TrustmeWatcher：用于工作场所微感知与可解释幸福感反馈的应用
TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback
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中文总结 AI 辅助
提出TrustmeWatcher应用，集成ActivityWatch采集计算机活动与问卷自报，通过仪表盘展示屏幕时间并利用本地AI模型预测幸福感分数，结合可解释AI帮助用户理解预测依据，支持隐私控制，已在17名参与者中部署验证。
中文摘要 AI 辅助
工作场所感知研究将长期行为轨迹与自我报告相结合，然而收集这些数据的工具往往与返回结果的界面分离。我们介绍了TrustmeWatcher，这是为TRUST-ME项目构建的应用，旨在连接这两方面的工作。TrustmeWatcher复用了ActivityWatch的操作系统级监视器来收集计算机活动，并添加了自身的应用层。它将收集到的轨迹转化为交互式屏幕时间仪表盘，同步在StreamDeck上完成的简短问卷的回复，并在视频亮点旁边展示问卷。活动记录和自我报告被对齐为带有标签的记录，用于模型开发。本文的范围仅限于将ActivityWatch数据作为模型输入。人工智能（AI）使用这些活动记录来预测六个归一化状态分数和一个整体幸福感分数。训练好的模型在本地运行，仪表盘以语义带形式展示其预测。可解释人工智能（XAI）帮助用户理解记录的活动如何对预测做出贡献。隐私控制允许用户暂停或恢复研究使用的摄像头和眼动追踪器。我们描述了工作流程、其用户设备与感知设置的边界，以及其与17名参与者的记录的使用情况。结果是部署的应用和研究工作流程，集成了活动回顾、研究数据收集、隐私控制、本地预测以及面向参与者的XAI评估界面。
英文摘要
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.
发表机构
- Università della Svizzera italiana (USI)（瑞士意大利语区大学）
- Jožef Stefan Institute (JSI)（约瑟夫·斯特凡研究所）
- Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）
机构由 AI 辅助整理，请以论文原文为准。
