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

2026-10-05

AI Summary

This study addresses the fragmentation between behavioral traces and self-reported data, as well as the decoupling of data collection from feedback in workplace sensing. We propose an end-to-end integrated system that repurposes ActivityWatch for OS-level activity monitoring synchronized with questionnaires, incorporates StreamDeck for hardware interaction, and leverages on-device AI models to predict well-being scores. Explainable AI (XAI) techniques are introduced to enhance prediction transparency and user trust while supporting granular privacy controls. An experimental deployment involving 17 participants validated a complete workflow encompassing activity review, data acquisition, model training, privacy preservation, and XAI evaluation. Ultimately, this work provides a trustworthy, privacy-preserving solution for digital health interventions in occupational settings.

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.

Problem

• workplace sensing
• well-being prediction
• explainable AI
• privacy control
• data integration

Innovation

• Workplace Micro-Sensing
• Explainable AI (XAI)
• Local Well-Being Prediction
• Privacy Control
• Interactive Dashboard