Abstract
In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy.
Highlights
The advances in hardware and software technologies have led to the adoption of smartenvironments in many contexts of our daily lives
To evaluate the performance of the smartphone and the smartwatch in our indoor location system, the positions detected by the two monitoring devices during the performed tests were compared
We have evaluated the proposed approach by comparing our results in terms of accuracy score with two different approaches that have been used in the location prediction literature as baselines: nearest locations (NL) where the nearest neighbor to the user’s current location is selected [49], and a hidden Markov model which characterizes the movement patterns [50]
Summary
The advances in hardware and software technologies have led to the adoption of smartenvironments in many contexts of our daily lives. The indoor positioning issue is addressed by considering the performance obtained while using two different kinds of device to estimate the indoor position: a smartphone and a smartwatch With both devices, the Bluetooth Low Energy (BLE) technology was exploited to obtain indoor positioning information. The spread of the emerging BLE technology makes BT energy efficient, which is a key requirement in many indoor applications This efficiency allows for higher measuring rates when determining a user’s location and for longer battery life. For these reasons, BLE is considered as one of the most suitable positioning technologies for indoor positioning currently. A more extensive analysis of the state-of-the-art can be found in the following reviews: [31,32]
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