Classification of Indoor-Outdoor Location using Blood Oxygen Saturation Signal

Abstract

Wearable technology, one of the most significant trends in the mobile computing evolution, has been changing our daily life. It has become increasingly popular in many different areas such as military, healthcare, entertainment, and education. In this paper, we aim to find out a person's indoor-outdoor location by oxygen saturation (SpO2) sensor. To this end, we build a new dataset consisting of twelve subjects between the ages of 20-65 and propose an ensemble learning based method for indoor-outdoor classification. We provide comparative tests with Naive Bayes (NB), k-nearest neighbor (kNN), and support vector machine (SVM) algorithms on the dataset and present empirical results regarding the SpO2 usage in different age groups. Our experimental results on real examples show that using RF gives best classification rates with an average accuracy of 69.33% for all test scenarios. Also, we see that, as the age increases, the oxygen saturation in the person's blood decreases.