Farhaan Mirza

Mobile technologies and the holistic management of chronic diseases

Ageing populations and unhealthy lifestyles have led to some chronic conditions such as diabetes and heart disease reaching epidemic proportions in many developed nations. This paper explores the potential of mobile technologies to improve this situation. The pervasive nature of these technologies can contribute holistically across the whole spectrum of chronic care ranging from public information access and awareness, through monitoring and treatment of chronic disease, to support for patient carers. A related study to determine the perceptions of healthcare providers to m-health confirmed the view that attitudes were likely to be more important barriers to progress than technology. A key finding concerned the importance of seamless and integrated m-health processes across the spectrum of chronic disease management.

A Systematic Review of Wearable Patient Monitoring Systems --- Current Challenges and Opportunities for Clinical Adoption

The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and ‘silo’ solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.

An Agent-Based Model of Access Uptake on a High-Speed Broadband Platform

We model the access uptake on a newly built high-speed fibre-to-the-home (FTTH) broadband network using a computational Agent Based Model (ABM). Two cases illustrate the model analysed in this paper: the Ultra-Fast Broadband (UFB) Network in New Zealand (NZ) and the National Broadband Network (NBN) in Australia. Common learnings of both projects are used in our model to describe and analyse the uptake of fibre connections to households and businesses. By design network operation is decoupled from service provision and the platform is open-access, meaning any provider can operate end-user services. In our model a high-speed broadband network is regarded as a two-sided platform that accommodates both end-users and service providers, creating the conditions for the two sides to exploit mutual network effects. Results show that the greater the number of users (end-users or providers) on one side, the more the number of users (provider or end-users) on the opposite side grows. Providing free connections and raising consumer awareness is a means for driving consumer uptake. Scenario based analysis allows us to investigate the magnitude of network effects’ on the fibre connection uptake.

A review on IoT healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback

Ageing populations and the increase in chronic diseases all over the world demand efficient healthcare solutions for maintaining well-being of people. One strategy that has drawn significant research attention is a focus on remote health monitoring systems based on Internet of Things (IoT) technology. This concept can help decrease pressure on hospital systems and healthcare providers, reduce healthcare costs, and improve homecare especially for patients with chronic diseases and the elderly. This paper explores the use of IoT-based applications in medical field and proposes an IoT Tiered Architecture (IoTTA) towards an approach for transforming sensor data into real-time clinical feedback. This approach considers a range of aspects including sensing, sending, processing, storing, and mining and learning. Using this approach will help to develop useful and effective solutions for pursuing systems development in IoT healthcare applications. The result of the review found that the growth of IoT applications for healthcare is in areas of self-care, data mining, and machine learning.

Sustainable, Holistic, Adaptable, Real-Time, and Precise (SHARP) Approach Towards Developing Health and Wellness Systems

As populations age and chronic diseases become more prevalent, new strategies are required to help people live well. Traditional models of episodic health care will not be sufficient to meet changing health care needs and the reorientation of services towards maintaining function as opposed to treating illness. One strategy to meet these challenges is an increased focus on self-care via use of broader social networks and seamless integration of applications with lifestyle activities, particularly for people with chronic diseases including diabetes, cardiovascular disease, and respiratory conditions. There has also been a rapid increase in a range of technologies for connecting different components of the health system and delivering services through smartphones and connected devices. Our proposal is to pursue systems development in healthcare in a way that considers a range of aspects known as SHARP: Sustainable, Holistic, Adaptive, Real-time and Precise. This approach will provide solutions that will be useful and effective for managing the long-term well-being of individuals.

A refinement of an iterative orthogonal projection method

Abstract The Kaczmarz algorithm is an iterative orthogonal projection method for solving linear systems of equations. As compared to direct methods such as Gaussian elimination or sparse QR-factorization, this algorithm is efficient for problems with sparse matrices, as they appear in constraint-based User Interface (UI) layout specifications. We present a variant of the Kaczmarz method for solving non-square systems that can be applied to Graphical User Interface (GUI) layout problems. In its original form the Kaczmarz algorithm cannot handle soft constraints. Therefore, we propose two algorithms for handling specifications containing soft constraints using prioritized irreducible infeasible subsystem (IIS) detection and prioritized grouping constraints. If we use Kaczmarz during resizing of a window in a GUI then the system can also be under-determined. In this case, space is not distributed in an aesthetically pleasing way. To distribute the space according to the preferred size of the layout, we introduce the least squares Kaczmarz method to get the desired results. The performance and convergence of the proposed algorithms are evaluated empirically using randomly generated UI layout specifications of various sizes. The results show that these methods outperform Matlab’s LINPROG, a well-known efficient linear programming solver.

