Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Constructing meaning about the delinquency of young girls in public-housing neighbourhoods

UID/SOC/04647/2013 SFRH/BPD/116119/2016Rooted in the theoretical approaches of social ecology and in childhood studies, the Ph.D. research project on which this paper is based aimed to achieve a better understanding of children’s socialization processes in multi-problematic spaces, particularly concerning their involvement in violence and delinquency. A case study based on ethnographic research and child-centred methods was carried out in six public-housing neighbourhoods in Portugal, which were chosen because they had relatively high levels of social deprivation, violence and crime. The specificity of the social group under study—children aged from 6 to 12 years old—and their living conditions, led us to extend the data collected by trying to learn, from the girls, the reasoning and the meanings they assign to their own actions in daily social practices. The intention was to study the features of girls’ socialization in the field through their own accounts of their lives and to examine their perspectives on offending behaviours. The genderized process of social learning in delinquency identified in the girls’ conversation is an important variable, as familial and social experiences tend to facilitate their entry into delinquency. The transmission of delinquent values takes place essentially within the female family circle or via female peers, rather than from the influence of male individuals.authorsversionpublishe

Self-control interventions for children under age 10 for improving self-control and delinquency and problem behaviors

Self-control improvement programs are intended to serve many purposes, most notably improving self-control. Yet, interventions such as these often aim to reduce delinquency and problem behaviors. However, there is currently no summary statement available regarding whether or not these programs are effective in improving self-control and reducing delinquency and problem behaviors. The main objective of this review is to assess the available research evidence on the effect of self-control improvement programs on self-control and delinquency and problem behaviors. In addition to investigating the overall effect of early selfcontrol improvement programs, this review will examine, to the extent possible, the context in which these programs may be most successful. The studies included in this systematic review indicate that self-control improvement programs are an effective intervention for improving self-control and reducing delinquency and problem behaviors, and that the effect of these programs appears to be rather robust across various weighting procedures, and across context, outcome source, and based on both published and unpublished data

A cooperative approach for handshake detection based on body sensor networks

Abstract—The handshake gesture is an important part of the social etiquette in many cultures. It lies at the core of many human interactions, either in formal or informal settings: exchanging greetings, offering congratulations, and finalizing a deal are all activities that typically either start or finish with a handshake. The automated detection of a handshake can enable wide range of pervasive computing scanarios; in particular, different types of information can be exchanged and processed among the handshaking persons, depending on the physical/logical contexts where they are located and on their mutual acquaintance. This paper proposes a novel handshake detection system based on body sensor networks consisting of a resource-constrained wristwearable sensor node and a more capable base station. The system uses an effective collaboration technique among body sensor networks of the handshaking persons which minimizes errors associated with the application of classification algorithms and improves the overall accuracy in terms of the number of false positives and false negatives

Collaborative body sensor networks

In this paper we propose reference architectures and SPINE-based middleware for Collaborative Body Sensor Networks (CBSNs) that can enable new smart wearable systems in the context of physical pervasive computing environments. CBSNs are wireless BSNs that are able to cooperate to support a common goal. Cooperation is therefore based on interaction among the CBSNs and distributed computation across the interacting CBSNs. In particular, interaction can be activated when CBSNs are in proximity and based on service-specific protocols that allow for service management between the involved CBSNs. Specifically, the paper presents C-SPINE, an enhancement of the SPINE middleware for CBSN applications. Finally, a collaborative emotion detection system, integrating heart rate sensing with handshake detection, is developed through C-SPINE and experimentally analyzed