Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Automatic image annotation applied to habitat classification

Habitat classification, the process of mapping a site with its habitats, is a crucial activity for monitoring environmental biodiversity. Phase 1 classification, a 10-class four-tier hierarchical scheme, is the most widely used scheme in the UK. Currently, no automatic approaches have been developed and its classification is carried out exclusively by ecologists. This manual approach using surveyors is laborious, expensive and subjective. To this date, no automatic approach has been developed. This thesis presents the first automatic system for Phase 1 classification. Our main contribution is an Automatic Image Annotation (AIA) framework for the automatic classification of Phase 1 habitats. This framework combines five elements to annotate unseen photographs: ground-taken geo-referenced photography, low-level visual features, medium-level semantic information, random projections forests and location-based weighted predictions. Our second contribution are two fully-annotated ground-taken photograph datasets, the first publicly available databases specifically designed for the development of multimedia analysis techniques for ecological applications. Habitat 1K has over 1,000 photographs and 4,000 annotated habitats and Habitat 3K has over 3,000 images and 11,000 annotated habitats. This is the first time ground-taken photographs have been used with such ecological purposes. Our third contribution is a novel Random Forest-based classifier: Random Projection Forests (RPF). RPFs use Random Projections as a dimensionality reduction mechanism in their split nodes. This new design makes their training and testing phase more efficient than those of the traditional implementation of Random Forests. Our fourth contribution arises from the limitations that low-level features have when classifying similarly visual classes. Low-level features have been proven to be inadequate for discriminating high-level semantic concepts, such as habitat classes. Currently, only humans posses such high-level knowledge. In order to obtain this knowledge, we create a new type of feature, called medium-level features, which use a Human-In-The-Loop approach to extract crucial semantic information. Our final contribution is a location-based voting system for RPFs. We benefit from the geographical properties of habitats to weight the predictions from the RPFs according to the geographical distance between unseen test photographs and photographs in the training set. Results will show that ground-taken photographs are a promising source of information that can be successfully applied to Phase 1 classification. Experiments will demonstrate that our AIA approach outperforms traditional Random Forests in terms of recall and precision. Moreover, both our modifications, the inclusion of medium-level knowledge and a location-based voting system, greatly improve the recall and precision of even the most complex habitats. This makes our complete image-annotation system, to the best of our knowledge, the most accurate automatic alternative to manual habitat classification for the complete categorization of Phase 1 habitats

Deliverable D1.1 State of the art and requirements analysis for hypervideo

This deliverable presents a state-of-art and requirements analysis report for hypervideo authored as part of the WP1 of the LinkedTV project. Initially, we present some use-case (viewers) scenarios in the LinkedTV project and through the analysis of the distinctive needs and demands of each scenario we point out the technical requirements from a user-side perspective. Subsequently we study methods for the automatic and semi-automatic decomposition of the audiovisual content in order to effectively support the annotation process. Considering that the multimedia content comprises of different types of information, i.e., visual, textual and audio, we report various methods for the analysis of these three different streams. Finally we present various annotation tools which could integrate the developed analysis results so as to effectively support users (video producers) in the semi-automatic linking of hypervideo content, and based on them we report on the initial progress in building the LinkedTV annotation tool. For each one of the different classes of techniques being discussed in the deliverable we present the evaluation results from the application of one such method of the literature to a dataset well-suited to the needs of the LinkedTV project, and we indicate the future technical requirements that should be addressed in order to achieve higher levels of performance (e.g., in terms of accuracy and time-efficiency), as necessary

Automatic image annotation applied to habitat classification

Habitat classification, the process of mapping a site with its habitats, is a crucial activity for monitoring environmental biodiversity. Phase 1 classification, a 10-class four-tier hierarchical scheme, is the most widely used scheme in the UK. Currently, no automatic approaches have been developed and its classification is carried out exclusively by ecologists. This manual approach using surveyors is laborious, expensive and subjective. To this date, no automatic approach has been developed. This thesis presents the first automatic system for Phase 1 classification. Our main contribution is an Automatic Image Annotation (AIA) framework for the automatic classification of Phase 1 habitats. This framework combines five elements to annotate unseen photographs: ground-taken geo-referenced photography, low-level visual features, medium-level semantic information, random projections forests and location-based weighted predictions. Our second contribution are two fully-annotated ground-taken photograph datasets, the first publicly available databases specifically designed for the development of multimedia analysis techniques for ecological applications. Habitat 1K has over 1,000 photographs and 4,000 annotated habitats and Habitat 3K has over 3,000 images and 11,000 annotated habitats. This is the first time ground-taken photographs have been used with such ecological purposes. Our third contribution is a novel Random Forest-based classifier: Random Projection Forests (RPF). RPFs use Random Projections as a dimensionality reduction mechanism in their split nodes. This new design makes their training and testing phase more efficient than those of the traditional implementation of Random Forests. Our fourth contribution arises from the limitations that low-level features have when classifying similarly visual classes. Low-level features have been proven to be inadequate for discriminating high-level semantic concepts, such as habitat classes. Currently, only humans posses such high-level knowledge. In order to obtain this knowledge, we create a new type of feature, called medium-level features, which use a Human-In-The-Loop approach to extract crucial semantic information. Our final contribution is a location-based voting system for RPFs. We benefit from the geographical properties of habitats to weight the predictions from the RPFs according to the geographical distance between unseen test photographs and photographs in the training set. Results will show that ground-taken photographs are a promising source of information that can be successfully applied to Phase 1 classification. Experiments will demonstrate that our AIA approach outperforms traditional Random Forests in terms of recall and precision. Moreover, both our modifications, the inclusion of medium-level knowledge and a location-based voting system, greatly improve the recall and precision of even the most complex habitats. This makes our complete image-annotation system, to the best of our knowledge, the most accurate automatic alternative to manual habitat classification for the complete categorization of Phase 1 habitats