This demo paper introduces a unique mobile application for real-time monitoring of physical therapy exercises. The application learns from therapist-guided exercise sessions and provides personalized feedback during at-home sessions. Each captured session is analyzed with state-of-the-art pose estimation, segmented into individual repetitions, and at-home exercises are evaluated against a personalized template. The application also offers intuitive real-time feedback during the at-home exercise in mirrored video and a comprehensive summary of the entire session. The demo video is available at: https://youtu.be/ew1gCw9nJNk.

Digitization of human motion using 2D or 3D skeleton representations offers exciting possibilities for many applications but, at the same time, requires scalable content-based retrieval techniques to make such data reusable. Although a lot of research effort focuses on extracting content-preserving motion features, there is a lack of techniques that support efficient similarity search on a large scale. In this paper, we introduce a new indexing scheme for organizing large collections of spatio-temporal skeleton sequences. Specifically, we apply the motion-word concept to transform skeleton sequences into structured text-like motion documents, and index such documents using an extended inverted-file approach. Over this index, we design a new similarity search algorithm that exploits the properties of the motion-word representation and provides efficient retrieval with a variable level of approximation, possibly reaching constant search costs disregarding the collection size. Experimental results confirm the usefulness of the proposed approach.

The rapid growth of unstructured data, commonly denoted as the Big Data challenge, requires new technologies that are capable of dealing with complex data objects such as multimedia. In this work, the authors focus on the content-based retrieval approach, which is able to organize such data by exploiting the similarity of data content. In particular, they focus on solutions that are able to combine multiple similarity measures during the query evaluation. The authors introduce a classification of existing approaches and analyze their performance in terms of effectiveness, efficiency, and scalability. Further, they present a novel technique of inherent fusion that combines the efficiency of fast indexed retrieval with the effectiveness of ranking methods. The performance of all discussed methods is evaluated by extensive experiments with user participation.

Effective management of multimedia data is becoming vital for success in the modern era of omnipresent data. Summarization tools, which allow users to quickly get the gist of a given data collection and have proven their usefulness in text domain, are now gaining popularity also in multimedia processing. However, existing algorithms provide visual-only summaries for image collections, which are difficult to index and search. This paper introduces a prototype software tool that automatically creates multi-modal summaries of personal image collections by enriching the visual collage with keyword annotation. The result is presented as a web page that allows users to browse and share the summarized data.

Unprecedented amounts of digital data are becoming available nowadays, but frequently the data lack some semantic information necessary to effectively organize these resources. For images in particular, textual annotations that represent the semantics are highly desirable. Only a small percentage of images is created with reliable annotations, therefore a lot of effort is being invested into automatic image annotation. In this paper, we address the annotation problem from a general perspective and introduce a new annotation model that is applicable to many text assignment problems. We also provide experimental results from several implemented instances of our model.