UKCP: A Collaborative network of cerebral palsy registers in the United Kingdom

Cerebral palsy (CP) is a relatively rare condition with enormous social and financial impact. Information about CP is not routinely collected in the United Kingdom. We have pooled non-identifiable data from the five currently active UK CP registers to form the UKCP database: birth years 1960–1997. This article describes the rationale behind this collaboration and the creation of the database. Data about 6910 children with CP are currently held. The mean annual prevalence rate was 2.0 per 1000 live births for birth years 1986–1996. Where type is known, 91 per cent have spastic CP. Where data are available, nearly one-third of children have severely impaired lower limb function, and nearly a quarter have severely impaired upper limb function. As well as describing the range and complexity of motor and associated impairments, the pooled data from the UKCP database provide a platform for studies of aetiology, long-term outcomes, participation and service needs. The UKCP database is an important national resource for the surveillance of CP and the study of its epidemiology in the United Kingdom.

Surveillance of cerebral palsy in Europe: a collaboration of cerebral palsy surveys and registers

Although cerebral palsy (CP) is the most common cause of motor deficiency in young children, it occurs in only 2 to 3 per 1000 live births. In order to monitor prevalence rates, especially within subgroups (birthweight, clinical type), it is necessary to study large populations. A network of CP surveys and registers was formed in 14 centres in eight countries across Europe. Differences in prevalence rates of CP in the centres prior to any work on harmonization of data are reported. The subsequent process to standardize the definition of CP, inclusion/exclusion criteria, classification, and description of children with CP is outlined. The consensus that was reached on these issues will make it possible to monitor trends in CP rate, to provide a framework for collaborative research, and a basis for services planning among European countries.

Evidence reasoning for event inference in smart transport video surveillance

In this paper we present a new event recognition framework, based on the Dempster-Shafer theory of evidence, which combines the evidence from multiple atomic events detected by low-level computer vision analytics. The proposed framework employs evidential network modelling of composite events. This approach can effectively handle the uncertainty of the detected events, whilst inferring high-level events that have semantic meaning with high degrees of belief. Our scheme has been comprehensively evaluated against various scenarios that simulate passenger behaviour on public transport platforms such as buses and trains. The average accuracy rate of our method is 81% in comparison to 76% by a standard rule-based method.

Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.

Hierarchical Task Network Planning with Common-Sense Reasoning for Multiple-People Behaviour Analysis

Safety on public transport is a major concern for the relevant authorities. We
address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone.