WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch
Ying Lei, Yancheng Cao, Will Wang, Yuanzhe Dong, Changchang Yin,, Weidan Cao, Ping Zhang, Jingzhen Yang, Bingsheng Yao, Yifan Peng, Chunhua, Weng, Randy Auerbach, Lena Mamykina, Dakuo Wang, Yuntao Wang, Xuhai Xu

TL;DR
WatchGuardian is a smartwatch system that enables users to define personalized, AI-driven interventions for undesirable actions with minimal data, significantly reducing such behaviors.
Contribution
It introduces a few-shot learning pipeline and data augmentation techniques for personalized action detection on smartwatches, enabling user-defined interventions with limited samples.
Findings
Achieved up to 87.7% accuracy with 10 samples
Reduced undesirable actions by 64% in real-world study
Outperformed rule-based interventions significantly
Abstract
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…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems
