There is a great deal of evidence that seems to suggest that natural light is far more beneficial to our health than artificial light. At the most obvious level, we can see that artificial light disrupts our natural circadian rhythm. That is especially true for the infamous blue light emitted by our electronic devices that can make it difficult for us to sleep. It is also possible that indoor lighting, such as fluorescent light, is actively harmful to our physiological health. Unfortunately, it is hard to study the effects in a scientific manner, because researchers can’t accurately determine when subjects are exposed to natural or artificial light. That’s why a team from Penn State has developed a wearable device that can tell the difference between indoor and outdoor light.
At this time, it is difficult to say with any kind of certainty if natural sunlight is truly more beneficial to human health than artificial light. There are also many questions about which characteristics of natural light are potentially beneficial. It may be possible to gain the same benefits from artificial light with the proper color temperature or UV output. We just don’t know, because it is difficult to track subjects’ light exposure over a long period of time. This new wearable device can accurately identify if the user is indoors or outdoors in order to help facilitate research about the effects of natural light. If natural light is actually better for us than artificial light, the same technology could be used to nudge users to get time outside in the same way that fitness trackers like Fitbit motivate people to walk more throughout their day.
Because the characteristics of artificial light vary, it is nontrivial to determine if a user is inside or outside. Light bulbs exist that very accurately mimic the color temperature of natural light. Other bulbs produce at least some UV light, just like the sun. To make accurate judgments, this wearable device is equipped with off-the-shelf sensors that can detect the wavelength, frequency, and intensity of light. The sensors are monitored by an affordable Adafruit Adalogger 32u4 board. The sensor data is fed to an artificial neural network (ANN) machine learning model that, when properly trained, can determine if the light is natural or artificial. It uses light intensity (which varies based on time of day, latitude, cloud cover, etc.), color temperature, and UV levels to make those judgements. This data can be logged over time and would be valuable for conducting studies around the effects of natural and artificial light on human physiology. Thanks to the use of off-the-shelf components, the device only costs about $70 to build, making it practical for conducting studies with many subjects.