scholarly journals IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model

2021 ◽  
Vol 13 (8) ◽  
pp. 195
Author(s):  
Akash Gupta ◽  
Adnan Al-Anbuky
Keyword(s):  
Hip Fracture ◽  
Real Time ◽  
System Performance ◽  
Digital Health ◽  
Time Behavior ◽  
Functional Requirements ◽  
Rehabilitation Process ◽  
Rehabilitation Model ◽  
The Impact ◽  
Movement Monitoring

Hip fracture incidence is life-threatening and has an impact on the person’s physical functionality and their ability to live independently. Proper rehabilitation with a set program can play a significant role in recovering the person’s physical mobility, boosting their quality of life, reducing adverse clinical outcomes, and shortening hospital stays. The Internet of Things (IoT), with advancements in digital health, could be leveraged to enhance the backup intelligence used in the rehabilitation process and provide transparent coordination and information about movement during activities among relevant parties. This paper presents a post-operative hip fracture rehabilitation model that clarifies the involved rehabilitation process, its associated events, and the main physical movements of interest across all stages of care. To support this model, the paper proposes an IoT-enabled movement monitoring system architecture. The architecture reflects the key operational functionalities required to monitor patients in real time and throughout the rehabilitation process. The approach was tested incrementally on ten healthy subjects, particularly for factors relevant to the recognition and tracking of movements of interest. The analysis reflects the significance of personalization and the significance of a one-minute history of data in monitoring the real-time behavior. This paper also looks at the impact of edge computing at the gateway and a wearable sensor edge on system performance. The approach provides a solution for an architecture that balances system performance with remote monitoring functional requirements.

scholarly journals System Level Simulation of Microgrid Power Electronic Systems

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 644
Author(s):  
Michal Frivaldsky ◽  
Jan Morgos ◽  
Michal Prazenica ◽  
Kristian Takacs
Keyword(s):  
Smart Grid ◽  
Simulation Model ◽  
Real Time ◽  
Power Converter ◽  
System Level ◽  
Time Behavior ◽  
Operational Parameters ◽  
System Level Simulation ◽  
Time Operation ◽  
The Impact

In this paper, we describe a procedure for designing an accurate simulation model using a price-wised linear approach referred to as the power semiconductor converters of a DC microgrid concept. Initially, the selection of topologies of individual power stage blocs are identified. Due to the requirements for verifying the accuracy of the simulation model, physical samples of power converters are realized with a power ratio of 1:10. The focus was on optimization of operational parameters such as real-time behavior (variable waveforms within a time domain), efficiency, and the voltage/current ripples. The approach was compared to real-time operation and efficiency performance was evaluated showing the accuracy and suitability of the presented approach. The results show the potential for developing complex smart grid simulation models, with a high level of accuracy, and thus the possibility to investigate various operational scenarios and the impact of power converter characteristics on the performance of a smart gird. Two possible operational scenarios of the proposed smart grid concept are evaluated and demonstrate that an accurate hardware-in-the-loop (HIL) system can be designed.

scholarly journals Round table: Online anti-vaccination movements: the role of social media in public health communications

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
Keyword(s):  
Public Health ◽  
Social Networks ◽  
Social Media ◽  
Real Time ◽  
Hpv Vaccine ◽  
Digital Health ◽  
World Health ◽  
Swine Flu ◽  
Potential Impact ◽  
The Impact

