Human I/O: Towards a Unified Approach to Detecting Situational Impairments
arxiv(2024)
摘要
Situationally Induced Impairments and Disabilities (SIIDs) can significantly
hinder user experience in contexts such as poor lighting, noise, and
multi-tasking. While prior research has introduced algorithms and systems to
address these impairments, they predominantly cater to specific tasks or
environments and fail to accommodate the diverse and dynamic nature of SIIDs.
We introduce Human I/O, a unified approach to detecting a wide range of SIIDs
by gauging the availability of human input/output channels. Leveraging
egocentric vision, multimodal sensing and reasoning with large language models,
Human I/O achieves a 0.22 mean absolute error and a 82
availability prediction across 60 in-the-wild egocentric video recordings in 32
different scenarios. Furthermore, while the core focus of our work is on the
detection of SIIDs rather than the creation of adaptive user interfaces, we
showcase the efficacy of our prototype via a user study with 10 participants.
Findings suggest that Human I/O significantly reduces effort and improves user
experience in the presence of SIIDs, paving the way for more adaptive and
accessible interactive systems in the future.
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