人类输入/输出:采用统一方法检测情境障碍

ArXiv Pub Date : 2024-03-06 DOI:10.1145/3613904.3642065
Xingyu Bruce Liu, Jiahao Nick Li, David Kim, Xiang 'Anthony' Chen, Ruofei Du
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引用次数: 0

摘要

在光线不足、噪音和多任务处理等情况下,情境诱发的障碍和残疾(SIIDs)会严重影响用户体验。虽然之前的研究已经推出了解决这些障碍的算法和系统,但它们主要是针对特定的任务或环境,无法适应 SIIDs 的多样性和动态性。我们引入了人类输入/输出(Human I/O),这是一种通过测量人类输入/输出通道的可用性来检测各种 SIID 的统一方法。人类 I/O 利用以自我为中心的视觉、多模态传感和大型语言模型推理,在 32 种不同场景下的 60 个野外以自我为中心的视频记录中,实现了 0.22 的平均绝对误差和 82% 的可用性预测准确率。此外,虽然我们工作的核心重点是检测 SIID 而不是创建自适应用户界面,但我们通过对 10 名参与者的用户研究展示了我们原型的功效。研究结果表明,人类输入/输出(Human I/O)可以在出现 SIID 的情况下显著减少工作量并改善用户体验,为未来开发更具适应性和可访问性的交互系统铺平了道路。
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Human I/O: Towards a Unified Approach to Detecting Situational Impairments
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% accuracy in 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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