Este trabajo explora las características del posicionamiento por trilateración con balizas BLE capaces de emitir diferentes señales simultáneamente. Los sistemas más populares en este contexto están basados en redes inalámbricas como el Wi-Fi y el novedoso Bluetooth de Baja Energía (BLE), que permite una mayor flexibilidad al ser usado como base para esta clase de sistemas. Dentro de estaárea, los sistemas diseñados para smartphones son de especial importancia dado lo extendido de su uso. The proposed method estimates the gait speed with an average error of less than 10 cm/s, and is capable of providing continuous non-invasive monitoring of the gait speed of users while they conduct their usual life routines, without any additional requirements other than wearing a smartwatch or an activity band with inertial sensors and BLE capabilities.Įl campo del posicionamiento en interiores es una de lasáreas con mayor crecimiento en losúltimos años. In this work we present a method, based on Bluetooth Low Energy (BLE), capable of detecting the user's walking speed in a continuous and non-invasive way, providing a useful tool for the monitoring and early detection of this type of diseases. Specifically, several studies have shown a correlation between slower gait speed and serious cognitive diseases such as Alzheimer's disease. The onset of some diseases associated with aging, such as dementia or cognitive decline, has been associated with a number of factors that can be detected in advance, thus offering the possibility of an early intervention to delay their onset. In the near future, as a consequence of the increasing percentage of elder people with respect to the total population, developed countries will face significant stress on their healthcare systems. As a result, the proposed fingerprinting clustering method outperforms three of the most well-known clustering algorithms in terms of processing time at the operational phase of fingerprinting. This work proposes a new clustering method based on the maximum Received Signal Strength (RSS) values to join similar fingerprints. Many of the current studies have proposed a variety of solutions based on the modification of traditional clustering algorithms in order to provide a better distribution of samples and reduce the computational load. Thus, the incoming fingerprint will be compared with a specific number of samples grouped by, for instance similarity (clusters). The most common solution is to divide the data set into clusters. In order to minimize the time response, many solutions have been proposed along the time. However, this technique suffers from scalability problems when the radio map has a large number of reference fingerprints because this might increase the time response in the operational phase. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. Nowadays, several indoor positioning solutions support Wi-Fi and use this technology to estimate the user position. As a validation of the dataset, a baseline analysis for data visualization, data filtering and collaborative distance estimation applying a path-loss based on the Levenberg-Marquardt Least Squares Trilateration method are included. The database is composed of three subsets: one devoted to the calibration in an indoor scenario one for ranging and collaborative positioning under Non-Line-of-Sight conditions and one for ranging and collaborative positioning in real office conditions. This article presents a Bluetooth Low Energy (BLE) database, including Received-Signal-Strength (RSS) and Ground-Truth (GT) positions, for indoor positioning and ranging applications, using mobile devices as transmitters and receivers. Unfortunately, capturing the required measurements to support such systems is tedious and time-consuming, as it requires simultaneous measurements using multiple mobile devices, and no such database are available in literature. The demand to enhance distance estimation and location accuracy in a variety of Non-Line-of-Sight (NLOS) indoor environments has boosted investigation into infrastructure-less ranging and collaborative positioning approaches.
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