Mobile User Indoor-Outdoor Detection Through Physical Daily Activities.

ABSTRACT: An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis i

SUBMITTER: Esmaeili Kelishomi A 

PROVIDER: S-EPMC6387420 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature