Reducing Drift in Parametric Motion Tracking

We develop a class of differential motion trackers that automatically stabilize when in finite domains. Most differ-ential trackers compute motion only relative to one previous frame, accumulating errors indefinitely. We estimate pose changes between a set of past frames, and develop a probabilistic framework for integrating those estimates. We use an approximation to the posterior distribution of pose changes as an uncertainty model for parametric motion in order to help arbitrate the use of multiple base frames. We demonstrate this framework on a simple 2D translational tracker and a 3D, 6-degree of freedom tracker

Speaker-adaptive multimodal prediction model for listener responses

The goal of this paper is to analyze and model the variability in speaking styles in dyadic interactions and build a predictive algorithm for listener responses that is able to adapt to these different styles. The end result of this research will be a virtual human able to automatically respond to a human speaker with proper listener responses (e.g., head nods). Our novel speaker-adaptive prediction model is created from a corpus of dyadic interactions where speaker variability is analyzed to identify a subset of prototypical speaker styles. During a live interaction our prediction model automatically identifies the closest prototypical speaker style and predicts listener responses based on this ``communicative style". Central to our approach is the idea of ``speaker profile" which uniquely identifies each speaker and enables the matching between prototypical speakers and new speakers. The paper shows the merits of our speaker-adaptive listener response prediction model by showing improvement over a state-of-the-art approach which does not adapt to the speaker. Besides the merits of speaker-adapta-tion, our experiments highlights the importance of using multimodal features when comparing speakers to select the closest prototypical speaker style

Gazedirector: Fully articulated eye gaze redirection in video

We present GazeDirector, a new approach for eye gaze redirection that uses model-fitting. Our method first tracks the eyes by fitting a multi-part eye region model to video frames using analysis-by-synthesis, thereby recovering eye region shape, texture, pose, and gaze simultaneously. It then redirects gaze by 1) warping the eyelids from the original image using a model-derived flow field, and 2) rendering and compositing synthesized 3D eyeballs onto the output image in a photorealistic manner. GazeDirector allows us to change where people are looking without person-specific training data, and with full articulation, i.e. we can precisely specify new gaze directions in 3D. Quantitatively, we evaluate both model-fitting and gaze synthesis, with experiments for gaze estimation and redirection on the Columbia gaze dataset. Qualitatively, we compare GazeDirector against recent work on gaze redirection, showing better results especially for large redirection angles. Finally, we demonstrate gaze redirection on YouTube videos by introducing new 3D gaze targets and by manipulating visual behavior

Killer Apps: Developing Novel Applications That Enhance Team Coordination, Communication, and Effectiveness

As part of the Lorentz workshop, “Interdisciplinary Insights into Group and Team Dynamics,” held in Leiden, Netherlands, this article describes how Geeks and Groupies (computer and social scientists) may benefit from interdisciplinary collaboration toward the development of killer apps in team contexts that are meaningful and challenging for both. First, we discuss interaction processes during team meetings as a research topic for both Groupies and Geeks. Second, we highlight teamwork in health care settings as an interdisciplinary research challenge. Third, we discuss how an automated solution for optimal team design could benefit team effectiveness and feed into team-based interventions. Fourth, we discuss team collaboration in massive open online courses as a challenge for both Geeks and Groupies. We argue for the necessary integration of social and computational research insights and approaches. In the hope of inspiring future interdisciplinary collaborations, we develop criteria for evaluating killer apps—including the four proposed here—and discuss future research challenges and opportunities that potentially derive from these developments

Predicting Head Pose in Dyadic Conversation

Natural movement plays a significant role in realistic speech animation. Numerous studies have demonstrated the contribution visual cues make to the degree we, as human observers, find an animation acceptable. Rigid head motion is one visual mode that universally co-occurs with speech, and so it is a reasonable strategy to seek features from the speech mode to predict the head pose. Several previous authors have shown that prediction is possible, but experiments are typically confined to rigidly produced dialogue. Expressive, emotive and prosodic speech exhibit motion patterns that are far more difficult to predict with considerable variation in expected head pose. People involved in dyadic conversation adapt speech and head motion in response to the others’ speech and head motion. Using Deep Bi-Directional Long Short Term Memory (BLSTM) neural networks, we demonstrate that it is possible to predict not just the head motion of the speaker, but also the head motion of the listener from the speech signal

