Human I/O：迈向检测情境性损伤的统一方法
Human I/O: Towards a Unified Approach to Detecting Situational Impairments
AI总结：
本文提出 Human I/O，通过第一人称视觉、多模态感知与大语言模型推理评估人类输入/输出通道可用性，以统一方式检测多样化、动态的情境性损伤。
摘要：
情境诱发的损伤与残疾（SIIDs）会在光线不足、噪声和多任务处理等情境中显著妨碍用户体验。尽管先前研究已经提出了用于应对这些损伤的算法和系统，但它们主要服务于特定任务或环境，无法适应 SIIDs 的多样性和动态性。我们提出 Human I/O，这是一种通过评估人类输入/输出通道可用性来检测广泛 SIIDs 的统一方法。借助第一人称视角视觉、多模态感知以及利用大语言模型进行推理，Human I/O 在涵盖32种不同场景的60段真实世界第一人称视角视频记录中，于可用性预测上取得了0.22的平均绝对误差和82%的准确率。此外，尽管我们工作的核心重点是检测 SIIDs，而不是创建自适应用户界面，但我们仍通过一项包含10名参与者的用户研究展示了原型的有效性。研究结果表明，在存在 SIIDs 的情况下，Human I/O 显著减少了操作负担并改善了用户体验，为未来更具适应性和可访问性的交互系统铺平了道路。
英文摘要：
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% 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
