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 indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications.
Highlights
One of the most significant technology trends in the current decade is an enormous proliferation of smart mobile devices in daily life
The other subjects’ data may have to be added in the set, which can increase the possibility of abnormal interference issues that can be reduced by constructing a robust general model
Detecting the current user’s environment type and switching between them automatically is critical for many high-layer applications such as location-based services (LBS), seamless indoor/outdoor navigation and localization (SNAL), and healthcare monitoring
Summary
One of the most significant technology trends in the current decade is an enormous proliferation of smart mobile devices in daily life. As reported in [1], the number of current smartphone users is. The influence of smart mobile devices and increasing the ability to access the internet anywhere represent a high motivation for producing mobile applications based on location-based systems (LBSs). The LBS sector is receiving significant research attention from academia and industry. All mobile applications based on LBS have a common requirement: the current user positioning. Since mobile users can be in many places such as open sky outdoors, crowded avenues, indoor environments, etc., the generation of positioning systems has to perform well both indoors and outdoors. The Global Positioning System (GPS) is good enough for outdoor environment positioning and recently some accurate indoor positioning systems
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
