Indoor positioning techniques, owing to received signal strength indicator (RSSI)-based sensors, can provide useful trajectory-based services. These services include user movement analytics, next-to-visit recommendation, and hotspot detection. However, the value of RSSI is often disturbed due to obstacles in indoor environment, such as doors, walls, and furnitures. Therefore, many indoor positioning techniques still extract an invalid trajectory from the disturbed RSSI. An invalid trajectory contains distant or impossible consecutive positions within a short time, which is unlikely in a real-world scenario. In this study, we enhanced indoor positioning techniques with movement constraints on BLE (Bluetooth Low Energy) RSSI data to prevent an invalid semantic indoor trajectory. The movement constraints ensure that a predicted semantic position cannot be far apart from the previous position. Furthermore, we can extend any indoor positioning technique using these movement constraints. We conducted comprehensive experimental studies on real BLE RSSI datasets from various indoor environment scenarios. The experimental results demonstrated that the proposed approach effectively extracts valid indoor semantic trajectories from the RSSI data.
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