MobiIO: Push the limit of indoor/outdoor detection through human's mobility traces

Presently existing lightweight indoor/outdoor detection schemes on phones acquire accuracy by sensing variations of ambient physical environmental properties with inherent sensors on mobile phones, with which, however, the detection scheme cannot work well in some ambient environments, where the variations are not very observable. This detection scheme is with very high dependency on light. The I/O detector does not work well in poor lighting or fast changing lighting settings, therefore the I/O detection is very much challenged at times like dawn, dusk, or night. The target of this paper is finding a pervasive detection scheme independent of physical environments. In this paper, we present MobiIO, an lightweight indoor and outdoor detection scheme based on analyses of human activities. By recording human indoor and outdoor motion activities with sensors, typical features of their activities are extracted. We compare assorted combinations or groupings of various properties with SVM classifier. We classify indoor/outdoor settings through classifiers like SVM, Bayes, decision trees, HMM and compare the effects of classification in between various classifying algorithms.