Characteristics of outdoor falls among older people: A qualitative study

Background Falls are a major threat to older people’s health and wellbeing. Approximately half of falls occur in outdoor environments but little is known about the circumstances in which they occur. We conducted a qualitative study to explore older people’s experiences of outdoor falls to develop understanding of how they may be prevented. Methods We conducted nine focus groups across the UK (England, Wales, and Scotland). Our sample was from urban and rural settings and different environmental landscapes. Participants were aged 65+ and had at least one outdoor fall in the past year. We analysed the data using framework and content analyses. Results Forty-four adults aged 65 – 92 took part and reported their experience of 88 outdoor falls. Outdoor falls occurred in a variety of contexts, though reports suggested the following scenarios may have been more frequent: when crossing a road, in a familiar area, when bystanders were around, and with an unreported or unknown attribution. Most frequently, falls resulted in either minor or moderate injury, feeling embarrassed at the time of the fall, and anxiety about falling again. Ten falls resulted in fracture, but no strong pattern emerged in regard to the contexts of these falls. Anxiety about falling again appeared more prevalent among those that fell in urban settings and who made more visits into their neighbourhood in a typical week. Conclusions This exploratory study has highlighted several aspects of the outdoor environment that may represent risk factors for outdoor falls and associated fear of falling. Health professionals are recommended to consider outdoor environments as well as the home setting when working to prevent falls and increase mobility among older people

Are transit trips symmetrical in time and space? Evidence from the Twin Cities

This study exploits electronic fare collection data to examine the symmetry of boardings and alightings along a transit route. The symmetry of boardings and alightings is arguably the most important concept in the estimation of travel distances such as average trip lengths and passenger miles from data from entry-only fare collection systems. The paper shows the ways such data can be used to examine the symmetry of boardings and alightings through travel patterns in spatial and temporal dimensions. A novel method for aggregating stops, especially for the nearest stops in the opposite direction, is used to compare boardings in one direction with alightings in the opposite direction. Spatially, the method allows examination of the characteristics of boardings and alightings in a spatial dimension. Temporally, the method examines whether a specific and symmetric passenger flow is observed between specific periods (e.g., between morning and afternoon peaks). A case study of the Minneapolis-Saint Paul, Minnesota, region is performed by using automatically collected data from Metro Transit. Automatic fare collection data reveal considerable variation in passenger flow between specific periods. The use of automated passenger-counting data shows this variation to be statistically significant when both temporal and spatial symmetry are examined on an individual day

Detection of delirium by nurses among long-term care residents with dementia

<p>Abstract</p> <p>Background</p> <p>Delirium is a prevalent problem in long-term care (LTC) facilities where advanced age and cognitive impairment represent two important risk factors for this condition. Delirium is associated with numerous negative outcomes including increased morbidity and mortality. Despite its clinical importance, delirium often goes unrecognized by nurses. Although rates of nurse-detected delirium have been studied among hospitalized older patients, this issue has been largely neglected among demented older residents in LTC settings. The goals of this study were to determine detection rates of delirium and delirium symptoms by nurses among elderly residents with dementia and to identify factors associated with undetected cases of delirium.</p> <p>Methods</p> <p>In this prospective study (N = 156), nurse ratings of delirium were compared to researcher ratings of delirium. This procedure was repeated for 6 delirium symptoms. Sensitivity, specificity, positive and negative predictive values were computed. Logistic regressions were conducted to identify factors associated with delirium that is undetected by nurses.</p> <p>Results</p> <p>Despite a high prevalence of delirium in this cohort (71.5%), nurses were able to detect the delirium in only a minority of cases (13%). Of the 134 residents not identified by nurses as having delirium, only 29.9% of them were correctly classified. Detection rates for the 6 delirium symptoms varied between 39.1% and 58.1%, indicating an overall under-recognition of symptoms of delirium. Only the age of the residents (≥ 85 yrs) was associated with undetected delirium (OR: 4.1; 90% CI: [1.5–11.0]).</p> <p>Conclusion</p> <p>Detection of delirium is a major issue for nurses that clearly needs to be addressed. Strategies to improve recognition of delirium could result in a reduction of adverse outcomes for this very vulnerable population.</p