We present an approach to estimate a persons light exposure using smartphones. We used web-sourced weather reports combined with smartphone light sensor data, time of day, and indoor/outdoor information, to estimate illuminance around the user throughout a day. Since light dominates every human’s circadian rhythm and influences the sleep-wake cycle, we developed a smartphone-based system that does not require additional sensors for illuminance estimation. To evaluate our approach, we conducted a free-living study with 12 users, each carrying a smartphone, a head-mounted light reference sensor, and a wrist-worn light sensing device for six consecutive days. Estimated light values were compared to the head-mounted reference, the wrist-worn device and a mean value estimate. Our results show that illuminance could be estimated at less than 20% error for all study participants, outperforming the wrist-worn device. In 9 out of 12 participants the estimation deviated less than 10% from the reference measurements.

Direct Link: https://doi.org/10.1145/2634317.2634346

Journal: ACM international symposium on wearable computers 2014 Sep 13 (pp. 43-46).

Keywords: Circadian rhythm, light exposure, smartphone,

Applications: Light Exposure,

CamNtech Reference: M14001

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