A Multiscale Geospatial Dataset and an Interactive Visualization Dashboard for Computational Epidemiology and Open Scientific Research.

The coronavirus disease (COVID-19) continued to strike as a highly infectious and fast-spreading disease in 2020 and 2021. As the research community actively responded to this pandemic, we saw the release of many COVID-19-related datasets and visualization dashboards. However, existing resources are insufficient to support multiscale and multifaceted modeling or simulation, which is suggested to be important by the computational epidemiology literature. This work presents a curated multiscale geospatial dataset with an interactive visualization dashboard under the context of COVID-19. This open dataset will allow researchers to conduct numerous projects or analyses relating to COVID-19 or simply geospatial-related scientific studies. The interactive visualization platform enables users to visualize the spread of the disease at different scales (e.g., country level to individual neighborhoods), and allows users to interact with the policies enforced at these scales (e.g., the closure of borders and lockdowns) to observe their impacts on the epidemiology.

Heterogeneous Crowd Simulation Using Parametric Reinforcement Learning.

Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and animation of interacting digital humans. Model-based approaches have successfully generated a plethora of simulators with a variety of foundations. However, prior approaches have been based on statically defined models predicated on simplifying assumptions, limited video-based datasets, or homogeneous policies. Recent works have applied reinforcement learning to learn policies for navigation. However, these approaches may learn static homogeneous rules, are typically limited in their generalization to trained scenarios, and limited in their usability in synthetic crowd domains. In this article, we present a multi-agent reinforcement learning-based approach that learns a parametric predictive collision avoidance and steering policy. We show that training over a parameter space produces a flexible model across crowd configurations. That is, our goal-conditioned approach learns a parametric policy that affords heterogeneous synthetic crowds. We propose a model-free approach without centralization of internal agent information, control signals, or agent communication. The model is extensively evaluated. The results show policy generalization across unseen scenarios, agent parameters, and out-of-distribution parameterizations. The learned model has comparable computational performance to traditional methods. Qualitatively the model produces both expected (laminar flow, shuffling, bottleneck) and unexpected (side-stepping) emergent qualitative behaviours, and quantitatively the approach is performant across measures of movement quality.

Gamification of Crowd-Driven Environment Design.

This article explores whether crowd-sourced human creativity within a gamified collaborative design framework can address the complexity of predictive environment design. This framework is predicated on gamifying crowd objectives and presenting environment design problems as puzzles. A usability study reveals that the framework is considered usable for the task. Participants were asked to configure an environment puzzle to reduce an important crowd metric, the total egress time. The design task was constructed to be straightforward and uses a simplified environment as a probe for understanding the utility of gamification and the performance of collaboration. Single-player and multiplayer designs outperformed both optimization and expert-sourced designs of the same environment and multiplayer designs further outperformed the single-player designs. Single-player and multiplayer iterations followed linear and exponential decrease trends in total egress time, respectively. Our experiments provide strong evidence toward an interesting novel approach of crowdsourcing collaborative environment design.

Interactive Architectural Design with Diverse Solution Exploration.

In architectural design, architects explore a vast amount of design options to maximize various performance criteria, while adhering to specific constraints. In an effort to assist architects in such a complex endeavour, we propose IDOME, an interactive system for computer-aided design optimization. Our approach balances automation and control by efficiently exploring, analyzing, and filtering space layouts to inform architects' decision-making better. At each design iteration, IDOME provides a set of alternative building layouts which satisfy user-defined constraints and optimality criteria concerning a user-defined space parametrization. When the user selects a design generated by IDOME, the system performs a similar optimization process with the same (or different) parameters and objectives. A user may iterate this exploration process as many times as needed. In this work, we focus on optimizing built environments using architectural metrics by improving the degree of visibility, accessibility, and information gaining for navigating a proposed space. This approach, however, can be extended to support other kinds of analysis as well. We demonstrate the capabilities of IDOME through a series of examples, performance analysis, user studies, and a usability test. The results indicate that IDOME successfully optimizes the proposed designs concerning the chosen metrics and offers a satisfactory experience for users with minimal training.

Treating Speech Movement Hypokinesia in Parkinson's Disease: Does Movement Size Matter?

Purpose: This study evaluates the effects of a novel speech therapy program that uses a verbal cue and gamified augmented visual feedback regarding tongue movements to address articulatory hypokinesia during speech in individuals with Parkinson's disease (PD). Method: Five participants with PD participated in an ABA single-subject design study. The treatment aimed to increase tongue movement size using a combination of a verbal cue and augmented visual feedback and was conducted in 10 45-min sessions over 5 weeks. The presence of visual feedback was manipulated during treatment. Articulatory working space of the tongue was the primary outcome measure and was examined during treatment and in cued and uncued sentences pre- and posttreatment. Changes in speech intelligibility in response to a verbal cue pre- and posttreatment were also examined. Results: During treatment, 4/5 participants showed a beneficial effect of visual feedback on tongue articulatory working space. At the end of the treatment, they used larger tongue movements when cued, relative to their pretreatment performance. None of the participants, however, generalized the effect to the uncued sentences. Speech intelligibility of cued sentences was judged as superior posttreatment only in a single participant. Conclusions: This study demonstrated that using an augmented visual feedback approach is beneficial, beyond a verbal cue alone, in addressing articulatory hypokinesia in individuals with PD. An optimal degree of articulatory expansion might, however, be required to elicit a speech intelligibility benefit.

