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Classifying Indoor Versus Outdoor Environments Using the Actiwatch Spectrum PRO | A 1000 Lux Threshold is

Sunya Abbasi¹, Shruthi Satheesh¹, Christine F Wildsoet¹

¹Herbert Wertheim School of Optometry and Vision Science, University of California Berkeley, Berkeley, CA, USA.

Current Eye Research

|September 29, 2025

Summary

Wearable light sensors can objectively classify environments. Red light irradiance thresholds more accurately distinguish indoor and outdoor settings than traditional white light, improving environmental classification accuracy.

Keywords:

Myopia illuminance light sensor outdoor activity wearable technology

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Area of Science:

Environmental monitoring
Wearable technology
Light sensing applications

Background:

Wearable light sensors offer objective data on habitual light environments.
Accurate classification of indoor vs. outdoor settings is crucial for various applications.
Existing methods using white light thresholds have limitations in accuracy.

Purpose of the Study:

To develop optimal illuminance and RGB irradiance thresholds for distinguishing indoor/outdoor environments.
To evaluate wearable light-sensing technology, exemplified by the Actiwatch Spectrum PRO.
To establish reliable proxies for classifying habitual light exposure.

Main Methods:

Six young adults wore the Actiwatch Spectrum PRO for two days.
The device recorded illuminance (lux) and RGB irradiance (µW/cm2) at 30-second intervals.
Participants manually logged environmental changes (indoor/outdoor); ROC analyses evaluated classification criteria.

Main Results:

A common 1000 lux white light threshold showed poor sensitivity (43.5%) for outdoor classification.
Time-dependent red light irradiance thresholds demonstrated improved performance.
Red light thresholds achieved 85.1% sensitivity and 82.6% specificity for outdoor settings.

Conclusions:

Derived red light irradiance thresholds outperform standard white light thresholds for indoor/outdoor classification.
Wavelength-dependent irradiance cutoffs show potential for more accurate environmental sensing.
Further research is needed, considering geographical, climatic, and seasonal variations.