Individual's recollections of their experiences in eye clinics and understanding of their eye condition:results from a survey of visually impaired people in Britain

Background/aims: Network 1000 is a UK-based panel survey of a representative sample of adults with registered visual impairment, with the aim of gathering information about people’s opinions and circumstances. Method: Participants were interviewed (Survey 1, n = 1007: 2005; Survey 2, n = 922: 2006/07) on a range of topics including the nature of their eye condition, details of other health issues, use of low vision aids (LVAs) and their experiences in eye clinics. Results: Eleven percent of individuals did not know the name of their eye condition. Seventy percent of participants reported having long-term health problems or disabilities in addition to visual impairment and 43% reported having hearing difficulties. Seventy one percent reported using LVAs for reading tasks. Participants who had become registered as visually impaired in the previous 8 years (n = 395) were asked questions about non-medical information received in the eye clinic around that time. Reported information received included advice about ‘registration’ (48%), low vision aids (45%) and social care routes (43%); 17% reported receiving no information. While 70% of people were satisfied with the information received, this was lower for those of working age (56%) compared with retirement age (72%). Those who recalled receiving additional non-medical information and advice at the time of registration also recalled their experiences more positively. Conclusions: Whilst caution should be applied to the accuracy of recall of past events, the data provide a valuable insight into the types of information and support that visually impaired people feel they would benefit from in the eye clinic.

Condition monitoring of pipelines

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Inference of helicopter airframe condition

The goal of this paper is to model normal airframe conditions for helicopters in order to detect changes. This is done by inferring the flying state using a selection of sensors and frequency bands that are best for discriminating between different states. We used non-linear state-space models (NLSSM) for modelling flight conditions based on short-time frequency analysis of the vibration data and embedded the models in a switching framework to detect transitions between states. We then created a density model (using a Gaussian mixture model) for the NLSSM innovations: this provides a model for normal operation. To validate our approach, we used data with added synthetic abnormalities which was detected as low-probability periods. The model of normality gave good indications of faults during the flight, in the form of low probabilities under the model, with high accuracy (>92 %). © 2013 IEEE.