Scene-based field validation of wearable light loggers

Abstract

Wearable light loggers are increasingly used to measure personal light exposure. However, there is no standardised method to test how well these devices perform in real-world settings. To address this, we developed a scene-based field validation framework combining wearable light loggers, laboratory-grade spectral reference instruments, and image-based scene characterisation to evaluate wearable performance in the field. We applied the validation framework to ActTrust2 and ActLumus light loggers using 433 natural, everyday scenes across two sites: T\"ubingen, Germany (n=210), and Izmir, T\"urkiye (n=223), spanning indoor and outdoor environments in daylight, artificial light and mixed scenarios, across wide range of photopic and melanopic equivalent daylight illuminances. Both light loggers exhibited high agreement with the reference instruments (R^2=0.988-0.990), but they systematically underestimated light exposure. The lighting condition, scene complexity, time of day, and study site contributed significantly to measurement bias. Resampling under specific lighting conditions indicated that bias ranged from -0.065 log units in daylight-only situations to -0.195 log units in artificial-only situations. This suggested that single-condition assessments with limited spectra or scene categories can underestimate or overestimate overall wearable performance. Bootstrap resampling demonstrated that performance estimates stabilised at approximately 100 scenes, indicating that a diverse sample of this size is sufficient for reliable field validation. Finally, cross-site validation showed consistent performance across two sites, supporting the framework's reproducibility. Overall, these findings establish our validation framework as an effective tool for guiding scene diversity and sampling design for ecologically valid field validation of wearable light loggers.