Monitoring and controlling the exposure of an individual to ultraviolet (UV) radiation is crucial for personal health. The use of the global navigation satellite system (GNSS) signals received by a personal off-the-shelf smartphone has been studied as a novel estimation method. In the existing method, satellites are grouped based on their positions and the signal information is represented by group statistics, leading to a coarse estimation. We propose a new UV index estimation method that directly utilizes satellite-wise information and their spatial relationships with a point-cloud neural network, considering the similarity between GNSS signals and point clouds. We collected GNSS signals and UV index data from two locations within the same area and demonstrated that the proposed method enhances the estimation accuracy and smoothness.

Monitoring the amount of UV irradiance to which individuals are exposed and ensuring that every individual receives the optimal amount has been the subject of extensive research. In previous research, the UV index was estimated using cell phone cameras, light sensors on smartphones, or wearable UV sensors. We propose a method for estimating the UV index using the widespread global navigation satellite system (GNSS) sensors available on smart-phones. In contrast to approaches that require the sensor to be exposed continuously to the irradiance, this method, which leverages GNSS sensors, has the potential advantage of enabling UV index measurement simply by carrying the phone as usual. As a first step in measuring the index using GNSS sensors, GNSS data were collected from cell phones placed at three locations in a single area; the OpenUV API was utilized as a baseline. The proposed method achieved a mean absolute error of 0.1523, which significantly outperformed the baseline.