Abstract Digital health has revolutionised healthcare, with implications for understanding public reaction to health emergencies and interventions. Social media provides a space where like-minded people can share interests and concerns in real-time, regardless of their location. This can be a force for good, as platforms like Twitter can spread correct information about outbreaks, for example in the 2009 swine flu pandemic. However, social media can also disseminate incorrect information or deliberately spread misinformation leading to adverse public health sentiment and outcomes. The current issues around trust in vaccines is the best-known example. Vaccine hesitancy, traditionally linked to issues of trust, misinformation and prior beliefs, has been increasingly fueled by influential groups on social media and the Internet. Ultimately, anti-vaccination movements have the potential to lead to outbreaks of vaccine-preventable diseases, especially if refusal is concentrated locally, creating vulnerable populations. For example, 2018-19 saw a large increase in incidence of measles in the US and Europe (where cases tripled from 2017), two regions where the disease was already or almost eliminated. In 2019, the World Health Organisation listed anti-vaccination movements as one of the top 10 threats to global public health. HPV vaccination is another example of the impact of anti-vaccination movements. As viral videos originating on YouTube spread across social networks, uptake has tumbled in a number of countries, with Japan, Denmark, Colombia and Ireland being badly hit. In Japan, the government came under sufficient pressure that they de-recommended HPV vaccine, seeing an 80% uptake rate fall below 1% in 2014. There have been reports of successful interventions by national governments. A recent campaign run by the HPV Alliance (a coalition of some 35 private companies, charities and public institutions) in Ireland has seen rates below 40% back up to a national average of 75%. A combination of hard-hitting personal testimonials, social media and traditional media promoted the HPV vaccine. Despite this, systematic engagement and supranational strategies are still in the early stages of being formulated. As misleading information spread through social media and digital networks has undesirable impact on attitudes to vaccination (and uptake rates), urgent actions are required. Analysis and visualisation techniques mining data streams from social media platforms, such as Twitter, Youtube enable real-time understanding of vaccine sentiments and information flows. Through identification of key influencers and flashpoints in articles about vaccination going viral, targeted public health responses could be developed. This roundtable discussion will showcase different ways in which media and social networks, accessible in real-time provide an opportunity for detecting a change in public confidence in vaccines, for identifying users and rumors and for assessing potential impact in order to know how to best respond. Key messages Social media has significantly enhanced our understanding of anti-vaccination movements and potential impact on public health attitudes and behaviors regarding vaccination. Innovative methods of analysing social media data, from digital health, data science and computer science, have an important role in developing health promotions to counter anti-vaccination movements.

scholarly journals A Generic Asset Model for Implementing Product Digital Twins in Smart Remanufacturing

2021 ◽  
Author(s):  
Mairi Kerin ◽  
Duc Truong Pham ◽  
Jun Huang ◽  
Jeremy Hadall
Keyword(s):  
Real Time ◽  
Manufacturing Industry ◽  
Integrated System ◽  
Embedded Sensors ◽  
Functional Requirements ◽  
Digital Twin ◽  
Digital Twins ◽  
The Impact ◽  
The Internet Of Things

Abstract A digital twin is a “live” virtual replica of a sensorised component, product, process, human, or system. It accurately copies the entity being modelled by capturing information in real time or near real time from the entity through embedded sensors and the Internet-of-Things. Many applications of digital twins in manufacturing industry have been investigated. This article focuses on the development of product digital twins to reduce the impact of quantity, quality, and demand uncertainties in remanufacturing. Starting from issues specific to remanufacturing, the article derives the functional requirements for a product digital twin for remanufacturing and proposes a UML model of a generic asset to be remanufactured. The model has been demonstrated in a case study which highlights the need to translate existing knowledge and data into an integrated system to realise a product digital twin, capable of supporting remanufacturing process planning.

scholarly journals COVID-19 – Protocol for Observational Studies Utilising Near Real-Time Electronic Australian General Practice Data to Promote Effective Care and Best-Practice Policy – A Design Thinking Approach

2020 ◽  
Author(s):  
Andrew Georgiou ◽  
Julie Li ◽  
Christopher Pearce ◽  
Adam McLeod ◽  
Nasir Wabe ◽  
...  
Keyword(s):  
General Practice ◽  
Real Time ◽  
Best Practice ◽  
Design Thinking ◽  
Digital Health ◽  
Health Networks ◽  
Primary Health ◽  
Effective Care ◽  
General Practices ◽  
The Impact

