AI 中文总结
研究针对日常可穿戴活动数据采集的标注难题，提出机会人群感知系统Pebbl，通过触发-动作例程实现原位标注，实验验证其低负担、高可靠性，为用户贡献活动数据采集提供新途径。
AI 中文摘要
在日常场景中采集带有丰富标注的可穿戴活动数据仍然存在困难，因为回溯式标注成本高昂且往往不够精确。现有的数据采集应用依赖于劳动密集型的自我报告策略，且主要将参与者视为人群标注者。我们提出了Pebbl，一个处于可行性阶段的系统，该系统通过机会人群感知来激励原位标注。Pebbl允许用户在智能手机上创建触发-动作例程，当检测到触发条件时，会收到关于有益动作的即时提醒。在原型系统中，触发条件是一组有限的四种常见音频线索，而动作用开放词汇的自然语言描述。每次确认执行都会产生一个带有明确起止边界的短传感器窗口，以及用户编写的动作标签。我们通过与6名可穿戴人体活动识别（HAR）研究人员组成的专家研讨会、一项包含21名受试者的实验室内研究以及一项包含8名受试者的试点部署来评估Pebbl。专家认为，与常见的标注工作流程相比，该方法负担更低且生态效度更高。在实验室中，Pebbl在受控条件下产生了可靠的执行日志，召回率为97.30%，精确率为97.15%，并且在感知负担和置信度方面比对比工作流程更受青睐。试点部署表明，该交互和感知管道可以在自由生活使用中运行，同时也暴露出误触发和上下文依赖等实际约束。总体而言，Pebbl朝着低负担、可分发的用户贡献可穿戴活动数据采集方法迈出了一步。
英文摘要
Collecting richly labeled wearable activity data in everyday settings remains difficult because retrospective annotation is costly and often imprecise. Prior data collection apps rely on a labor-intensive self-reporting strategy and primarily treat participants as crowd labelers. We present Pebbl, a feasibility-stage system that incentivizes in-situ labeling through opportunistic crowdsensing. Pebbl lets users author trigger-action recipes on a smartphone and receive just-in-time reminders for beneficial actions when a trigger is detected. In the prototype, triggers are a limited set with four common audio cues, while actions are described in open-vocabulary natural language. Each confirmed execution yields a short sensor window with explicit start/end boundaries and a user-authored action label. We evaluate Pebbl through an expert workshop with wearable Human Activity Recognition (HAR) researchers (N = 6), a within-subject in-lab study (N = 21), and a pilot deployment (N = 8). Experts viewed the approach as lower burden and more ecologically valid than common labeling workflows. In the lab, Pebbl produced reliable execution logs under controlled conditions (recall = 97.30%, precision = 97.15%) and was preferred over comparison workflows on perceived burden and confidence. The pilot deployment shows that the interaction and sensing pipeline can function in free-living use, while surfacing practical constraints such as false triggers and context dependence. Overall, Pebbl represents a step toward a low-burden, distributable collection approach of user-contributed wearable activity data.
CommentsAccepted to IMWUT 2026 Issue 3
DOI：10.1145/3831976
