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RayWatch: Hemispherical Diffusion on Wrist UV Sensor for Indoor-Outdoor Sensing
Harrison Dong1,2, Glenn Fernandes1,2, Christopher Romano1,2
1Department of Computer Science, Northwestern University, Evanston, IL, USA.
Abstract:
Excessive ultraviolet (UV) exposure is the principal driver of melanoma, yet at-risk individuals seldom receive timely, context-aware cues to apply protection. Existing wrist-worn UV monitors often struggle to recognize timely outdoor exposure because UV readings vary sharply with wrist orientation and sensor angle. To address this gap, we developed a wrist-watch form-factor device that embeds an AS7331 UV photodiode beneath a hemispherical polytetrafluoroethylene (PTFE) dome, which diffuses incident light and stabilizes the sensor's angular response. Ten participants wore the device during routine daily activities, generating more than 93k datapoints of annotated indoor-outdoor data. We implemented an on-device logistic-regression classifier, integrating UVA, UVB, and inertial features to distinguish indoor from outdoor contexts. Under leave-one-participant-out cross-validation, the PTFE-dome watch achieved 94% accuracy and a weighted F1 score of 0.95, outperforming both a flat-window GUVA-S12SD sensor (71% accuracy, F1 = 0.72) and a cylindrical-PTFE enclosure (84% accuracy, F1 = 0.85). By coupling a compact PTFE dome with on-device machine learning (ML), our system delivers reliable, on-wrist UV context sensing and paves the way for unobtrusive, personalized interventions to reduce cumulative UV exposure.

