Wearable Sensing for Longitudinal Automatic Task Analysis

A day in your life can be described in terms of a series of tasks: you begin each task, become loaded to some extent by the objects, movements, communication and/or mental challenges required by that task, then you later switch to a new task, and so on. However, methods for task analysis to date, such as the diary method are manual, post-hoc and subjective, and therefore impossible to scale for large numbers of users or to directly interface with computing systems.

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Published
2024-10-23
DOI
https://doi.org/10.1145/3689092.3689417
Citations
1
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.64
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Wearable Sensing for Longitudinal Automatic Task Analysis

Julien Epps
1 citations
Human-Automation Interaction and Safety
0.64
article

Wearable Sensing for Longitudinal Automatic Task Analysis

Julien Epps
article en
1 citations

Abstract

A day in your life can be described in terms of a series of tasks: you begin each task, become loaded to some extent by the objects, movements, communication and/or mental challenges required by that task, then you later switch to a new task, and so on. However, methods for task analysis to date, such as the diary method are manual, post-hoc and subjective, and therefore impossible to scale for large numbers of users or to directly interface with computing systems.

UNSW Sydney (AU)
Openalex Percentile: Top 26%
Human-Automation Interaction and Safety
0.64
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Wearable Sensing for Longitudinal Automatic Task Analysis — Julien Epps · (2024) | TGRS Research Map | TGRS