AWARE Narrator and the Utilization of Large Language Models to Extract Behavioral Insights from Smartphone Sensing Data

These sensors include accelerometer, GPS/geolocation, Bluetooth, communication logs (phone and SMS), application usage and keyboard activity. Given their various sensors and the opportunities to utilise them, smartphones, the Swiss army knives of digital technology, have proven to be valuable personal sensing devices, with applications in domains such as health, education and leisure. Given their potential to track various health-related behaviours and user contexts, as well as the emergence of health apps, smartphone sensing has become a pivotal topic in digital health. This is particularly the case in digital mental health, where the concept of digital phenotyping has emerged in recent years. In short, digital phenotyping espouses the idea that the data created from our use of and interaction with digital technologies, such as smartphones, can be mined or analysed to infer behaviours and, ultimately assess mental health [1, 2]. The focus of our work in this paper is on leveraging smartphone sensing as a tool in psychology and mental health. Once raw sensor data is collected, it is typically processed into information features that can be used in statistical analyses and machine learning model construction. For instance, from raw geolocation data one, features such as total distance travelled or time spent at the most visited location can be derived. In this paper, however, we propose a novel approach to analyze smartphone sensing data.