# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 6 Systems for HAR
### Related Work  
Different experiences in which the combined study of HAR and IPS is addressed are reported. For example, [PERSON] et al. [72] developed a compensation mechanism, EPBCM, to improve location prediction by reducing drift and position errors caused by navigation algorithms. The model incorporates three algorithms: the HMM module for detecting accelerometric values for activity recognition and two others for localization based on PDR and BLE beacon. An unscented Kalman filter (UKF) is used in estimating position and orientation. Inertial sensors from a smartphone were used for data collection. The HMM model was applied to inertial data to recognize the activities carried out by the owner of the mobile device. Through a 3D navigation system, the movements of the subjects were traced. For the calculation of proximity between the smartphone and the fixed position of the beacon, the average weighted centroid localization algorithm (AWCLA) was applied. The EPBCM algorithm uses an estimation combiner to combine the position coordinates of the two position algorithms to obtain a position with less error. Localization based on the BLE beacon is applied as a compensation algorithm. The process is divided into several steps through which the data is filtered. On the sensory data, the orientation estimation is carried out with the help of the UKF. The Kalman filter is applied to the beacon signals for noise cancellation, attenuation of RSS measurements, and integration of raw measurements of the signals. The authors developed several performance analyses to ascertain the significance of the proposed mechanisms, such as position accuracy, comparison between performed and estimated tasks, and validity of orientation estimation based on AHRS and UHF. The results of HMM-based activity detection were verified with the K-mean clustering technique. The comparison between the real data of six different locations and those estimated with EPCM revealed an error ranging from 0.01 to 0.65 depending on the reference position. A comparison was also made between the positions estimated with the three algorithms (EPBCM, PDR, and BLE beacon) and the real ones. XEPCM's estimates were the most accurate. To evaluate the performance of the activity tracking system, the trajectories of the route taken from one floor to the upper floor using two flights of stairs were displayed. The analysis confirmed the effectiveness of the tool. Several pedestrian walking experiments were performed to further evaluate performance and compare the smartphone's position with the various landmarks along the way. The estimated trajectory differed slightly from the real one. [PERSON] et al. [78], as part of an innovative home project, introduced a system to recognize human activities using a network of sensors, including video cameras. The classic sensors of smart homes, dedicated to contact, temperature, motion, light, and any other device capable of connecting via a wireless communication medium, are distributed everywhere and can be used as constituent elements of behavior detection and recognition algorithms. The information provided by the cameras can be redundant but is helpful in case of malfunction of the sensors and is still useful in situations where several subjects perform actions simultaneously and the sensors cannot detect them on their own. The application aims to recognize daily living activities in an elderly care context. The authors proposed a qualitative model for recognizing human activities on the basis of an unsupervised learning approach using episodic extraction techniques. Due to the variety of activities carried out in sequence, the authors focused their experiments on recognizing the \"meal preparation\" activity due to the system's accuracy. The first results showed that the system could learn fragments of activities rather than entire activities. To measure the system's performance, they extracted 13 episodes learned from the training data occurring when meals are prepared.