Obesity is one of the major health risk factors behind the rise of non-communicable conditions. Understanding the factors influencing obesity is very complex since there are many variables that can affect the health behaviors leading to it. Nowadays, multiple data sources can be used to study health behaviors, such as wearable sensors for physical activity and sleep, social media, mobile and health data. In this paper we describe the design of a dashboard for the visualization of actigraphy and biometric data from a childhood obesity camp in Qatar. This dashboard allows quantitative discoveries that can be used to guide patient behavior and orient qualitative research.

Sleep and physical activity are human behaviours that play a major role in our health. Poor sleep or lack of physical activity have been found to increase health risks and reduce quality of life. The rapid adoption and evolution of pervasive computing systems, both in the health and wellness domain, are creating a new data-intensive context in which we can learn about the sleep and physical activity patterns of individuals. In this paper we provide an overview of the toolkit we have developed to conduct research on personal health data about sleep and physical activity. This toolkit has been used to develop predictive models of sleep quality based on wearable data, and also to create data visualizations to help healthcare professionals in making decisions.

This is in response to recent feedback from some readers, which requires some clarifications regarding our IEJ oin algorithm published in [1]. The feedback revolves around four points: (1) a typo in our illustrating example of the join process; (2) a naming error for the index used by our algorithm to improve the bit array scan; (3) the sort order used in our algorithms; and (4) a missing explanation on how duplicates are handled by our self join algorithm.

This paper reports the work of creating bilingual mappings in English for certain synsets of Hindi wordnet, the need for doing this, the methods adopted and the tools created for the task.Hindi wordnet, which forms the foundation for other Indian language wordnets, has been linked to the English WordNet.To maximize linkages, an important strategy of using direct and hypernymy linkages has been followed.However, the hypernymy linkages were found to be inadequate in certain cases and posed a challenge due to sense granularity of language.Thus, the idea of creating bilingual mappings was adopted as a solution.A bilingual mapping means a linkage between a concept in two different languages, with the help of translation and/or transliteration.Such mappings retain meaningful representations, while capturing semantic similarity at the same time.This has also proven to be a great enhancement of Hindi wordnet and can be a crucial resource for multilingual applications in natural language processing, including machine translation and cross language information retrieval.

Inequality joins, which join relational tables on inequality conditions, are used in various applications. While there have been a wide range of optimization methods for joins in database systems, from algorithms such as sort-merge join and band join, to various indices such as B + -tree, R * -tree and Bitmap, inequality joins have received little attention and queries containing such joins are usually very slow. In this paper, we introduce fast inequality join algorithms. We put columns to be joined in sorted arrays and we use permutation arrays to encode positions of tuples in one sorted array w.r.t. the other sorted array. In contrast to sort-merge join, we use space efficient bit-arrays that enable optimizations, such as Bloom filter indices, for fast computation of the join results. We have implemented a centralized version of these algorithms on top of PostgreSQL, and a distributed version on top of Spark SQL. We have compared against well known optimization techniques for inequality joins and show that our solution is more scalable and several orders of magnitude faster.

In this paper we develop a confidence measure that can determine if a given set of samples is suitable for inclusion in the reconstruction of a higher resolution dataset. The confidence measure is formulated as a weighted combination of two well defined objective functions. We discuss the scope of the confidence measure and the two key factors that affect it: (i) non-uniformity of the samples and (ii) error in temporal registration. We also present a greedy iterative rank-based method that uses the confidence measure for reconstruction from multiple sample sets. The proposed method is evaluated with real video, audio and MRI data.

Recent advances in computer vision and pattern recognition have fueled numerous initiatives that aim to intelligently recognize human activities. In this paper, we propose an algorithm for nonintrusive human activity recognition. We use an adaptive background-foreground separation technique to extract motion information and generate silhouettes (foreground) from the input videos. We then derive directionality-based feature vectors (directional vectors) from the silhouette contours and use the distinct data distribution of directional vectors in a vector space for clustering and recognition. We also exploit the dynamic characteristic of human motion in order to smooth decisions over time and reduce errors in activity recognition. Our approach is monocular, tolerant to moderate view changes, and can be applied to both frontal and lateral views of most activities. Experiments with short and long video sequences show robust recognition under conditions of varying view angles, zoom depths, backgrounds, and frame rates.

Temporal registration is the establishment of correspondence between two (or more) temporal frames of video sequences, or 3-D volume data. In this paper, we propose to use event dynamics, a property that is inherent to an event and is thus common to all acquisitions of the event, for both global and local temporal registration of video sequences in order to generate high temporal resolution video. We compare our approach to a widely used linear interpolation based temporal registration algorithm and demonstrate that in the case of low temporal acquisition rate, a global event dynamics based approach, such as ours, has smaller temporal registration error. We also present a unique application of our work in solving 3-D (2D + time) high temporal resolution medical data visualization problem.

The capability of creating video data from MRI has many advantages in visualization for medical practitioners, including (i) not subjecting patients to harmful radiations, (ii) being able to monitor patients at short inter-exam time intervals, and (iii) being able to capture 3D volume data. The quality and speed with which MRI data can be acquired, however, poses a challenge towards supporting good quality visualization. In this work we present results from our preliminary attempts at enhancing the temporal resolution of video captured via MRI. Our initial focus is on visualization of swallowing and associated problems that are broadly categorized as Dysphagia. We present a method to register data from multiple swallows to generate high temporal resolution MRI videos. 1.

Human gesture recognition is an active topic of vision research which has applications in diverse fields such as collaborative virtual environments and robot teleoperation. We propose a novel method for the recognition of hand gestures, used by air marshals for steering aircraft on the runway, using the Radon transform. Various aspects of the algorithm, including acquisition, segmentation, labeling and recognition using the parametric Radon transform are addressed in this paper. A binary skeleton representation of the human subject is computed. The Radon transform is used to generate maxima corresponding to specific orientations of the skeletal representation. Feature vectors are extracted from the transform space by computing the normalized cumulative projections of the Radon transform on the angle axis. K-means clustering is then applied to recognize static gestures from the extracted features. This technique has the potential to provide information about the exact orientation of gesture segments and can find use in ground control of unmanned air vehicles. Experiments with image data corresponding to the various ground air traffic control gestures used in directing aircrafts, highlight the potential application of this approach.