Pervasive sensors have become essential in research for gathering real-world data. However, current studies often focus solely on objective data, neglecting subjective human contributions. We introduce an approach and system for collecting big-thick data, combining extensive sensor data (big data) with qualitative human feedback (thick data). This fusion enables effective collaboration between humans and machines, allowing machine learning to benefit from human behavior and interpretations. Emphasizing data quality, our system incorporates continuous monitoring and adaptive learning mechanisms to optimize data collection timing and context, ensuring relevance, accuracy, and reliability. The system comprises three key components: a) a tool for collecting sensor data and user feedback, b) components for experiment planning and execution monitoring, and c) a machine-learning component that enhances human-machine interaction.

翻译：普适传感器已成为研究中收集现实世界数据的关键工具。然而，当前研究往往仅关注客观数据，忽视了人类的主观贡献。本文提出了一种采集大数据与厚数据的方法与系统，将广泛的传感器数据（大数据）与定性的人类反馈（厚数据）相结合。这种融合促进了人机之间的有效协作，使机器学习能够从人类行为与解释中获益。本系统强调数据质量，通过持续监测与自适应学习机制来优化数据采集的时机与情境，确保数据的相关性、准确性与可靠性。该系统包含三个关键组成部分：a) 用于采集传感器数据与用户反馈的工具；b) 用于实验规划与执行监测的组件；c) 一个增强人机交互的机器学习组件。