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.
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Cite this article
APA 7
Yu, C., Costa, L. J., Anžur, Z., Li, M., Slapničar, G., Kirilenko, D., Gjoreski, M., Luštrek, M., & Langheinrich, M. (2026). TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback. https://omanscience.com/en/articles/trustmewatcher-an-application-for-workplace-micro-sensing-and-explainable-well-being-feedback
MLA 9
Yu, Chengyu, et al. "TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback." https://omanscience.com/en/articles/trustmewatcher-an-application-for-workplace-micro-sensing-and-explainable-well-being-feedback.
Chicago (author–date)
Yu, Chengyu, Leon Jacopo Costa, Zoja Anžur, Mohan Li, Gašper Slapničar, Daniil Kirilenko, Martin Gjoreski, Mitja Luštrek, and Marc Langheinrich. 2026. "TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback." https://omanscience.com/en/articles/trustmewatcher-an-application-for-workplace-micro-sensing-and-explainable-well-being-feedback.
Harvard
Yu, C., Costa, L. J., Anžur, Z., Li, M., Slapničar, G., Kirilenko, D., Gjoreski, M., Luštrek, M. and Langheinrich, M. (2026) 'TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback', Available at: https://omanscience.com/en/articles/trustmewatcher-an-application-for-workplace-micro-sensing-and-explainable-well-being-feedback.
Vancouver
Yu C, Costa LJ, Anžur Z, Li M, Slapničar G, Kirilenko D, et al. TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback. https://omanscience.com/en/articles/trustmewatcher-an-application-for-workplace-micro-sensing-and-explainable-well-being-feedback
IEEE
C. Yu, L. J. Costa, Z. Anžur, M. Li, G. Slapničar, D. Kirilenko, M. Gjoreski, M. Luštrek, and M. Langheinrich, "TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback," https://omanscience.com/en/articles/trustmewatcher-an-application-for-workplace-micro-sensing-and-explainable-well-being-feedback.