arXiv AI
Aug 5
From Wearable Data to Personalized and Actionable Health Insights
arXiv:2608. 03251v1 Announce Type: cross Abstract: Commercial wearable devices continuously capture rich physiological data (e.
TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback
TrustmeWatcher is an application developed for the TRUST‑ME project that integrates OS‑level activity monitoring from ActivityWatch with a user interface for well‑being feedback. It transforms computer‑activity traces into an interactive screen‑time dashboard, synchronizes short questionnaire responses from a StreamDeck, and displays these alongside video highlights. The app uses local AI models to predict six normalized state scores and an overall well‑being score, providing explainable AI insights and privacy controls such as camera and eye‑tracker pause/resume, and has been tested with records from 17 participants.
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arXiv AI
Aug 25
RIACT: A Responsible AI System for Personalized Study Habit Tracking and Early Burnout Signal Detection in University Students
arXiv:2608.21379v1 Announce Type: new Abstract: Student burnout is highly prevalent in higher education, with reported rates ranging from 12% to over 70% and consistently exceeding those of the worki...
arXiv AI
Jul 29
On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomy
arXiv:2510. 12201v2 Announce Type: replace Abstract: As AI becomes more common in everyday living, there is an increasing demand for intelligent systems that are both performant and understandable.
arXiv AI
Aug 3
SERUM: State Extraction and Refinement for User Modeling
arXiv:2607. 29181v1 Announce Type: cross Abstract: Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow.
arXiv AI
Jul 14
Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos
arXiv:2607. 11523v1 Announce Type: cross Abstract: When should an intelligent assistant speak up without being asked?
arXiv AI
Jul 23
SenWorld: A Digital-Twin Simulation for Generating Context-Rich Evaluation Data
arXiv:2607. 19949v1 Announce Type: new Abstract: Smartphone personal assistants reason over longitudinal personal data, yet evaluating them requires context-rich evaluation data whose correct answers are known, and real device traces are too privacy-sensitive to share.
arXiv Computer Vision
Oct 22
ProactEgoMem: Benchmarking Proactive Memory in Streaming Egocentric Video
ProactEgoMem is a benchmark for evaluating proactive memory in streaming egocentric video, focusing on three tasks—Information Capture, State Tracking, and Location Tracking—to reflect real-world environmental needs. It introduces two levels of proactiveness: Notification Level, which requires a single timely alert, and Intervention Level, which demands continuous memory maintenance via an active tool lifecycle. The benchmark includes daily and scientific datasets with corresponding QA sets, and shows that current models struggle to capture memory both timely and accurately.
Hugging Face Trending Papers
Jul 13
Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos
When should an intelligent assistant speak up without being asked? Continuous egocentric video offers rich, evolving context that enables a new form of assistance: one that is proactive rather than merely reactive.
arXiv Machine Learning
Aug 4
TRACE-TS: Attribution-Grounded and Traceable Sensor-Language Reasoning for Human Activity Understanding
arXiv:2608. 00200v1 Announce Type: cross Abstract: Wearable sensors capture fine-grained motion patterns that support rich behavioral understanding, yet most existing methods reduce these signals to activity labels.
Hugging Face Trending Papers
Aug 4
From Wearable Data to Personalized and Actionable Health Insights
Commercial wearable devices continuously capture rich physiological data (e. g.
arXiv AI
Aug 14
DomusFM: A Foundation Model for Event-Based Behavioral Monitoring in Smart-Homes
arXiv:2602. 01910v2 Announce Type: replace Abstract: Smart-home sensor-based behavioral monitoring holds significant potential for healthcare, independent living, and early detection of functional or cognitive changes.
arXiv AI
Jun 15
TwinBI: An Agentic Digital Twin for Efficient Augmented Interactions with Business Intelligence Dashboards
arXiv:2606. 13731v1 Announce Type: new Abstract: Business intelligence (BI) increasingly combines dashboard interaction with LLM-based assistance, but these two modes often fall out of sync during multi-step analysis.