Sentence-Level Movements in Parkinson's Disease: Loud, Clear, and Slow Speech.

Purpose: To further understand the effect of Parkinson's disease (PD) on articulatory movements in speech and to expand our knowledge of therapeutic treatment strategies, this study examined movements of the jaw, tongue blade, and tongue dorsum during sentence production with respect to speech intelligibility and compared the effect of varying speaking styles on these articulatory movements. Method: Twenty-one speakers with PD and 20 healthy controls produced 3 sentences under normal, loud, clear, and slow speaking conditions. Speech intelligibility was rated for each speaker. A 3-dimensional electromagnetic articulograph tracked movements of the articulators. Measures included articulatory working spaces, ranges along the first principal component, average speeds, and sentence durations. Results: Speakers with PD demonstrated significantly smaller jaw movements as well as shorter than normal sentence durations. Between-speaker variation in movement size of the jaw, tongue blade, and tongue dorsum was associated with speech intelligibility. Analysis of speaking conditions revealed similar patterns of change in movement measures across groups and articulators: larger than normal movement sizes and faster speeds for loud speech, increased movement sizes for clear speech, and larger than normal movement sizes and slower speeds for slow speech. Conclusions: Sentence-level measures of articulatory movements are sensitive to both disease-related changes in PD and speaking-style manipulations.

Evaluating and Optimizing Evacuation Plans for Crowd Egress.

Evacuation planning is an important and difficult task in building design. The proposed framework can identify optimal evacuation plans using decision points, which control the ratio of agents that select a particular route at a specific spatial location. The authors optimize these ratios to achieve the best evacuation based on a quantitatively validated metric for evacuation performance. This metric captures many of the important aspects of an evacuation: total evacuation time, average evacuation time, agent speed, and local agent density. The proposed approach was validated using a night club model that incorporates real data from an actual evacuation.

Game-Based Augmented Visual Feedback for Enlarging Speech Movements in Parkinson's Disease.

Purpose: The purpose of this pilot study was to demonstrate the effect of augmented visual feedback on acquisition and short-term retention of a relatively simple instruction to increase movement amplitude during speaking tasks in patients with dysarthria due to Parkinson's disease (PD). Method: Nine patients diagnosed with PD, hypokinetic dysarthria, and impaired speech intelligibility participated in a training program aimed at increasing the size of their articulatory (tongue) movements during sentences. Two sessions were conducted: a baseline and training session, followed by a retention session 48 hr later. At baseline, sentences were produced at normal, loud, and clear speaking conditions. Game-based visual feedback regarding the size of the articulatory working space (AWS) was presented during training. Results: Eight of nine participants benefited from training, increasing their sentence AWS to a greater degree following feedback as compared with the baseline loud and clear conditions. The majority of participants were able to demonstrate the learned skill at the retention session. Conclusions: This study demonstrated the feasibility of augmented visual feedback via articulatory kinematics for training movement enlargement in patients with hypokinesia due to PD. Supplemental Materials: https://doi.org/10.23641/asha.5116840.

Visual tracking of laparoscopic instruments in standard training environments.

We propose a method for accurately tracking the spatial motion of standard laparoscopic instruments from video. By exploiting the geometric and photometric invariants common to standard FLS training boxes, the method provides robust and accurate tracking of instruments from video. The proposed method requires no modifications to the standard FLS training box, camera or instruments.

Support vector machines improve the accuracy of evaluation for the performance of laparoscopic training tasks.

BACKGROUND: Despite technological advances in the tracking of surgical motions, automatic evaluation of laparoscopic skills remains remote. A new method is proposed that combines multiple discrete motion analysis metrics. This new method is compared with previously proposed metric combination methods and shown to provide greater ability for classifying novice and expert surgeons. METHODS: For this study, 30 participants (four experts and 26 novices) performed 696 trials of three training tasks: peg transfer, pass rope, and cap needle. Instrument motions were recorded and reduced to four metrics. Three methods of combining metrics into a prediction of surgical competency (summed-ratios, z-score normalization, and support vector machine [SVM]) were compared. The comparison was based on the area under the receiver operating characteristic curve (AUC) and the predictive accuracy with a previously unseen validation data set. RESULTS: For all three tasks, the SVM method was superior in terms of both AUC and predictive accuracy with the validation set. The SVM method resulted in AUCs of 0.968, 0.952, and 0.970 for the three tasks compared respectively with 0.958, 0.899, and 0.884 for the next best method (weighted z-normalization). The SVM method correctly predicted 93.7, 91.3, and 90.0% of the subjects' competencies, whereas the weighted z-normalization respectively predicted 86.6, 79.3, and 75.7% accurately (p < 0.002). CONCLUSIONS: The findings show that an SVM-based analysis provides more accurate predictions of competency at laparoscopic training tasks than previous analysis techniques. An SVM approach to competency evaluation should be considered for computerized laparoscopic performance evaluation systems.