用户可自定义智能手表实时干预不良行为，仅需少量样本即可生效。
WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch
- 基于少量样本的少样本学习，适配个人独特动作识别
- 三至十次样本下准确率超76.8%，F1值达74.8%以上
- 适合需要个性化行为矫正的用户，如专注力管理或情绪调节
尽管即时干预（JITI）在改善常见健康行为方面有效，但个体对干预自身不良行为的需求各不相同，可能影响身心与社交健康。我们提出 WatchGuardian，一种基于智能手表的个性化即时干预系统，允许用户仅用少量样本自定义针对特定行为的干预策略。为在有限新数据下识别新动作，我们采用预训练惯性测量单元（IMU）模型，并在公开手势数据集上进行微调；随后设计数据增强与合成流程，训练额外分类层以实现定制化。离线评估中，26名参与者在分别使用3、5、10个样本时，平均准确率达76.8%、84.7%、87.7%，F1得分分别为74.8%、84.2%、87.2%。四小时干预实验表明，相较于规则基线，本系统使不良行为减少64.0% ± 22.6%，显著优于基准组29.0%。结果证明，该可定制、基于AI的即时干预系统在应对个体化行为问题上极具潜力，有望推动更广泛的人工智能辅助个性化干预应用。
原文摘要 · Abstract (English)
While just-in-time interventions (JITIs) have effectively targeted common health behaviors, individuals often have unique needs to intervene in personal undesirable actions that can negatively affect physical, mental, and social well-being. We present WatchGuardian, a smartwatch-based JITI system that empowers users to define custom interventions for these personal actions with a small number of samples. For the model to detect new actions based on limited new data samples, we developed a few-shot learning pipeline that finetuned a pre-trained inertial measurement unit (IMU) model on public hand-gesture datasets. We then designed a data augmentation and synthesis process to train additional classification layers for customization. Our offline evaluation with 26 participants showed that with three, five, and ten examples, our approach achieved an average accuracy of 76.8%, 84.7%, and 87.7%, and an F1 score of 74.8%, 84.2%, and 87.2% We then conducted a four-hour intervention study to compare WatchGuardian against a rule-based intervention. Our results demonstrated that our system led to a significant reduction by 64.0 +- 22.6% in undesirable actions, substantially outperforming the baseline by 29.0%. Our findings underscore the effectiveness of a customizable, AI-driven JITI system for individuals in need of behavioral intervention in personal undesirable actions. We envision that our work can inspire broader applications of user-defined personalized intervention with advanced AI solutions.
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