A Methodology and a Platform for High-quality Rich Personal Data Collection

In recent years, pervasive use of sensors in smart devices, e.g., phones, watches, medical devices, has increased dramatically the availability of personal data. However, existing research on data collection primarily focuses on the objective view of reality, as provided, for instance, by sensors, often neglecting the integration of subjective human input, as provided, for instance, by user answers to questionnaires. This limits substantially the exploitability of the collected data. In this paper, we present a methodology and a platform designed for the collection of a combination of large-scale sensor data and qualitative human feedback. The methodology has been designed to be deployed on top, and enrich functionalities of an existing data collection APP, called iLog, which has been used in large scale, worldwide data collection experiments. The main goal is to put the key actors involved in an experiment, i.e., the researcher in charge, the participant, and iLog in better control of the experiment itself, thus improving the quality and richness of the data collected. The novel functionalities of the resulting platform are: (i) a time-wise representation of the situational context within which the data collection is performed, (ii) an explicit representation of the temporal context within which the data collection is performed, (iii) a calendar-based dashboard for the real-time monitoring of the data collection context(s), and, (iv) a mechanism for the run-time revision of the data collection plan. The practicality and utility of the proposed functionalities are demonstrated in a case study involving 350 University students.