构建首个日常监测传感器数据问答数据集，助力用户从时序数据中获取真实洞察。
SensorQA: A Question Answering Benchmark for Daily-Life Monitoring
- 由人工创建5.6K条真实生活问题，配准确答案，覆盖长期时序数据场景。
- 测试主流AI模型在边缘设备上表现，发现性能与效率均未达理想水平。
- 适合研究人机交互、智能健康、边缘计算的学者和开发者使用。
随着传感器数据的快速增长，如何以人类可理解的方式解析和交互这些数据变得至关重要。现有研究多聚焦于分类模型的学习，而较少关注终端用户如何主动从传感器数据中提取有用信息，这常受限于缺乏合适的基准数据集。为此，我们提出SensorQA，首个面向日常生活监测的长期时序传感器数据问答数据集。该数据集由人工创建，包含5.6K条多样化且实用的问题，反映真实人类兴趣，并配有基于传感器数据生成的精确答案。我们进一步为先进AI模型建立了基准，并在典型边缘设备上评估其性能。结果揭示当前模型在问答表现与效率方面仍存在显著差距，凸显了新方法的需求。数据集与代码已开源：https://github.com/benjamin-reichman/SensorQA。
原文摘要 · Abstract (English)
With the rapid growth in sensor data, effectively interpreting and interfacing with these data in a human-understandable way has become crucial. While existing research primarily focuses on learning classification models, fewer studies have explored how end users can actively extract useful insights from sensor data, often hindered by the lack of a proper dataset. To address this gap, we introduce SensorQA, the first human-created question-answering (QA) dataset for long-term time-series sensor data for daily life monitoring. SensorQA is created by human workers and includes 5.6K diverse and practical queries that reflect genuine human interests, paired with accurate answers derived from sensor data. We further establish benchmarks for state-of-the-art AI models on this dataset and evaluate their performance on typical edge devices. Our results reveal a gap between current models and optimal QA performance and efficiency, highlighting the need for new contributions. The dataset and code are available at: https://github.com/benjamin-reichman/SensorQA.
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