A Methodology and System For Big-Thick Data Collection

Pervasive sensors have become an essential component in research for gathering real-world data. Nevertheless, current studies on data collecting predominantly concentrate on the objective perception of reality, frequently disregarding the incorporation of subjective human contributions. We introduce an approach and system tailored for gathering big-thick data, a fusion of extensive sensor data (big data) with qualitative human feedback (thick data). Big-thick data enables effective two-way collaboration between humans and machines by supplying data that enables machine learning from human behaviour and activities, as well as data that captures the human interpretation of their actions. The system comprises of three key components: a) a tool for collecting sensor data and user feedback, b) planning and monitoring components for experiment planning and execution monitoring respectively, and c) a machine-learning component that utilizes the data to enhance human-machine interaction.