Huiru Zheng

Position-sensing technologies for movement analysis in stroke rehabilitation

Research has focused on improvement of the quality of life of stroke patients. Gait detection, kinematics and kinetics analysis, home-based rehabilitation and telerehabilitation are the areas where there has been increasing research interest. The paper reviews position-sensing technologies and their application for human movement tracking and stroke rehabilitation. The review suggests that it is feasible to build a home-based telerehabilitation system for sensing and tracking the motion of stroke patients.

Human Activity Detection in Smart Home Environment with Self-Adaptive Neural Networks

One of key components in the development of smart home technology is the detection and recognition of activities of daily life. Based on a self-adaptive neural network called growing self-organizing maps (GSOM), this paper presents a new computational approach to cluster analysis of human activities of daily living within smart home environment. It was tested on a dataset collected from a set of simple state-change sensors installed on a one-bedroom apartment during a period of about two weeks. The results obtained indicate that, due to its advanced evolving, self- adaptive properties, the GSOM exhibits several appealing features in the analysis of useful patterns encoded in daily activity data. The approaches described in this paper contribute to the development of a user-friendly and interactive data-mining platform for the analysis of human activities within smart home environment through the improvement of pattern discovery, visualization and interpretation.

A review of the role of assistive technology for people with dementia in the hours of darkness

Assistive Technology (AT) has been utilized to support people with dementia (PwD) and their carers in the home. Such support can extend the time that PwD can remain safely at home and reduce the burden on the tertiary healthcare sector. Technology can assist people in the hours of darkness as well as during the day. The objective of this literature review is to evaluate reported healthcare technologies appropriate to night time care. This paper summarises and categorises the current evidence base. In all, 131 abstracts were returned from a database search, yielding fifty four relevant papers which were considered in detail. While night-time specific studies identified very few papers (4 papers, 7%), most of the more general AT findings could be adopted to benefit night-time assistance. Studies have used technology for prompting and reminding as loss of time and forgetfulness are major problems; for monitoring daily activities in a sensor enriched environment and utilised location aware technologies to provide information to enhance safety. Technology also supports a range of therapies to alleviate symptoms. Therapies include the delivery of music and familial pictures for reminiscing, the use of light therapy to enhance wellbeing and the provision of mental tasks to stimulate the brain and maintain activity levels.

Permutation - based statistical tests for multiple hypotheses.

BackgroundGenomics and proteomics analyses regularly involve the simultaneous test of hundreds of hypotheses, either on numerical or categorical data. To correct for the occurrence of false positives, validation tests based on multiple testing correction, such as Bonferroni and Benjamini and Hochberg, and re-sampling, such as permutation tests, are frequently used. Despite the known power of permutation-based tests, most available tools offer such tests for either t-test or ANOVA only. Less attention has been given to tests for categorical data, such as the Chi-square. This project takes a first step by developing an open-source software tool, Ptest, that addresses the need to offer public software tools incorporating these and other statistical tests with options for correcting for multiple hypotheses.ResultsThis study developed a public-domain, user-friendly software whose purpose was twofold: first, to estimate test statistics for categorical and numerical data; and second, to validate the significance of the test statistics via Bonferroni, Benjamini and Hochberg, and a permutation test of numerical and categorical data. The tool allows the calculation of Chi-square test for categorical data, and ANOVA test, Bartletts test and t-test for paired and unpaired data. Once a test statistic is calculated, Bonferroni, Benjamini and Hochberg, and a permutation tests are implemented, independently, to control for Type I errors. An evaluation of the software using different public data sets is reported, which illustrates the power of permutation tests for multiple hypotheses assessment and for controlling the rate of Type I errors.ConclusionThe analytical options offered by the software can be applied to support a significant spectrum of hypothesis testing tasks in functional genomics, using both numerical and categorical data.

homeML: an open standard for the exchange of data within smart environments

This work describes a potential solution to the problems caused by the heterogeneous nature of the data which may be collected within smart home environments. Such information may be generated at an intra- or inter-institutional level following laboratory testing or based on in-situ evaluations. We offer a solution to this problem in the form of a system/application/format independent means of storing such data. This approach will inevitably support the exchange of data within the research community and form the basis of the establishment of an openly accessible data repository. Within this abstract we present the outline design of homeML, an XML based schema for representation of information within smart homes and through exemplars demonstrate the potential of such an approach. An example of the typical type of software browser required for the data representation is also presented.

