Automatic Metadata Generation Through Analysis of Narration Within Instructional Videos

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Current activity recognition based assistive living solutions have adopted relatively rigid models of inhabitant activities. These solutions have some deficiencies associated with the use of these models. To address this, a goal-oriented solution has been proposed. In a goal-oriented solution, goal models offer a method of flexibly modelling inhabitant activity. The flexibility of these goal models can dynamically produce a large number of varying action plans that may be used to guide inhabitants. In order to provide illustrative, video-based, instruction for these numerous actions plans, a number of video clips would need to be associated with each variation. To address this, rich metadata may be used to automatically match appropriate video clips from a video repository to each specific, dynamically generated, activity plan. This study introduces a mechanism of automatically generating suitable rich metadata representing actions depicted within video clips to facilitate such video matching. This performance of this mechanism was evaluated using eighteen video files; during this evaluation metadata was automatically generated with a high level of accuracy

An approach to provide dynamic, illustrative, video-based guidance within a goal-driven smart home

The global population is aging in a never-before seen way, introducing an increasing ageing-related cognitive ailments, such as dementia. This aging is coupled with a reduction in the global support ratio, reducing the availability of formal and informal support and therefore capacity to care for those suffering these aging related ailments. Assistive Smart Homes (SH) are a promising form of technology enabling assistance with activities of daily living, providing support of suffers of cognitive ailments and increasing their independence and quality of life. Traditional SH systems have deficiencies that have been partially addressed by through goal-driven SH systems. Goal-driven SHs incorporate flexible activity models, goals, which partially address some of these issues. Goals may be combined to provide assistance with dynamic and variable activities. This paradigm-shift, however, introduces the need to provide dynamic assistance within such SHs. This study presents a novel approach to achieve this through video based content analysis and a mechanism to facilitate matching analysed videos to dynamic activities/goals. The mechanism behind this approach is detailed and followed by the presentation of an evaluation where showing promising results were shown