Data for diagnosis and clinical studies are now typically gathered by hand. While more detailed, exhaustive behavioral assessments scales have been developed, they have the drawback of being too time consuming and manual assessment can be subjective. Besides, clinical knowledge is required for accurate manual assessment, for which extensive training is needed. Therefore our great research challenge is to leverage machine learning techniques to better understand patients health status automatically based on continuous computer observations. In this paper, we study the problem of health status prediction for geriatric patients using observational data. In the first part of this paper, we propose a distance metric learning algorithm to learn a Mahalanobis distance which is more precise for similarity measures. In the second part, we propose a robust classifier based on ℓ2,1-norm regression to predict the geriatric patients' health status. We test the algorithm on a dataset collected from a nursing home. Experiment shows that our algorithm achieves encouraging performance.

The Smart Health and Wellbeing workshop is organized to develop a platform for authors to discuss fundamental principles, algorithms or applications of intelligent data acquisition, processing and analysis of healthcare data. We are particularly interested in information and knowledge management papers, in which the approaches are accompanied by an in-depth experimental evaluation with real world data. This paper provides an overview of the workshop and the accepted contributions.

A number of researchers have been building high-level semantic concept detectors such as outdoors, face, building, to help with semantic video retrieval. Our goal is to examine how many concepts would be needed, and how they should be selected and used. Simulating performance of video retrieval under different assumptions of concept detection accuracy, we find that good retrieval can be achieved even when detection accuracy is low, if sufficiently many concepts are combined. We also derive suggestions regarding the types of concepts that would be most helpful for a large concept lexicon. Since our user study finds that people cannot predict which concepts will help their query, we also suggest ways to find the best concepts to use. Ultimately, this paper concludes that "concept-based" video retrieval with fewer than 5000 concepts, detected with a minimal accuracy of 10% mean average precision is likely to provide high accuracy results in broadcast news retrieval.

Social interaction plays an important role in our daily lives. It is one of the most important indicators of physical or mental changes in aging patients. In this article, we investigate the problem of detecting social interaction patterns of patients in a skilled nursing facility using audio/visual records. Our studies consist of both a “Wizard of Oz” style study and an experimental study of various sensors and detection models for detecting and summarizing social interactions among aging patients and caregivers. We first simulate plausible sensors using human labeling on top of audio and visual data collected from a skilled nursing facility. The most useful sensors and robust detection models are determined using the simulated sensors. We then present the implementation of some real sensors based on video and audio analysis techniques and evaluate the performance of these implementations in detecting interactions. We conclude the article with discussions and future work.

The authors developed an extensible system for video exploitation that puts the user in control to better accommodate novel situations and source material. Visually dense displays of thumbnail imagery in storyboard views are used for shot-based video exploration and retrieval. The user can identify a need for a class of audiovisual detection, adeptly and fluently supply training material for that class, and iteratively evaluate and improve the resulting automatic classification produced via multiple modality active learning and SVM. By iteratively reviewing the output of the classifier and updating the positive and negative training samples with less effort than typical for relevance feedback systems, the user can play an active role in directing the classification process while still needing to truth only a very small percentage of the multimedia data set. Examples are given illustrating the iterative creation of a classifier for a concept of interest to be included in subsequent investigations, and for a concept typically deemed irrelevant to be weeded out in follow-up queries. Filtering and browsing tools making use of existing and iteratively added concepts put the user further in control of the multimedia browsing and retrieval process.

This paper introduces the video collage, a novel effective interface for browsing and interpreting video collections. The paper discusses how collages are automatically produced, illustrates their use, and evaluates their effectiveness as summaries across news stories. Collages are presentations of text and images derived from multiple video sources, which provide an interactive visualization for a set of video documents, summarizing their contents and providing a navigation aid for further exploration. The dynamic creation of collages is based on user context, e.g., an originating query, coupled with automatic processing to refine the candidate imagery. Named entity identification and common phrase extraction provides descriptive text. The dynamic manipulation of collages allows user-directed browsing and reveals additional detail. The utility of collages as summaries is examined with respect to other published news summaries.

The Informedia Digital Library Project allows full content indexing and retrieval of text, audio and video material. The integration of speech recognition, image processing, natural language processing and information retrieval overcomes limits in each technology to create a useful system. In order to answer the question how good speech recognition has to be in order to be useful and usable for indexing and retrieving speech recognizer generated transcripts, some empirical evidence is presented that illustrates the degradation of information retrieval at different levels of speech accuracy. In our experiments, word error rates up to 25% did not significantly impact information retrieval and error rates of 50% still provided 85 to 95% of the recall and precision relative to fully accurate transcripts in the same retrieval system.