## 2 Related Work  
Many researchers have brought forward optimizations to the LANDMARC approach in order to stabilize the results and obtain higher accuracy, robustness and depth of operation when implementing localization. These modifications include antenna and reader positioning, such as ANTspin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna, which introduces a rotary table in the experiment. The reader antenna is fixed on the rotary table to continuously collect dynamic data. When compared with static acquisition, there is more information for localization [2]. Excluding antenna-based optimization approaches, data processing and predictive modeling approaches have been used, such as 3 DLRA, which proposes a new three-dimensional localization method based on deep learning: combining RFID absolute location with relative location, analyzing the variation characteristics of the received signal strength (RSSI) and phase, further mining data characteristics by deep learning [3]. Some researchers have applied newer technologies such as ZigBee. ZigBee technology is the main technical index in the transmission, using data information as a carrier. It is widely used in human daily communication transmission. It is of great research value to apply the LANDMARC algorithm to ZigBee technology [4]. [PERSON] et al. presented a positioning system which uses two widely applied algorithms including Cell of Origin and the LANDMARC approach [5]. Other improvements include the Human Movement-based Relative Localization system, namely HMRL, using passive RFID to achieve accurate relative localization, and even some deep learning-inspired ones, such as advanced LANDMARC with the adaptive k-nearest neighbor algorithm [6; 7; 8; 9; 10; 11].  
All these improvements highlight how LANDMARC is the cornerstone of most indoor positioning systems (IPS). Using smart bracelets as wearable hardware as a part of the medical IPS is a more appropriate solution because there is a broad choice of this type of device on the market [12]. The VIRE algorithm improves the LANDMARC algorithm by inserting virtual tags evenly between reference tags [13]. Usually, in RFID positioning systems, we need more than one antenna to allow the positioning algorithm to achieve more accuracy. Moreover, some researchers have proposed single antenna-based positioning methods, which are more appropriate to be used in indoor environments. In addition, we can use data mining methods to improve the accuracy of indoor localization algorithms [14] and more sensors can be used for indoor positioning [15]. [PERSON] et al. presented the power-adaptation scheme, which enhanced the positioning accuracy in a single antenna RFID system [16]. In the practical test, as a result of the complex environmental factors and multipath effect, the RSSI value from the RFID system cannot directly reflect the distance between the tag and the antenna. Unlike other RFID-based localization systems, [PERSON] et al. proposed a novel RFID-based methodology which employs a single nonsteered stationary antenna and three passive tags mounted on each object to estimate the location and orientation of objects. This work provides an alternative and relatively cheap approach for remotely estimating the pose of an object [17].