Is it possible to cure Internet addiction with the Internet

Significant technological advancements over the last two decades have led to enhanced accessibility to computing devices and the Internet. Our society is experiencing an ever-growing integration of the Internet into everyday lives, and this has transformed the way we obtain and exchange information, communicate and interact with one another as well as conduct business. However, the term ‘Internet addiction’ (IA) has emerged from problematic and excessive Internet usage which leads to the development of addictive cyber-behaviours, causing health and social problems. The most commonly used intervention treatments such as motivational interviewing, cognitive-behavioural therapy, and retreat or inpatient care mix a variety of psychotherapy theories to treat such addictive behaviour and try to address underlying psychosocial issues that are often coexistent with IA, but the efficacy of these approaches is not yet proved. The aim of this paper is to address the question of whether it is possible to cure IA with the Internet. After detailing the current state-of-the-art including various IA definitions, risk factors, assessment methods and IA treatments, we outline the main research challenges that need to be solved. Moreover, we propose an Internet-based IA Recovery Framework (IARF) which uses AI to closely observe, visualize and analyse patient’s Internet usage behaviour for possible staged intervention. The proposal to use smart Internet-based systems to control IA can be expected to be controversial. This paper is intended to stimulate further discussion and research in IA recovery through Internet-based frameworks.

Falls management framework for supporting an independent lifestyle for older adults: a systematic review

Falls are one of the common health and well-being issues among the older adults. Internet of things (IoT)-based health monitoring systems have been developed over the past two decades for improving healthcare services for older adults to support an independent lifestyle. This research systematically reviews technological applications related to falls detection and falls management. The systematic review was conducted in accordance to the preferred reporting items for systematic reviews and meta-analysis statement (PRISMA). Twenty-four studies out of 806 articles published between 2015 and 2017 were identified and included in this review. Selected studies were related to pre-fall and post-fall applications using motion sensors (10; 41.67%), environment sensors (10; 41.67%) and few studies used the combination of these types of sensors (4; 16.67%). As an outcome of this review, we postulated a falls management framework (FMF). FMF considered pre- and post-fall strategies to support older adults live independently. A part of this approach involved active analysis of sensor data with the aim of helping the older adults manage their risk of fall and stay safe in their home. FMF aimed to serve the researchers, developers, clinicians and policy makers with pre- and post-falls management strategies to enhance the older adults’ independent living and well-being.

Aspect of Blame in Tweets: A Deep Recurrent Neural Network Approach

Twitter as an information dissemination tool has proved to be instrumental in generating user curated content in short spans of time. Tweeting usually occurs when reacting to events, speeches, about a service or product. This in some cases comes with its fair share of blame on varied aspects in reference to say an event. Our work in progress details how we plan to collect the informal texts, clean them and extract features for blame detection. We are interested in augmenting Recurrent Neural Networks (RNN) with self-developed association rules in getting the most out of the data for training and evaluation. We aim to test the performance of our approach using human-induced terror-related tweets corpus. It is possible tailoring the model to fit natural disaster scenarios.

Detecting Falls Using a Wearable Accelerometer Motion Sensor

This research aims to early detect falls based on the rapid acceleration changes using the threshold based approach, using a single accelerometer. We propose the Acceleration Change-based Falls Detection Algorithm (ACFDA). The ACFDA observes and detects the rapid change of acceleration in vertical axis and the average value of signal magnitude vector of acceleration to differentiate falls from other activities of daily life (ADL). Initial results demonstrates that our algorithm achieved 100% of sensitivity, 95.65% of specificity and 96.35% of accuracy when tested with a total of 44 intentional falls and 230 ADLs in 32 datasets. Future work will focus on developing other strategies to reduce false alarms for improving both specificity and accuracy of the algorithm while still maintaining 100% of sensitivity.