MINDSET: A benchMarking suIte exploring seNsing Data for SElf sTates inference

Ubiquitous devices, such as smartphones and wearables, are becoming increasingly popular for monitoring user behavior, health, and well-being. Through omnipresent monitoring, they aim to raise user awareness and encourage positive health behavior change. Yet, ubiquitous technologies suffer from expectation mismatch, lack of user-centric adaptiveness, and, ultimately, high abandonment rates. This work is motivated by the vital need to tailor and personalize ubiquitous technologies while dealing with the challenges arising from the lack of user profiling and the absence of relevant, self-reported user data. To this end, we show that the automatically passively collected sensing data from the wearables can be exploited to improve personalization and infer several user states relevant to demographic, physiological, psychological, and personality aspects, complementing the need for time-consuming self-reports. To accomplish this task, and enable the reproducibility and extensibility of our work; we propose an extensive benchmark suite by exploiting sensing data harvested from ubiquitous devices.Our benchmark covers a wide range of personalization tasks, including modeling gender, age, personality states, and stress, experimenting on the publicly available, newly released LifeSnaps dataset containing over 71 million rows of data capturing the daily lives of 71 participants in their naturalistic environments. The proposed benchmarking continuum showcases the strong potential for the applicability of the presented work in critical applications, such as mental healthcare monitoring, privacy preservation, and responsible artificial intelligence (AI), by fostering fairness assessments when protected attribute knowledge is unavailable.