MaraVis: Representation and Coordinated Intervention of Medical Encounters in Urban Marathon

Abstract

There is an increased use of Internet-of-Things and wearable sensing devices in the urban marathon to ensure an effective response to unforeseen medical needs. However, the massive amount of real-time, heterogeneous movement and psychological data of runners impose great challenges on prompt medical incident analysis and intervention. Conventional approaches compile such data into one dashboard visualization to facilitate rapid data absorption but fail to support joint decision-making and operations in medical encounters. In this paper, we present MaraVis, a real-time urban marathon visualization and coordinated intervention system. It first visually summarizes real-time marathon data to facilitate the detection and exploration of possible anomalous events. Then, it calculates an optimal camera route with an arrangement of shots to guide offline effort to catch these events in time with a smooth view transition. We conduct a within-subjects study with two baseline systems to assess the efficacy of MaraVis.

A survey of urban visual analytics: Advances and future directions

“…Organized large-scale activities may also demand emergency responses. For example, marathons should be monitored to ensure that medical assistance is timely [105]. Analyses of this kind should be in near real time, as experts need to quickly find abnormalities and respond to them.…”

“…3D maps provide a realistic urban context in a more immersive and engaging way than 2D geographic maps [150]. They are commonly used in public security [105,[114][115][116] and architectural domains [120,[122][123][124] because they provide a sense of presence. For example, Li et al [105] and Li et al [116] adopted 3D views to track people who are moving in the urban space with optimal scene Reproduced with permission from Ref.…”

“…Representation learning embeds data into a highdimensional space by vectorization [161]. In this Traffic modelling [53,60,64,80] Data cleaning [13,20] Object tracking [114] Heuristic search [65,69,70,93,98] Simulation-based [16,32,87,113,120,124] Mathematical programming [105,116] Indexing [15,17,22 space, adversarial examples can also be generated for adversarial learning [75,76]. The vectors capture the inherent relationships between data.…”

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

“…ARMath [ 125 ] is a Deep Learning powered tool for children to discover mathematical concepts with real-life objects with augmented reality. MaraVis [ 126 ] is a Deep Learning powered tool for real-time urban marathon visualization and coordinated intervention. Silva [ 127 ] is a tool to find out potential sources of unfairness in datasets or ML models.…”

“…Given that the majority in those sectors are not AI experts developing AI systems for them requires us to investigate the human aspect of such systems. Our analysis shows that application domains have specifically targeted gaming [63,70,165,174,211], interactive technologies [69,112,113,118,130,131,[133][134][135]137,140,144,152,153,155,212,213], medicine [49,57,67,110,114,180,192,193,203,214,215], psychiatry [98], music [65,83,85,102,153], sports [126], dating [60], video production [84], assistive technologies [2,80,86,88,89,…”