Paul Fergus

Managing Distributed Networked Appliances in Home Networks

Recent years have seen an exponential growth in the use of home networks, from the Internet-enabled PC to network-enabled home appliances. Ordinary and everyday appliances used in the home will increasingly become integral components of these networks. They are required to join, leave, and self-configure in accordance with their dynamic environment. New platforms and applications are needed to mediate interactions between devices to overcome the inherent problems, limitations, and costs of bespoke solutions. While combining our disparate devices to meet future needs is challenging, it will also allow for better exploitation of networked devices to provide obvious benefits to the consumer. A number of approaches and technologies that attempt to redress this issue exist; however, they are not without their own disadvantages. This paper discusses these technologies and introduces some of the novel approaches that have been proposed to address the issue of device autoconfiguration using peer-to-peer technologies. In this way, disparate devices are able to achieve self-management without user interaction. A case study is also presented to illuminate these approaches and demonstrate the applicability of our approach.

Prediction of preterm deliveries from EHG signals using machine learning.

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be

Whole body interaction

26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier.

Dynamic service composition in home appliance networks

In this workshop we explore the notation of whole body interaction. We bring together different disciplines to create a new research direction for study of this emerging form of interaction

Body Area Networks for Movement Analysis in Physiotherapy Treatments

The proliferation of networked appliances and the complex functions they provide make it ever harder for a specialist, let alone an ordinary home user, to configure them to provide a given service. The use of flexible middleware architectures, combined with application level services will allow for better exploitation of these features both for the benefit of performance and simplicity. For example, a TV, DVD player and radio all have output speakers and are capable of producing sound, however there is no common framework to harness this functionality. In this paper we address this issue and propose a home network architecture that interconnects home appliances and their associated services using descriptive ontologies to guide the composition process itself. In this network, home appliances are interconnected using a Service Integration Controller (SIC), which discovers and dynamically composes the services they provide and efficiently coordinates the communications between all services independent of the protocol being used. The prototype we implemented uses a home entertainment system as a case study and shows that this framework fulfils the requirements of the system design.

Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques

Recent technological advances in Micro Electro Mechanical Systems (MEMS) have enabled the design of lowcost, lightweight sensor nodes capable of sensing, processing and communicating different types of data. These tiny sensor nodes leverage the ideas found in Wireless Sensor Networks (WSNs) and this has lead to a large number of applications in the health sector. For example, telemonitoring is used to track, monitor and manage patient psychophysical data and help in the administration of drugs in hospitals. In this paper, we present a novel framework that exploits these ideas further, where body area WSNs and gaming have been combined to assist in physiotherapy treatments for patients with physical disabilities or ailments. The proposed framework has three main components, the body area WSN, the game, and the data acquisition manager. The body WSN is fixed to the patients body and data is collected and stored in real-time. This data in parallel is feed directly into the control services allowing gaming objects, i.e. virtual representations of patients, to control by physically moving his/her body parts. Whilst the patient plays the game, data is regularly collected from body sensor nodes. This allows real-time data from sensor nodes to be used by the game to adjust game levels according to the medical status of the patient. This allows treatments to be automatically adapted to maximise physiotherapy treatments and speed up recovery. In this paper, we present a working prototype that successfully demonstrates the applicability of our approach.

Remote physiotherapy treatments using wireless body sensor networks

The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders.

SCCIR: Smart Cities Critical Infrastructure Response Framework

Technology plays an important role in both primary and secondary healthcare. With widespread use of mobile devices and ubiquitous communications, new and novel platforms are emerging to administer care. Ordinary and everyday appliances used in the home are becoming integral components within these platforms and this could potentially revolutionise how health related information is monitored, accessed and used to administer better treatments. Despite the many challenges that exist, such platforms will allow for better exploitation of networked devices to provide benefits to patients with conditions, such as arthritis and back pain. Currently these conditions are treated through physiotherapy sessions in the community, which are often costly and difficult to resource. Physiotherapists alternate between patients. This means that assessments are sporadic and subjective. This paper aims to address these limitations using a system to implement body area and sensor networks within the home with data management functions for collecting and storing motion data. This data can be accessed via the home or remotely in one or more medical facilities. Using this data, quantitative assessments are performed and used to measure the patients progress. A case study is presented that successfully illustrates tour approach.

3D Java web-based games development and deployment

Critical infrastructures play important roles in ensuring the wellbeing of the populace. Protecting critical infrastructures and ensuring their continued operation will be an important part of future Smart City ecosystems. Minimising the destruction of failing critical infrastructure components or system components that are geographically close critical services is essential. Equally important are the system of systems relationships that a failing system has, as these could render a minor system critical under certain circumstances. Infrastructure failure is usually brought under control through system adaptation e.g. using sensor area networks to close valves or by emergency response e.g. extinguishing fires. Current response procedures rely on antiquated information sharing techniques and provide little or no opportunity to effect change within the failing infrastructures systems. There may also be minimal understanding of the important systems of systems role that is being provided by components of the failing system. This paper details our initial work on the Smart Cities Critical Infrastructure response framework. The goal of our framework is to provide a response strategy to first responders based on smart cities information flows. Where these information flows have been compromised we propose a robust infrastructure state preservation system as to provide an interface to a failing critical infrastructure.

A semantic framework for self-adaptive networked appliances

Currently, web-based online gaming applications are predominately utilising Adobe Flash or Java Applets as their core technologies. These games are often casual, two-dimensional games and do not utilise the specialist graphics hardware which has proliferated across modern PCs and Consoles. Multi-user online game play in these titles is often either non-existent or extremely limited. Computer games applications which grace the current generation of consoles and personal computers are designed to utilise the increasingly impressive hardware power at their disposal. However, these are commonly distributed using a physical medium or deployed through custom, proprietary networking mechanisms and rely upon platform-specific networking APIs to facilitate multi-user online game play. In order to unify the concepts of these disparate styles of gaming, in this paper we propose a novel integrated development environment called Homura and NetHomura. Homura is based on the Eclipse platform and extends the jME game engine, with new interfaces, content and libraries, thus, providing a software suite that integrates source editors, compilers, including spatial and positional editors to afford advanced graphical functionalities within the IDE. We also present two interconnected systems which are implemented using Java Web Start and JXTA P2P technologies, providing a platform-independent framework capable of deploying hardware accelerated cross-platform, cross-browser online-enabled Java games, as part of the NetHomura Project.