Indoor and Outdoor Classification Using Light Measurements and Machine Learning

Abstract

This work presents an indoor/outdoor classification system which uses light measurements coupled with machine learning algorithms to predict whether the sensing system is indoors or outdoors. The system measures ultraviolet light, color temperature, luminosity, and red, green, blue, and clear components of light at one-minute intervals using an Arduino-based measurement system. Three machine learning algorithms – support vector machine, artificial neural network, and bagged tree – were trained and tested using experimentally collected sensor data from multiple locations, dates, and times. A comparison of these classifiers revealed superior classification performance of the bagged tree classifier (>99%) compared to the other two algorithms. Each of the presented classifiers offered high estimation performance (>96.9%) in all the considered cases with cross-validation. These results demonstrate the feasibility of using light measurements alone to predict indoor or outdoor condition, which has practical applications in psychology research.