mobile experience sampling

Existing mobile sensing data collection methods neglect subjective feedback (e.g., questionnaires, self-reports), leading to fragmented contextual understanding and inaccurate behavioral modeling. To address this, we propose a human-in-the-loop collaborative sensing framework built upon the iLog platform. Our approach introduces three key innovations: (1) a dual-dimensional “context–time” modeling paradigm; (2) a calendar-style real-time monitoring dashboard; and (3) a dynamic acquisition plan revision mechanism. Leveraging context-aware modeling, real-time visual analytics, an adaptive experimental workflow engine, and purposeful human–system interaction design, the framework enhances controllability for researchers, participants, and the system itself. Evaluated with 350 university students, our method significantly improves semantic richness, contextual completeness, and overall data quality—enabling more accurate behavioral modeling and fine-grained personalized analysis.

Mobile applications widely rely on sensor data to infer user context for personalization, yet such implicit inference logic lacks transparency. To address this, we propose a sandbox-based auditing framework designed to enhance transparency: it employs sensor simulation and structured virtual personas to inject multimodal behavioral data in real time; integrates Android-side real-time data injection, automated screenshot capture, and GPT-4 Vision–driven UI semantic parsing; and establishes an end-to-end behavior–response analysis pipeline. Unlike conventional adversarial sensor spoofing, our approach pioneers the use of sensor forgery for *observable auditing*, enabling dynamic visualization and reproducible validation of personalization mechanisms. We evaluate the framework across fitness, e-commerce, and lifestyle service apps, demonstrating statistically significant response variations under controlled changes in activity level, location, and time-of-day. Our work provides both a novel paradigm and empirical foundation for privacy-enhancing design and user-controllable transparency tools.

为理解社交媒体使用如何影响福祉，提出CAST框架，通过多维度测量和建模个体在不同时间尺度上的行为、生理及体验。

This study addresses the challenge of predicting regret over social media use in real-world settings. Through a seven-day in-the-wild experience sampling study integrating smartphone usage logs, physiological sensing via Bangle.js 2 smartwatches, and session-level questionnaires and interviews (N=21 participants, 1,445 sessions), it demonstrates for the first time that the discrepancy between intended and actual usage is a stronger predictor of regret than conventional duration-based metrics. Regret is found to be more likely following nighttime use or use after productivity-related applications. The work further reveals the influence of perceived alternative activity value on regret and proposes a two-tier intervention framework that combines general contextual features—exhibiting cross-user generalizability—with personalized physiological signals, which enhance individualized prediction accuracy.

Academic researchers face significant challenges in collecting mobile screen data—including limited access due to proprietary platform restrictions, stringent commercial monopolies, and heightened privacy compliance requirements. Existing open-source frameworks predominantly focus on sensor data and lack robust, privacy-compliant, and flexible mechanisms for capturing screen content. Method: We propose Crepe, the first no-code Android screen data collection tool designed specifically for academic research. It introduces a novel graph-query-based UI structural representation to enable semantic identification and high-precision localization of screen elements. Crepe integrates declarative demonstration learning, on-device processing, and a permission sandbox to ensure informed consent and real-time user opt-out. Contribution/Results: Empirical evaluation across diverse applications demonstrates that Crepe achieves zero-configuration extraction of dynamic text and UI controls with high accuracy, effectively circumventing data monopolies while enabling privacy-preserving screen-content research.

研究通过分析457万次应用事件，利用应用活动日志而非回忆使用情况来理解用户行为，揭示了不同群体的使用模式和行为差异。

This study addresses the problem of task interruption and difficulty in tracking outcomes caused by user distraction during mobile voice interactions. To this end, it proposes a request-centric decoupled architecture that separates conversation, execution, and delivery. By employing persistent request record management, the system supports task interruption, revision, and retrieval under intermittent user engagement. Validation through an Android prototype demonstrates that the system can sustain execution after disconnection, preserve results, and provide failure feedback, thereby achieving state visualization. Ultimately, this work establishes a foundational framework for persistent voice services on personal computing devices.

This study addresses the limitations of existing research on non-driving activities (NDAs) in fully autonomous driving, which often overlooks the influence of diverse scenarios and struggles to capture dynamic contexts through traditional methods. To bridge this gap, we propose an experience-centered research framework that reconceptualizes NDAs not as isolated time-fillers but as dynamic sequences shaped by pre- and post-trip contexts. Methodologically, this work integrates participatory design, diary studies, narrative scripting, and mixed reality technologies to conduct a comprehensive investigation. The findings reveal user preferences for in-vehicle activities and their subjective significance, thereby reshaping the conceptualization of vehicles as sociotechnical systems. Furthermore, this research validates the effectiveness of the proposed approach in connecting present-day user experiences with future mobility scenarios.

通过真实交通环境下的实地研究，使用多模态感知技术评估乘客体验，解决了自动驾驶车辆用户接受度问题。

研究通过多模态情绪标注方法，解决传统数据收集方式忽视参与者可用性和情感复杂性的问题，以支持更丰富细腻的情绪数据收集。