SBIR Phase I: Development of An Accurate Low-Cost Wearable Ultraviolet Dosimeter For The General Population

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to equip consumers with a scientific tool to measure and control their exposure to ultraviolet (UV) radiation, thereby mitigating their risk of getting skin cancer while enjoying the benefits of sunlight such as Vitamin D and outdoors activities. In the United States, skin cancer has become a major public health issue with an estimated 3.5M people being treated each year for a cost of over $8B. The rate of Vitamin D deficiency in the US has been estimated to be over 40% leading to increased risk for depression, cardiovascular disease, and cancer. Up until now, accurate measurement of ultraviolet exposure remains confined to research laboratories. This project aims at carrying a scientific breakthrough in UV dosimetry and bring a laboratory-grade technology to consumers. The proposed project aims to achieve a scientific breakthrough in UV dosimetry by combining several detectors, machine learning algorithms, and customized calibration. We anticipate that this breakthrough will lead to a drastic improvement in accuracy to closer match that of laboratory-grade equipment, while keeping the size and cost of our instrument in line with consumers? expectations. The scientific challenge is to have the UV sensor be accurate when it measures most solar spectra, and there is an infinity of these based on location, weather, and time of the year. Our strategy is to stay away from incremental improvements (e.g. filter optimization) or expensive developments (e.g. a full-blown spectrometer). Instead, we are testing the hypothesis that the combination of machine learning algorithms and a small set of carefully chosen detectors will enable us to build a small low-cost instrument able to identify the local solar spectrum and provide correctly calibrated real-time measurements. To achieve this, we propose to apply clustering techniques to find representative spectra of solar UV and train several detectors to recognize them and correct the measurement. The execution of this project requires top-level R&D among diverse collaborators, whom we have gathered for this project.