Abstract
Given the large impact that the spectrum and intensity of light can have on people's health and well-being, it is of fundamental importance to understand the properties of light received under normal living conditions. Historically, as research into the biological responses of light has traditionally focused on laboratory studies with controlled lighting conditions, little is known about people's light exposure outside of experimental environments. Spectrace is the first wearable compressive spectrometer designed for continuous spectral light tracking in everyday environments. This paper presents the sensor and its evaluation based on wearability considerations and three performance criteria: 1) its accuracy (in terms of spectral sensing capability), 2) its reliability (notably as far as directional response is concerned), and 3) its adaptability to the large dynamics of ambient conditions. Results show the potential use of the newly developed sensor for chronobiological studies and beyond.
Highlights
Within the last century, people's relationship with their lighting environment has dramatically changed
Over the last couple decades, investigations have been conducted to study the effects of this decoupling and, more generally, of light exposure on human biological responses, given the large impact that light can have on people's health and well-being [1,2,3]
Considering that research into the biological responses of light has traditionally focused on laboratory studies with highly controlled lighting conditions, little is known about people's light exposure outside of experimental environments
Summary
People's relationship with their lighting environment has dramatically changed. While a number of low-fidelity light-logging actigraphy dosimeters (based on tristimulus signals) have been proposed over the years in the hopes of increasing in-field viability, they have been tested repeatedly in the literature with mixed results [9,10,11,12,13] These devices seek to mimic the physiological and neurophotic responses, our understanding of the physiology and neurodynamics is still incomplete. SpecRATM relies on a concepts related to compressive sensing and data-driven sparse sensor placement [17] whereby signals can be recovered by exploiting regularities in a low-rank feature space This involves regressing on a library of n spectra in a d" × n matrix Ψ where d" = 81 is the target ‘working’ dimension. Our method is generalizable to other frequency domains and allows for the derivation of other metrics outside human biology (e.g., photosynthetic photon flux)
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