus, ultimately, a profile-based decision
should autonomously be made by the device or its integrated system, in order to ensure the usage
of the best geolocation technology for each situation.Desenvolvimentos recentes em dispositivos IoT e em sistemas de comunicação, trouxeram
consigo novas soluçÔes capazes de oferecer uma deteção avançada dos ambientes circundantes.
Por outro lado, no decorrer das Ășltimas dĂ©cadas, a esperança mĂ©dia de vida aumentou, o que se
traduz tambĂ©m num considerĂĄvel aumento do nĂșmero de pessoas idosas. Por conseguinte, perante
o conjunto destes factores, existe actualmente uma procura constante de soluçÔes de suporte a uma
Active and Assisted Living desse grupo de pessoas.
A presente tese tenciona propor uma solução que ajude a conhecer a localização dos dispositivos
IoT que possam estar a ajudar pessoas. A solução proposta deve ter em consideração os fatores
de risco do grupo-alvo em cada momento e também as restriçÔes técnicas do dispositivo, como
a energia disponĂvel e os meios de comunicação. Deste modo, em Ășltima instĂąncia, uma decisĂŁo
baseada num perfil deve ser tomada autonomamente pelo dispositivo ou pelo seu sistema, para
garantir a utilização da tecnologia de geolocalização mais adequada em cada situação
Healthcare in the Smart Home: A Study of Past, Present and Future
Open Access journalUbiquitous or Pervasive Computing is an increasingly used term throughout the technology industry and is beginning to enter the consumer electronics space in its most recent form under the umbrella term: âInternet of Thingsâ. One area of focus is in augmenting the home with intelligent, networked sensors and computers to create a Smart Home which opens a host of possibilities for the role of tomorrowâs dwelling. As the worldâs population continues to live longer and consequently experience more medical-related ailments, at the same time institutional healthcare is struggling to cope, the role of the Smart Home becomes paramount to monitoring a dwellerâs health and providing any necessary intervention. This study looks at the history of Smart Home Healthcare, current research areas, and potential areas of future investigation. Unique categorisations are presented in Activities of Daily Living (ADL) and Personal Sensors, and a thorough look at the application of Smart Home Healthcare is presented. Technology can augment traditional methods of healthcare delivery and in some cases completely replace it. Costs can be reduced and medical adherence can be increased, all of which contribute to a more sustainable and effective model of care
Analysis of Android Device-Based Solutions for Fall Detection
Falls are a major cause of health and psychological problems as well as
hospitalization costs among older adults. Thus, the investigation on automatic Fall
Detection Systems (FDSs) has received special attention from the research community
during the last decade. In this area, the widespread popularity, decreasing price, computing
capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based
devices (especially smartphones) have fostered the adoption of this technology to deploy
wearable and inexpensive architectures for fall detection. This paper presents a critical and
thorough analysis of those existing fall detection systems that are based on Android devices.
The review systematically classifies and compares the proposals of the literature taking into
account different criteria such as the system architecture, the employed sensors, the detection
algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the
evaluation methods that are employed to assess the effectiveness of the detection process.
The review reveals the complete lack of a reference framework to validate and compare the
proposals. In addition, the study also shows that most research works do not evaluate the
actual applicability of the Android devices (with limited battery and computing resources) to
fall detection solutions.Ministerio de EconomĂa y Competitividad TEC2013-42711-
Quality Function Deployment Method and Its Application on Wearable Technology Product Development
The purpose of this thesis was to investigate consumers and product developerâs expectations of wearable technology products in the context of the Quality Function Deployment (QFD) framework. The specific objectives were to: 1) Explore the quality features that consumers consider most important when purchasing wearable technology product. 2) Explore the technical features product developers consider most important in the development of wearable technology. 3) Identify the technical features that wearable technology product developers need to focus on to meet the customer requirements.
The Qualtrix online survey system was used to collect demographic, quantitative and essay length written responses from participants. Three hundred seventy eight men and women who were either consumers of wearable technology or professionals involved in its design and manufacture participated in this research. Data were analyzed with Statistical Analysis System (SAS) Enterprise 6.1. Open ended questions were analyzed for content and coded on an Excel spreadsheet using the thematic method.
Results indicate consumers considered the most important feature of wearable technology to be Product Safety whereas professionals involved in its design and manufacture regarded Materials Selection as the most important aspect.
This study provides valuable information for both industry and academia and identifies areas that must be addressed by manufacturers of wearable technology to meet consumerâs demand for product features
Development of a Wireless Mobile Computing Platform for Fall Risk Prediction
Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research
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
Sensor-Based Locomotion Data Mining for Supporting the Diagnosis of Neurodegenerative Disorders: A Survey
Locomotion characteristics and movement patterns are reliable indicators of neurodegenerative diseases (NDDs). This survey provides a systematic literature review of locomotion data mining systems for supporting NDD diagnosis. We discuss techniques for discovering low-level locomotion indicators, sensor data acquisition and processing methods, and NDD detection algorithms. The survey presents a comprehensive discussion on the main challenges for this active area, including the addressed diseases, locomotion data types, duration of monitoring, employed algorithms, and experimental validation strategies. We also identify prominent open challenges and research directions regarding ethics and privacy issues, technological and usability aspects, and availability of public benchmarks
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