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2. Behavior Modeling for a Beacon-Based Indoor Location System

Author

Aritz Bilbao-Jayo, Aitor Almeida, Ilaria Sergi, Teodoro Montanaro, Luca Fasano, Mikel Emaldi, and Luigi Patrono

Subjects
ambient assisted living, indoor positioning, internet of things, performance, smartphone, smartwatch, Chemical technology, TP1-1185
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.

Published
2021
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3. Behavior Modeling for a Beacon-Based Indoor Location System

Author

Aitor Almeida, Luca Fasano, Teodoro Montanaro, Mikel Emaldi, Aritz Bilbao-Jayo, Luigi Patrono, Ilaria Sergi, Bilbao-Jayo, A., Almeida, A., Sergi, I., Montanaro, T., Fasano, L., Emaldi, M., and Patrono, L.

Subjects
Indoor positioning, Computer science, Wearable device, Performance, Real-time computing, wearable device, 02 engineering and technology, TP1-1185, smartphone, Biochemistry, Article, smartwatch, Analytical Chemistry, Smartwatch, Position (vector), 0202 electrical engineering, electronic engineering, information engineering, Humans, Internet of thing, Electrical and Electronic Engineering, Instrumentation, ambient assisted living, business.industry, Chemical technology, Mean percentage error, indoor positioning, 020206 networking & telecommunications, Location systems, internet of things, Atomic and Molecular Physics, and Optics, Ambient assisted living, Location prediction, Systems architecture, 020201 artificial intelligence & image processing, Smartphone, Internet of Things, business, performance, Human
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.

Published
2021

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