Automatically detecting pain in video through facial action units

In a clinical setting, pain is reported either through patient self-report or via an observer. Such measures are problematic as they are: 1) subjective, and 2) give no specific timing information. Coding pain as a series of facial action units (AUs) can avoid these issues as it can be used to gain an objective measure of pain on a frame-by-frame basis. Using video data from patients with shoulder injuries, in this paper, we describe an active appearance model (AAM)-based system that can automatically detect the frames in video in which a patient is in pain. This pain data set highlights the many challenges associated with spontaneous emotion detection, particularly that of expression and head movement due to the patient's reaction to pain. In this paper, we show that the AAM can deal with these movements and can achieve significant improvements in both the AU and pain detection performance compared to the current-state-of-the-art approaches which utilize similarity-normalized appearance features only.

Practical introduction to development of sage applications

Throughout this workshop session we have looked at various configurations of Sage as well as using the Sage UI to run Sage applications (e.g. the image viewer). More advanced usage of Sage has been demonstrated using a Sage compatible version of Paraview highlighting the potential of parallel rendering. The aim of this tutorial session is to give a practical introduction to developing visual content for a tiled display using the Sage libraries. After completing this tutorial you should have the basic tools required to develop your own custom Sage applications. This tutorial is designed for software developers and intermediate programming knowledge is assumed, along with some introductory OpenGL . You will be required to write small portions of C/C++ code to complete this worksheet. However if you do not feel comfortable writing code (or have never written in C or C++), we will be on hand throughout this session so feel free to ask for some help. We have a number of machines in this lab running a VNC client to a virtual machine running Fedora 12. You should all be able to log in with the username “escience”, and password “escience10”. Some of the commands in this worksheet require you to run them as the root user, so note the password as you may need to use it a few times. If you need to access the Internet, then use the username “qpsf01”, password “escience10”

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

A socially responsible model of micro-credit delivery to support sustainable community development in emerging economies

Micro-finance, which includes micro-credit as one of its core services, has become an important component of a range of business models – from those that operate on a strictly economic basis to those that come from a philanthropic base, through Non Government Organisations (NGOs). Its success is often measured by the number of loans issued, their size, and the repayment rates. This paper has a dual purpose: to identify whether the models currently used to deliver micro-credit services to the poor are socially responsible and to suggest a new model of delivery that addresses some of the social responsibility issues, while supporting community development. The proposed model is currently being implemented in Beira, the second largest city in Mozambique. Mozambique exhibits many of the characteristics found in other African countries so the model, if successful, may have implications for other poor African nations as well as other developing economies.

Development and pilot test of a peer-support based Cardiac-Diabetes Self-Management program : a study protocol

Background: People with cardiac disease and type 2 diabetes have higher hospital readmission rates (22%)compared to those without diabetes (6%). Self-management is an effective approach to achieve better health outcomes; however there is a lack of specifically designed programs for patients with these dual conditions. This project aims to extend the development and pilot test of a Cardiac-Diabetes Self-Management Program incorporating user-friendly technologies and the preparation of lay personnel to provide follow-up support. Methods/Design: A randomised controlled trial will be used to explore the feasibility and acceptability of the Cardiac-Diabetes Self-Management Program incorporating DVD case studies and trained peers to provide follow-up support by telephone and text-messaging. A total of 30 cardiac patients with type 2 diabetes will be randomised, either to the usual care group, or to the intervention group. Participants in the intervention group will received the Cardiac-Diabetes Self-Management Program in addition to their usual care. The intervention consists of three faceto- face sessions as well as telephone and text-messaging follow up. The face-to-face sessions will be provided by a trained Research Nurse, commencing in the Coronary Care Unit, and continuing after discharge by trained peers. Peers will follow up patients for up to one month after discharge using text messages and telephone support. Data collection will be conducted at baseline (Time 1) and at one month (Time 2). The primary outcomes include self-efficacy, self-care behaviour and knowledge, measured by well established reliable tools. Discussion: This paper presents the study protocol of a randomised controlled trial to pilot evaluates a Cardiac- Diabetes Self-Management program, and the feasibility of incorporating peers in the follow-ups. Results of this study will provide directions for using such mode in delivering a self-management program for patients with both cardiac condition and diabetes. Furthermore, it will provide valuable information of refinement of the intervention program.

Workflow support for scheduling in surgical care processes

Real-world business processes rely on the availability of scarce, shared resources, both human and non-human. Current workflow management systems support allocation of individual human resources to tasks but lack support for the full range of resource types used in practice, and the inevitable constraints on their availability and applicability. Based on past experience with resource-intensive workflow applications, we derive generic requirements for a workflow system which can use its knowledge of resource capabilities and availability to help create feasible task schedules. We then define the necessary architecture for implementing such a system and demonstrate its practicality through a proof-of-concept implementation. This work is presented in the context of a real-life surgical care process observed in a number of German hospitals.

Online we are all able bodied : Online psychological sense of community and social support found through membership of disability-specific websites promotes well-being for people living with a physical disability

People with a physical disability are a population who for a number of reasons may be vulnerable to social isolation. Research into Internet-based support sites has found that social support and an online sense of community can be developed through computer mediated communication channels. This study aims to gain an understanding of the benefits that membership of disability-specific online communities may have for people with a physical disability. An online survey was administered to a sample of users of such sites (N = 160). Results indicated that users did receive moral support and personal advice through participating in such online communities. Further, results indicated that online social support and feeling a sense of community online were positively associated with participants' well-being in the areas of personal relations and personal growth.

Cardiac health diagnosis using higher order spectra and support vector machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

Available evidence does not support routine administration of antipyretics to reduce duration of fever or illness

Commentary on : Carey JV. Literature review : should antipyretic therapies routinely be administered to patients with [corrected] fever? J Clin Nurs 2010;19:2377–93.

Elicitator : a user-friendly, interactive tool to support scenario-based elicitation of expert knowledge

Expert elicitation is the process of determining what expert knowledge is relevant to support a quantitative analysis and then eliciting this information in a form that supports analysis or decision-making. The credibility of the overall analysis, therefore, relies on the credibility of the elicited knowledge. This, in turn, is determined by the rigor of the design and execution of the elicitation methodology, as well as by its clear communication to ensure transparency and repeatability. It is difficult to establish rigor when the elicitation methods are not documented, as often occurs in ecological research. In this chapter, we describe software that can be combined with a well-structured elicitation process to improve the rigor of expert elicitation and documentation of the results

Building support for learning within a Doctor of Education programme

Professional doctorates were introduced in the 1990s for practitioners to research ‘real-world’ problems relevant to their respective workplace communities and contexts. An array of difficulties faces professional doctoral students as they transition from professionals to practitioner researchers. This study sought to understand the learning journey of a cohort of students at an Australian university and to assess whether the cohort approach provided the necessary support for students to reach their scholarly destinations. Throughout the first 18 months of the programme, focus group interviews and surveys were conducted to gauge students’ experiences and to evaluate developments for support within the programme. Utilising a socio-cultural perspective helped identify and explain the importance of shared practice in fostering learning, the development of academic and researcher identities, and the role of communities of practice. Challenges of managing time and overcoming the professional and academe divide were facilitated by the evolving developments of the programme.