Abstract Background: Health systems around the world have been forced to make choices about how to prioritise care, manage infection control and maintain reserve capacity for future disease outbreaks. Primary health care has moved into the frontline as COVID-19 testing transitions from hospitals to multiple providers, where tracking testing behaviours can be fragmented and delayed. Pooled general practice data are a valuable resource which can be used to inform population and individual care decision-making. This project aims to utilise near real-time electronic general practice data to promote effective care and best-practice policy. Methods: The project will utilise a design thinking approach involving all collaborators (Primary Health Networks [PHNs], general practices, consumer groups, researchers, and digital health developers, pathology professionals) to enhance the development of meaningful and translational project outcomes. The project will be based on a series of observational studies utilising near real-time electronic general practice data from a secure and comprehensive digital health platform [POpulation Level Analysis and Reporting (POLAR) general practice data warehouse]. The study will be carried out over 1.5 years (July 2020 – December 2021) using data from over 350 general practices within three Victorian Primary Health Networks (PHNs) and Gippsland PHN, Eastern Melbourne PHN and South Eastern Melbourne PHN, supplemented by data from consenting general practices from two PHNs in New South Wales, Central and Eastern Sydney PHN and South Western Sydney PHN. Discussion: Developed using a design thinking approach, this project will deliver: 1) A near real-time geo-spatial reporting framework at community, state and nation-wide levels to identify emerging trends and monitor the impact of interventions/policy decisions. 2) Timely evidence about the impact of the COVID-19 pandemic related to its diagnosis, treatment and medications prescribed and its impact on patients. 3) A predictive geo-spatial analytics dashboard for timely, evidence-based decision-making at community, state and nation-wide levels. 4) An evidence-based suite of general practice outcome measures to monitor incidence, prevalence, recovery and mortality in response to the COVID-19 pandemic.

Transient Analysis of Downtimes and Bottleneck Dynamics in Serial Manufacturing Systems

2010 ◽  
Vol 132 (5) ◽  
Author(s):  
Qing Chang ◽  
Stephan Biller ◽  
Guoxian Xiao
Keyword(s):  
Real Time ◽  
System Performance ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Production Control ◽  
Production Performance ◽  
Impact Factors ◽  
Serial Transfer ◽  
Personnel Staffing ◽  
The Impact

In manufacturing industry, downtimes have been considered as major impact factors of production performance. However, the real impacts of downtime events and relationships between downtimes and system performance and bottlenecks are not as trivial as it appears. To improve the system performance in real-time and to properly allocate limited resources/efforts to different stations, it is necessary to quantify the impact of each station downtime event on the production throughput of the whole transfer line. A complete characterization of the impact requires a careful investigation of the transients of the line dynamics disturbed by the downtime event. We study in this paper the impact of downtime events on the performance of inhomogeneous serial transfer lines. Our mathematical analysis suggests that the impact of any isolated downtime event is only apparent in the relatively long run when the duration exceeds a certain threshold called opportunity window. We also study the bottleneck phenomenon and its relationship with downtimes and opportunity window. The results are applicable to real-time production control, opportunistic maintenance scheduling, personnel staffing, and downtime cost estimation.

The Calculation of TTR in PROFIBUS System

2012 ◽  
Vol 482-484 ◽  
pp. 2183-2187 ◽  
Author(s):  
Li Ping Zhen ◽  
Shao Wei Si ◽  
Huan Qing Xie
Keyword(s):  
Real Time ◽  
Data Exchange ◽  
Selection Method ◽  
Time Behavior ◽  
The Real ◽  
Transmission Errors ◽  
Random Behavior ◽  
Token Passing ◽  
Master Station ◽  
The Impact

In PROFIBUS system, we analyzed the time behavior of data exchange and token-passing, and give the TTR selection method, when each master station holding enough token time. And then we discussed the random characteristics of networks and FDL, give the formula of random behavior to calculate time, and get the TTR and the revised value of TTR in PROFIBUS system which has FDL and MS1 communication. Finally, further discussed the case of transmission errors, analyzed the impact of transmission errors to TTR and the real-time of system, and give the TTR and the revised value in this situation.

scholarly journals COVID-19 – Protocol for Observational Studies Utilising Near Real-Time Electronic Australian General Practice Data to Promote Effective Care and Best-Practice Policy – A Design Thinking Approach

2021 ◽  
Author(s):  
Andrew Georgiou ◽  
Julie Li ◽  
Christopher Pearce ◽  
Adam McLeod ◽  
Nasir Wabe ◽  
...  
Keyword(s):  
General Practice ◽  
Real Time ◽  
Best Practice ◽  
Design Thinking ◽  
Digital Health ◽  
Health Networks ◽  
Primary Health ◽  
Effective Care ◽  
General Practices ◽  
The Impact

