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 \Dataset, the first human-created question-answering (QA) dataset for long-term time-series sensor data for daily life monitoring. \Dataset 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: \url{https://github.com/benjamin-reichman/SensorQA}.

本研究针对传感器数据的解读与人机交互的不足，提出了首个基于人类创建的长期时间序列传感器数据的问答数据集。该数据集包含5.6千个多样化的实际查询，配以来自传感器数据的准确答案，评估了现有AI模型的表现，并揭示当前模型与最佳问答性能之间的差距，呼吁更多的贡献。

SensorQA：日常生活监测的问答基准