CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments
<|reference_start|>CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments: Wearable sensor systems have demonstrated a great potential for real-time, objective monitoring of physiological health to support behavioral interventions. However, obtaining accurate labels in free-living environments remains difficult due to limited human supervision and the reliance on self-labeling by patients, making data collection and supervised learning particularly challenging. To address this issue, we introduce CUDLE (Cannabis Use Detection with Label Efficiency), a novel framework that leverages self-supervised learning with real-world wearable sensor data to tackle a pressing healthcare challenge: the automatic detection of cannabis consumption in free-living environments. CUDLE identifies cannabis consumption moments using sensor-derived data through a contrastive learning framework. It first learns robust representations via a self-supervised pretext task with data augmentation. These representations are then fine-tuned in a downstream task with a shallow classifier, enabling CUDLE to outperform traditional supervised methods, especially with limited labeled data. To evaluate our approach, we conducted a clinical study with 20 cannabis users, collecting over 500 hours of wearable sensor data alongside user-reported cannabis use moments through EMA (Ecological Momentary Assessment) methods. Our extensive analysis using the collected data shows that CUDLE achieves a higher accuracy of 73.4%, compared to 71.1% for the supervised approach, with the performance gap widening as the number of labels decreases. Notably, CUDLE not only surpasses the supervised model while using 75% less labels, but also reaches peak performance with far fewer subjects.<|reference_end|>
@article{azghan2024cudle:,
    title={CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in
  Uncontrolled Environments},
    author={Reza Rahimi Azghan, Nicholas C. Glodosky, Ramesh Kumar Sah, Carrie
  Cuttler, Ryan McLaughlin, Michael J. Cleveland, Hassan Ghasemzadeh},
    journal={arXiv preprint arXiv:2410.03211},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03211},
    primaryClass={cs.LG}
}