# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 6 Systems for HAR
### Related Work
The RSSI tag is the principal value used to determine the location of the target tags using the K-nearest neighbor (KNN) algorithm. Their solution is based on a low-cost system that uses a small number of passive reference tags and a single antenna reader to locate objects and mounts a mobile tag. Information about the tag is stored in a file containing a number, ID, collection time, and RSSI value of collected tags. The path loss is applied to construct an error map for all reference locations based on this data. Subsequently, this error map and RSSI values are used in the context of the KNN method to locate the object's coordinates. The reference tag grid based on the landmark model includes 28 reference positions. The target positions are distributed randomly in this tag grid. The RSSI value of tags is collected at a fixed antenna position. The process for the RSSI collection in the database is divided into two phases: a first phase in which the data of all the reference tags are collected, and the second phase which is dedicated to collecting the target position data. The measurements for each of the target positions as for the reference positions are repeated 10 times. The process is systematically repeated from the first to the last target position. The results obtained showed a localization error of about 32 cm. [PERSON] et al. [83] proposed an articulated platform based on the use of sensors on smartphones to identify the subjects' activities, as well as to identify the internal environment. The project does not require additional devices. They combined pedestrian dead reckoning (PDR), human activity recognition (HAR), and landmarks to acquire accurate indoor location information. The authors applied a hidden Markov model to deduce the user's initial position. From the detection of the activities of individuals, an indoor semantic landmark model was created to study the activities and trajectories. To estimate the subject's position, the PDR is initially applied, to which landmarks are later added to correct the position. With the phone's sensors, HAR is used to identify user activity. In addition, with HMM, the user's initial position is then estimated. The PDR-smartphone system, with the help of inertial and orientation sensors, allows for tracing the user's trajectory. The process includes step detection, step length estimation, direction estimation, and trajectory correction. As a supervised learning method to deduce user activities from sensory data, the KNN algorithm is applied, and, with the help of landmarks, the accuracy is then improved. As landmarks are used as the key points in a trajectory, a landmark list can denote a trajectory. There are three types of landmarks added to the list: stairs, turn, and doors. Adding semantic information to the landmark and the adjacent segment, we get a semantic description of a trajectory. Experiments conducted to fully evaluate the proposed approach not only showed high localization accuracy but also highlighted the effect of the landmark on location accuracy. The accuracy of the classification was greater than 99% in detecting stairs and walking activities. Lastly, the results on the localization error showed that the PDR errors increase with distance, and a high average localization accuracy (0.59) was achieved; they used the landmark to correct the cumulation errors. [PERSON] et al. [84] designed an original application with a model based on Wi-Fi fingerprints starting from the consideration that people's behaviors can influence the propagation of the Wi-Fi signal and introduce specific patterns in Wi-Fi signals, called Wi-Fi fingerprints, which can be further explored to identify human activities and locations. They proposed a novel deep learning framework for joint activity recognition and indoor localization using Wi-Fi channel state information (CSI) fingerprints.