Abstract Background: Health systems around the world have been forced to make choices about how to prioritise care, manage infection control and maintain reserve capacity for future disease outbreaks. Primary health care has moved into the frontline as COVID-19 testing transitions from hospitals to multiple providers, where tracking testing behaviours can be fragmented and delayed. Pooled general practice data are a valuable resource which can be used to inform population and individual care decision-making. This project aims to utilise near real-time electronic general practice data to promote effective care and best-practice policy. Methods: The project will utilise a design thinking approach involving all collaborators (Primary Health Networks [PHNs], general practices, consumer groups, researchers, and digital health developers, pathology professionals) to enhance the development of meaningful and translational project outcomes. The project will be based on a series of observational studies utilising near real-time electronic general practice data from a secure and comprehensive digital health platform [POpulation Level Analysis and Reporting (POLAR) general practice data warehouse]. The study will be carried out over 1.5 years (July 2020 – December 2021) using data from over 350 general practices within three Victorian Primary Health Networks (PHNs) and Gippsland PHN, Eastern Melbourne PHN and South Eastern Melbourne PHN, supplemented by data from consenting general practices from two PHNs in New South Wales, Central and Eastern Sydney PHN and South Western Sydney PHN. Discussion: Developed using a design thinking approach, this project will deliver: 1) A near real-time geo-spatial reporting framework at community, state and nation-wide levels to identify emerging trends and monitor the impact of interventions/policy decisions. 2) Timely evidence about the impact of the COVID-19 pandemic related to its diagnosis, treatment and medications prescribed and its impact on patients. 3) A predictive geo-spatial analytics dashboard for timely, evidence-based decision-making at community, state and nation-wide levels. 4) An evidence-based suite of general practice outcome measures to monitor incidence, prevalence, recovery and mortality in response to the COVID-19 pandemic.

scholarly journals COVID-19: protocol for observational studies utilizing near real-time electronic Australian general practice data to promote effective care and best-practice policy—a design thinking approach

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Andrew Georgiou ◽  
Julie Li ◽  
Christopher Pearce ◽  
Adam McLeod ◽  
Nasir Wabe ◽  
...  
Keyword(s):  
General Practice ◽  
Real Time ◽  
Observational Studies ◽  
Best Practice ◽  
Design Thinking ◽  
Digital Health ◽  
Clinical Population ◽  
Effective Care ◽  
General Practices ◽  
The Impact

Abstract Background Health systems around the world have been forced to make choices about how to prioritize care, manage infection control and maintain reserve capacity for future disease outbreaks. Primary healthcare has moved into the front line as COVID-19 testing transitions from hospitals to multiple providers, where tracking testing behaviours can be fragmented and delayed. Pooled general practice data are a valuable resource which can be used to inform population and individual care decision-making. This project aims to examine the feasibility of using near real-time electronic general practice data to promote effective care and best-practice policy. Methods The project will utilize a design thinking approach involving all collaborators (primary health networks [PHNs], general practices, consumer groups, researchers, and digital health developers, pathology professionals) to enhance the development of meaningful and translational project outcomes. The project will be based on a series of observational studies utilizing near real-time electronic general practice data from a secure and comprehensive digital health platform [POpulation Level Analysis and Reporting (POLAR) general practice data warehouse]. The study will be carried out over 1.5 years (July 2020–December 2021) using data from over 450 general practices within three Victorian PHNs and Gippsland PHN, Eastern Melbourne PHN and South Eastern Melbourne PHN, supplemented by data from consenting general practices from two PHNs in New South Wales, Central and Eastern Sydney PHN and South Western Sydney PHN. Discussion The project will be developed using a design thinking approach, leading to the building of a meaningful near real-time COVID-19 geospatial reporting framework and dashboard for decision-makers at community, state and nationwide levels, to identify and monitor emerging trends and the impact of interventions/policy decisions. This will integrate timely evidence about the impact of the COVID-19 pandemic related to its diagnosis and treatment, and its impact across clinical, population and general practice levels.

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