Association Model between Visual Feature and AQI Rank Using Lifelog Data

Air Quality Index (AQI) is an indicator of the rank of air pollution that is very vital for the environmental impacts to the public health. In this paper, we propose an association model between visual feature and AQI rank of lifelog data. Visual data (i.e., environmental pictures) and numerical data (i.e., environmental AQI measurements) of lifelog are utilized for the data training stage. The features of the visual data are extracted using a CNN-based method, where the latter are calculated using the standard AQI ranking. The extracted visual features and ranked AQI are combined as the input data for a deep neural network MLP (Multi-layer Perception) to study the association relationship between visual feature and AQI rank. The experimental results show that the proposed method can provide accurate predictions of good or unhealthy AQI ranks from lifelog visual data.