TY - JOUR
T1 - Indoor and Outdoor Classification Using Light Measurements and Machine Learning
AU - Rhudy, Matthew B.
AU - Dolan, Scott K.
AU - Mello, Catherine
AU - Greenauer, Nathan
N1 - Publisher Copyright:
© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
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U2 - 10.1080/08839514.2021.2012001
DO - 10.1080/08839514.2021.2012001
M3 - Article
AN - SCOPUS:85121030785
SN - 0883-9514
VL - 36
JO - Applied Artificial Intelligence
JF - Applied Artificial Intelligence
IS - 1
M1 - 2012001
ER -