18,074 research outputs found
Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation
This paper presents a novel autonomous quality metric to quantify the
rehabilitations progress of subjects with knee/hip operations. The presented
method supports digital analysis of human gait patterns using smartphones. The
algorithm related to the autonomous metric utilizes calibrated acceleration,
gyroscope and magnetometer signals from seven Inertial Measurement Unit
attached on the lower body in order to classify and generate the grading system
values. The developed Android application connects the seven Inertial
Measurement Units via Bluetooth and performs the data acquisition and
processing in real-time. In total nine features per acceleration direction and
lower body joint angle are calculated and extracted in real-time to achieve a
fast feedback to the user. We compare the classification accuracy and
quantification capabilities of Linear Discriminant Analysis, Principal
Component Analysis and Naive Bayes algorithms. The presented system is able to
classify patients and control subjects with an accuracy of up to 100\%. The
outcomes can be saved on the device or transmitted to treating physicians for
later control of the subject's improvements and the efficiency of physiotherapy
treatments in motor rehabilitation. The proposed autonomous quality metric
solution bears great potential to be used and deployed to support digital
healthcare and therapy.Comment: 5 Page
Smart hospital emergency system via mobile-based requesting services
In recent years, the UK’s emergency call and response has shown elements of great strain as of today. The strain on emergency call systems estimated by a 9 million calls (including both landline and mobile) made in 2014 alone. Coupled with an increasing population and cuts in government funding, this has resulted in lower percentages of emergency response vehicles at hand and longer response times. In this paper, we highlight the main challenges of emergency services and overview of previous solutions. In addition, we propose a new system call Smart Hospital Emergency System (SHES). The main aim of SHES is to save lives through improving communications between patient and emergency services. Utilising the latest of technologies and algorithms within SHES is aiming to increase emergency communication throughput, while reducing emergency call systems issues and making the process of emergency response more efficient. Utilising health data held within a personal smartphone, and internal tracked data (GPU, Accelerometer, Gyroscope etc.), SHES aims to process the mentioned data efficiently, and securely, through automatic communications with emergency services, ultimately reducing communication bottlenecks. Live video-streaming through real-time video communication protocols is also a focus of SHES to improve initial communications between emergency services and patients. A prototype of this system has been developed. The system has been evaluated by a preliminary usability, reliability, and communication performance study
Development of Wearable Systems for Ubiquitous Healthcare Service Provisioning
This paper reports on the development of a wearable system using wireless
biomedical sensors for ubiquitous healthcare service provisioning. The
prototype system is developed to address current healthcare challenges such as
increasing cost of services, inability to access diverse services, low quality
services and increasing population of elderly as experienced globally. The
biomedical sensors proactively collect physiological data of remote patients to
recommend diagnostic services. The prototype system is designed to monitor
oxygen saturation level (SpO2), Heart Rate (HR), activity and location of the
elderly. Physiological data collected are uploaded to a Health Server (HS) via
GPRS/Internet for analysis.Comment: 6 pages, 3 figures, APCBEE Procedia 7, 2013. arXiv admin note:
substantial text overlap with arXiv:1309.154
IoMT-Blockchain based Secured Remote Patient Monitoring Framework for Neuro-Stimulation Device
Biomedical Engineering's Internet of Medical Things (IoMT) is helping to
improve the accuracy, dependability, and productivity of electronic equipment
in the healthcare business. Real-time sensory data from patients may be
delivered and subsequently analyzed through rapid development of wearable IoMT
devices, such as neuro-stimulation devices with a range of functions. Data from
the Internet of Things is gathered, analyzed, and stored in a single location.
However, single-point failure, data manipulation, privacy difficulties, and
other challenges might arise as a result of centralization. Due to its
decentralized nature, blockchain (BC) can alleviate these issues. The viability
of establishing a non-invasive remote neurostimulation system employing
IoMT-based transcranial Direct Current Stimulation is investigated in this work
(tDCS). A hardware-based prototype tDCS device has been developed that can be
operated over the internet using an android application. Our suggested
framework addresses the problems of IoMTBC-based systems, meets the criteria of
real-time remote patient monitoring systems, and incorporates literature best
practices in the relevant fields.Comment: 8 Figures and 2 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
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