*Article* **Landmark-Assisted Compensation of User's Body Shadowing on RSSI for Improved Indoor Localisation with Chest-Mounted Wearable Device**

**Md Abdulla Al Mamun, David Vera Anaya, Fan Wu and Mehmet Rasit Yuce \***

> Department of Electrical and Computer Systems Engineering, Clayton Campus, Monash University, Melbourne, VIC 3800, Australia; md.mamun1@monash.edu (M.A.A.M.); david.veraanaya@monash.edu (D.V.A.); fan.wu@monash.edu (F.W.)

**\*** Correspondence: mehmet.yuce@monash.edu

**Abstract:** Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance. The broad aim of this study is to mitigate the user's body shadowing effect on RSSI to improve localisation accuracy. Firstly, this study examines the effect of the user's body on RSSI. Then, an angle estimation method is proposed by leveraging the concept of landmark. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. After that, a compensation model is proposed to correct the RSSI. Finally, the unknown location is estimated using the nearest neighbour method. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm.

**Keywords:** indoor localisation; fingerprinting; landmark; wearable device; inertial measurement device; motion mode detection; body shadowing compensation; nearest neighbour
