An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.

Characteristics and Treatment of Medical Device Related Infections

Medical device related infections are becoming an increasing prevalent area of infectious disease. They can be attributed to a multitude of factors from an increasing elderly population with reduced immunological status to increasing microbial resistance and evolution. Of greatest significance is the failure of standard antimicrobial regimens to eradicate biomaterial-related infections due to the formation of microbial biofilms consisting of extracellular polymeric substances. Biofilms form and thrive at the abiotic device surface where nutrients are more concentrated and symbiotic colonies can be formed. The formation of a biofilm matrix occurs in a series of steps beginning with reversible attachment of bacteria to the surface of the substrate and terminating in dispersion of mature biofilm microcolonies that aim to colonise fresh surfaces high in nutrients. Mature biofilms can resist 10-1000 times the concentrations of standard antibiotic regimens that are required to kill genetically equivalent planktonic forms. The extent of the infection and the pathogen(s) present can be attributed to both the form and location of the device. It is important that preventative measures and treatment strategies relate to combating the causative microorganisms. Preventative measures include: the use of anti-infective biomaterials that can be coated or incorporated with standard or innovative antimicrobials; modified anti-adhesive medical devices; environmental sterilisation protocols and prophylactic drug therapy. Treatment of established infection may require removal of the device or if deemed possible the device may be salvageable through the initiation of antimicrobial therapy. The increasing spectre of antibiotic resistance and medical device related infections are a large and increasing burden on health care systems and the patient’s quality of life and long term prognosis. As an infectious disease it represents one of the most difficult challenges facing modern science and healthcare.

FDTD analysis of close-coupled 418 MHz radiating devices for human biotelemetry

This paper describes the finite-difference time-domain (FDTD) analysis of antenna-body interaction effects occurring when chest-mounted 418 MHz radio transmitters are used for medical telemetry applications. Whole-body software models (homogeneous, layered and tissue-segmented) were developed for an adult male subject. Using an electrically small (300 mm(2)) planar loop antenna, calculated radiation efficiencies ranged between 33.5% and 39.2% for a whole-body model, and between 60.7% and 66.1% for a torso; radiation patterns were found to be largely independent of model composition. The computed radiation efficiency for a 21.5 kg phantom representing a six-year-old female was within 1.1 dB of measured results (actual body mass 28 kg) and well-correlated azimuthal radiation patterns were noted.

The potential of polymeric nanoparticles to increase the immunogenicity of the hapten clenbuterol

Micro-and nanoparticles prepared front the biodegradable and biocompatible polymers poly(lactide-co-glycolide) (PLGA) and polymetylmethacrylate (PMMA) have been successfully used as immunopotentiating antigen delivery systems. In our study, this approach was used to improve polyclonal antibody production to clenbuterol (CBL), a model hapten. PLGA and PMMA nanoparticles were loaded with either CBL alone or with a clenbuterol-transferrin conjugate (CBL-Tfn) and administered subcutaneously to mice. PLGA nano-particles were administered with or without the saponin adjuvant Quil A. The anti-CBL titres present in experimental sera were determined by an enzyme immunoassay (ELISA). CBL-Tfn-loaded PLGA nanoparticles co-administered with Quil A had obvious advantages immmunologically over the currently used method of raising antibodies to CBL (the positive control). The combined adjuvanticity of Quil A and PLGA nanoparticles resulted in a positive response in all four of the mice tested and in higher antibody titles than were seen in the positive control group. Furthermore, the sustained release of immunogen from the nanoparticles permitted a reduction in immunizing frequency over the 15-week study period.