# WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch

arXiv: https://arxiv.org/abs/2502.05783 · HTML: https://mlsanity.com/paper/2502.05783/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 162 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Multi-task Self-Supervised Learning for Human Activity Detection](https://mlsanity.com/paper/1907.11879/) (2019)
- [Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data](https://mlsanity.com/paper/2206.02909/) (2022)
- [Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity](https://mlsanity.com/paper/1909.03539/) (2019)
- [Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches](https://mlsanity.com/paper/1609.06354/) (2016)
- [Data Augmentation techniques in time series domain: A survey and taxonomy](https://mlsanity.com/paper/2206.13508/) (2022)
- [Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens](https://mlsanity.com/paper/2102.00593/) (2021)
- [IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2402.01049/) (2024)
- [Near-Tight Margin-Based Generalization Bounds for Support Vector Machines](https://mlsanity.com/paper/2006.02175/) (2020)
- [Online Few-shot Gesture Learning on a Neuromorphic Processor](https://mlsanity.com/paper/2008.01151/) (2020)
- [FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2011.06104/) (2020)
- [Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure](https://mlsanity.com/paper/2306.07458/) (2023)
- [MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention](https://mlsanity.com/paper/2309.16639/) (2023)
- [Enabling hand gesture customization on wrist-worn devices](https://mlsanity.com/paper/2203.15239/) (2022)
- [On PAC-Bayesian Bounds for Random Forests](https://mlsanity.com/paper/1810.09746/) (2018)
- [Detecting Receptivity for mHealth Interventions in the Natural Environment](https://mlsanity.com/paper/2011.08302/) (2020)
- [EchoWrist: Continuous Hand Pose Tracking and Hand-Object Interaction Recognition Using Low-Power Active Acoustic Sensing On a Wristband](https://mlsanity.com/paper/2401.17409/) (2024)
- [Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention](https://mlsanity.com/paper/2403.05584/) (2024)
- [InteractOut: Leveraging Interaction Proxies as Input Manipulation Strategies for Reducing Smartphone Overuse](https://mlsanity.com/paper/2401.16668/) (2024)
- [GesturePrint: Enabling User Identification for mmWave-based Gesture Recognition Systems](https://mlsanity.com/paper/2408.05358/) (2024)
- [Enabling Voice-Accompanying Hand-to-Face Gesture Recognition with Cross-Device Sensing](https://mlsanity.com/paper/2303.10441/) (2023)
- [Anticipatory Detection of Compulsive Body-focused Repetitive Behaviors with Wearables](https://mlsanity.com/paper/2106.10970/) (2021)
- [Wearable Sensor-Based Few-Shot Continual Learning on Hand Gestures for Motor-Impaired Individuals via Latent Embedding Exploitation](https://mlsanity.com/paper/2405.08969/) (2024)

## Built on by (most cited first, 4 of 4)

- [ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild](https://mlsanity.com/paper/2512.06721/) (2025)
- [Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation](https://mlsanity.com/paper/2609.03727/) (2026)
- [Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks](https://mlsanity.com/paper/2603.18570/) (2026)
- [FM-FoG: A Real-Time Foundation Model-based Wearable System for Freezing-of-Gait Mitigation](https://mlsanity.com/paper/2509.24176/) (2025)

## Abstract (arXiv)

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.