research-article

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

Authors:

Tianjian ChenAuthors Info & Claims

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Pages 1 - 12

https://doi.org/10.1145/3313831.3376281

Published: 23 April 2020 Publication History

Abstract

There is an increased use of Internet-of-Things and wearable sensing devices in the urban marathon to ensure 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.

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Cited By

Dimara EStasko J(2022)A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?IEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311481328:1(1128-1138)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3114813
Deng ZWeng DLiu STian YXu MWu Y(2022)A survey of urban visual analytics: Advances and future directionsComputational Visual Media10.1007/s41095-022-0275-79:1(3-39)Online publication date: 18-Oct-2022
https://doi.org/10.1007/s41095-022-0275-7
Kaluarachchi TReis ANanayakkara S(2021)A Review of Recent Deep Learning Approaches in Human-Centered Machine LearningSensors10.3390/s2107251421:7(2514)Online publication date: 3-Apr-2021
https://doi.org/10.3390/s21072514

Index Terms

MaraVis: Representation and Coordinated Intervention of Medical Encounters in Urban Marathon
1. Human-centered computing
  1. Human computer interaction (HCI)
  2. Visualization

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cover image ACM Conferences

CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

April 2020

10688 pages

ISBN:9781450367080

DOI:10.1145/3313831

General Chairs:
Regina Bernhaupt
Eindhoven University of Technology, Netherlands
,
Florian 'Floyd' Mueller
Monash University, Australia
,
David Verweij
Newcastle University, UK
,
Josh Andres
RMIT, Australia
,
Program Chairs:
Joanna McGrenere
University of British Columbia, Canada
,
Andy Cockburn
University of Canterbury, New Zealand
,
Ignacio Avellino
University of Maryland Baltimore County, USA
,
Alix Goguey
Grenoble Alpes University, France
,
Pernille Bjørn
University of Copenhagen, Denmark
,
Shengdong (Shen) Zhao
National University of Singapore, Singapore
,
Briane Paul Samson
Future University Hakodate, Japan & De La Salle University, Philippines
,
Rafal Kocielnik
University of Washington, USA

Copyright © 2020 ACM.

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Published: 23 April 2020

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HKUST-WeBank Joint Laboratory Project - Innovative, Interactive, and Collaborative Online Advertising

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CHI '20

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CHI '20: CHI Conference on Human Factors in Computing Systems

April 25 - 30, 2020

HI, Honolulu, USA

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Cited By

Dimara EStasko J(2022)A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?IEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311481328:1(1128-1138)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3114813
Deng ZWeng DLiu STian YXu MWu Y(2022)A survey of urban visual analytics: Advances and future directionsComputational Visual Media10.1007/s41095-022-0275-79:1(3-39)Online publication date: 18-Oct-2022
https://doi.org/10.1007/s41095-022-0275-7
Kaluarachchi TReis ANanayakkara S(2021)A Review of Recent Deep Learning Approaches in Human-Centered Machine LearningSensors10.3390/s2107251421:7(2514)Online publication date: 3-Apr-2021
https://doi.org/10.3390/s21072514

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