# Location Based Indoor and Outdoor Lightweight Activity Recognition System
## 2 Related Work
The concept presented in [30] is to use existing Location-Based Services (LBS) infrastructure to track the user and achieve other system navigation goals. The information source is the freely available and up-to-date internet infrastructure. Initially, conventional methods are used to determine the user's location. The semantic labels are used and associated with the underlying location in order to relate the location with the web content. The semantic labels are obtained through reverse geocoding, which returns the names of local attractions, streets, cities, states, and countries, etc. The labels are then used to search for the associated detailed content in the web. The region data is kept in two places: a locally populated database and a web database. In the LBSN, community detection techniques are useful for social media algorithms to discover people with common interests and keep them tightly connected. Community detection can be used in machine learning to detect groups with similar properties and extract groups for various reasons. In [31] community detection is applied to explore the friendship circles among the users and within each friendship circle recommend POI for each user. In [32] a model of human mobility that reliably predicts the locations and dynamics of future human movement using cell phone location data, as well as data from two online location-based social networks is presented. With regard to the use of the meanings of the locations, in [33] an effective POI recommendation method for LBSNs based on collaborative filtering is described. The proposed method focuses on inferring user's check-in behaviors from user's location history to make a high-quality recommendation of POIs to a user based on the current location and opinions of similar users. The model is validated using two real-world datasets collected from Foursquare. To enable analysis of movement behavior, in [34; 35] authors present an approach to enrich trajectory data with semantic POI information and show how additional insights can be gained. In particular, a stepwise approach to generate insights from recorded vehicle movements is presented. Authors first processed a massive movement dataset and extracted frequent and dense destination areas. Then, they enriched the data with context information using Foursquare. The proposed model is evaluated with two case studies on a large electric scooter data set and tested on data with known ground truth. In [36] an automatic Semantic Location History (SLH) construction system is presented, named iManer. It consists of an improved Kalman filter-based approach to obtain accurate location and heading estimation from inertial sensor readings of smartphones. The collected users' location and heading histories are further mined to estimate Areas of Interaction (AoI). In order to integrate heterogeneous location aware systems into ubiquitous computing environment, in [37] authors present a novel semantic location-aware model based on ontology. The proposed model can share, reason, and manage context information and services in a spatial containment relationship and adjusting the usage policies of services dynamically. In [38] addresses location issues in ubiquitous mobile computing and presents a semantic location model that incorporates query and context semantics in a user-centered model to support context-aware cooperative mobile events. To recognize and use semantics, an event-reasoning mechanism based on semantic matching is established. A natural language query is defined, and a prototype is discussed as a scenario for evaluating the location model, resolving the perplexing problem of converting from linguistics to semantics, and then to pragmatics. The approach proposed in [39] extends that used in semantic GIServices to LBSs, while keeping context in mind. Contextual information is linked to information, action, and user spaces. It is composed of five elements: user, location, time, event, and object.