@inproceedings{3fda98c117034f47b31df08b8971a963,
title = "Assessing the feasibility of speech-based activity recognition in dynamic medical settings",
abstract = "We describe an experiment conducted with three domain experts to understand how well they can recognize types and performance stages of activities using speech data transcribed from verbal communications during dynamic medical teamwork. The insights gained from this experiment will inform the design of an automatic activity recognition system to alert medical teams to process deviations in real time. We contribute to the literature by (1) characterizing how domain experts perceive the dynamics of activity-related speech, and (2) identifying the challenges associated with system design for speech-based activity recognition in complex team-based work settings.",
keywords = "Activity recognition, Decision support, Emergency medicine, Narrative schema, Speech analysis, Speech modeling",
author = "Swathi Jagannath and Aleksandra Sarcevic and Neha Kamireddi and Ivan Marsic",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright is held by the author/owner(s).; 2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019 ; Conference date: 04-05-2019 Through 09-05-2019",
year = "2019",
month = may,
day = "2",
doi = "10.1145/3290607.3312983",
language = "English (US)",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
booktitle = "CHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems",
}