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- W2891454189 abstract "This paper describes the implementation of indoor localization technology using Bluetooth modules and beacons featuring Bluetooth Low Energy (BLE) by smart watches. We implemented a Hidden Markov Model (HMM)-based fingerprinting method using various data from recognized BLE signals. For fingerprinting, we obtained both a received signal strength indication and a signal observation frequency that were obtained from nearby beacons. The location was estimated from both a presurveilled profile and an exponential fit model. When using an exponential fit model with the signal observation frequency, we were able to achieve approximately 80% accuracy, even with little data. In addition, when using the HMM-based fingerprinting and a transition model based on the probability of users' movement, the accuracy of our location prediction increased by up to 15%." @default.
- W2891454189 created "2018-09-27" @default.
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- W2891454189 date "2018-08-01" @default.
- W2891454189 modified "2023-10-13" @default.
- W2891454189 title "HMM-Based Indoor Localization Using Smart Watches' BLE Signals" @default.
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- W2891454189 doi "https://doi.org/10.1109/ficloud.2018.00050" @default.
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