Visualization of data for ambient assisted living services

Ambient assisted living (AAL) services that provide support for people to remain in their homes are increasingly being used in healthcare systems around the world. Typically, these ambient assisted living services provide additional information though location-awareness, presence-awareness, and context-awareness capabilities, arising from the prolific use of telecommunications devices including sensors and actuators in the home of the person receiving care. In addition there is a need to provide abstract information, in context, to local and remote stakeholders. There are many different viewing options utilizing converged networks and the resulting explosion in data and information has resulted in a new problem, as these new ambient assisted living services struggle to convey meaningful information to different groups of end users. The article discusses visualization of data from the perspective of the needs of the differing end user groups, and discusses how algorithms are required to contextualize and convey information across location and time. In order to illustrate the issues, current work on nighttime AAL services for people with dementia is described.

Smart self management: assistive technology to support people with chronic disease

We have developed a personalised self management system to support self management of chronic conditions with support from health-care professionals. Accelerometers are used to measure gross levels of activity, for example walking around the house, and used to infer higher level activity states, such as standing, sitting and lying. A smart phone containing an accelerometer and a global positioning system (GPS) module can be used to monitor outdoor activity, providing both activity and location based information. Heart rate, blood pressure and weight are recorded and input to the system by the user. A decision support system (DSS) detects abnormal activity and distinguishes life style patterns. The DSS is used to assess the self management process, and automates feedback to the user, consistent with the achievement of their life goals. We have found that telecare and assistive technology is feasible to support self management for chronic conditions within the home and local community environments.

Web-based monitoring system for home-based rehabilitation with stroke patients

Research on developing low cost home-based rehabilitation systems aim to provide support for the rehabilitation of post stroke patients in the home environment to promote/aid functional recovery and ultimately enhance their quality of life (QoL). A Web-based system has been proposed for monitoring the home-based rehabilitation and providing both therapeutic instruction and support information. The system will support specific rehabilitation interventions, provide a three-dimensional (3D) visual output and measure the effectiveness of the resulting actions undertaken by the participant. Information regarding process can be reviewed and accessed by the patient, their carers and health professionals.

iGAIT: An interactive accelerometer based gait analysis system

This paper presents a software program (iGAIT) developed in MATLAB, for the analysis of gait patterns extracted from accelerometer recordings. iGAIT provides a user-friendly graphical interface to display and analyse gait acceleration data recorded by an accelerometer attached to the lower back of subjects. The core function of iGAIT is gait feature extraction, which can be used to derive 31 features from acceleration data, including 6 spatio-temporal features, 7 regularity and symmetry features, and 18 spectral features. Features extracted are summarised and displayed on screen, as well as an option to be stored in text files for further review or analysis if required. Another unique feature of iGAIT is that it provides interactive functionality allowing users to manually adjust the analysis process according to their requirements. The system has been tested under Window XP, Vista and Window 7 using three different types of accelerometer data. It is designed for analysis of accelerometer data recorded with sample frequencies ranging from 5 Hz to 200 Hz.

Integration of Genomic Data for Inferring Protein Complexes from Global Protein–Protein Interaction Networks

Protein-protein interactions (PPIs) play crucial roles in virtually every aspect of cellular function within an organism. One important objective of modern biology is the extraction of functional modules, such as protein complexes from global protein interaction networks. This paper describes how seven genomic features and four experimental interaction data sets were combined using a Bayesian-networks-based data integration approach to infer PPI networks in yeast. Greater coverage and higher accuracy were achieved than in previous high-throughput studies of PPI networks in yeast. A Markov clustering algorithm was then used to extract protein complexes from the inferred protein interaction networks. The quality of the computed complexes was evaluated using the hand-curated complexes from the Munich Information Center for Protein Sequences database and gene-ontology-driven semantic similarity. The results indicated that, by integrating multiple genomic information sources, a better clustering result was obtained in terms of both statistical measures and biological relevance.