TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback

Abstract:Workplace sensing studies combine long-running behaviour traces with self-reports, yet the tools that collect those data often sit apart from the interface that returns results. We present TrustmeWatcher, the application built for the TRUST-ME project to connect this work. TrustmeWatcher reuses ActivityWatch's OS-level watchers for computer-activity collection and adds its own application layer. It turns the collected traces into an interactive screen-time dashboard, synchronizes responses from short questionnaires completed on the StreamDeck, and presents questionnaires alongside video highlights. Activity records and self-reports are aligned into labelled records for model development. The scope of this paper is limited to ActivityWatch data as model input. Artificial intelligence (AI) uses these activity records to predict six normalized state scores and an overall well-being score. The trained model runs locally, and the dashboard presents its predictions in semantic bands. Explainable artificial intelligence (XAI) helps users understand how recorded activity contributed to a prediction. Privacy Control lets users pause or resume the camera and eye tracker used by the study. We describe the workflow, its user-device and sensing-setup boundaries, and its use with records from 17 participants. The result is a deployed application and study workflow that integrates activity review, study data collection, privacy control, local prediction, and a participant-facing interface for XAI evaluation.

4 pages, 5 figures. Accepted at XAI for U 2026, the 3rd International Workshop on Explainable AI for Ubiquitous, Pervasive and Wearable Computing, co-located with UbiComp/ISWC 2026

Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)

arXiv:2610.06657 [cs.LG]

(or arXiv:2610.06657v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.06657 arXiv-issued DOI via DataCite (pending registration)