This work analyses the impact of the first multi-building multi-floor open available dataset for Wi-Fi fingerprinting, the UJIIndoorLoc dataset, 10 years after it was presented at the Fifth International Conference on Indoor Positioning and Indoor Navigation. First, we revisit the dataset description, providing some clarifications. Second, we have methodologically analyzed all the research works that mentioned or used the dataset. This analysis has brought more insights about the real impact of the dataset on this research field, but also how this dataset has been used in other contexts. Third, we present a second-order analysis, where the most popular (in terms of citations) works using it have been analyzed. The main objective of this work is to show the impact that public databases can have in the indoor positioning research field, and highlight good practices in providing open research datasets and using them.

Pedestrian Dead Reckoning (PDR) plays a crucial role in indoor and outdoor localization, particularly in environments where Global Navigation Satellite Systems (GNSS) signals are limited or unavailable. In this study, we compare various PDR algorithms using real-world data collected on both indoor and outdoor tracks. Our investigation focuses on identifying optimal methods for step-detection and step-length estimation, essential components of PDR systems. We find that simpler algorithms, such as the SciPy find peaks function and simple threshold techniques, perform better than more complex approaches, showing robustness across different individuals and environmental conditions. Additionally, we propose a new method for step detection and path reconstruction and we show the critical role of Orientation data (AHRS). We highlight the importance of proper preparation and calibration for reliable trajectory reconstruction. Our findings provide valuable insights for developing and implementing effective PDR systems in both indoor and outdoor settings.

Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.

This paper presents a set of unsupervised methods based on Bluetooth Low Energy (BLE) technology to obtain the position and transitions that users perform between the zones where they usually stay longer in their own homes. In particular, two different methods are studied to detect the user's position, the first based on proximity and the second based on fingerprinting techniques with self-training. The proposed methodology allows a very easy-to-use deployment by the user and with a very low economic cost. A set of experiments have been carried out to assess the performance of the proposed techniques presented in this paper, where a real user has captured data for several days at home. The results obtained show that the fingerprinting-based method is the most effective to accurately detect the user's position and the transitions performed between the different areas of interest at home.

Abstract Human activity recognition and deep learning are two fields that have attracted attention in recent years. The former due to its relevance in many application domains, such as ambient assisted living or health monitoring, and the latter for its recent and excellent performance achievements in different domains of application such as image and speech recognition. In this article, an extensive analysis among the most suited deep learning architectures for activity recognition is conducted to compare its performance in terms of accuracy, speed, and memory requirements. In particular, convolutional neural networks (CNN), long short‐term memory networks (LSTM), bidirectional LSTM (biLSTM), gated recurrent unit networks (GRU), and deep belief networks (DBN) have been tested on a total of 10 publicly available datasets, with different sensors, sets of activities, and sampling rates. All tests have been designed under a multimodal approach to take advantage of synchronized raw sensor' signals. Results show that CNNs are efficient at capturing local temporal dependencies of activity signals, as well as at identifying correlations among sensors. Their performance in activity classification is comparable with, and in most cases better than, the performance of recurrent models. Their faster response and lower memory footprint make them the architecture of choice for wearable and IoT devices.

The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smart-phones or smart-watches, can increase the performance of indoor positioning systems in AAL environments. Furthermore, this paper assesses complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies. Moreover, this can be done without compromising important aspects such as battery duration or unobtrusiveness.

One of the main problems of Indoor Positioning Systems (IPSs) based on WiFi fingerprinting is the radio map maintenance. It is well known that the creation of the radio map is a tedious and long-time task. In addition, if sometime after its creation, some access points are removed from the environment the accuracy of the IPS can be dramatically affected. This paper presents a new methodology to deal with this problem using imputation based techniques. An extensive set of experiments, comparing different imputation techniques, has been performed to demonstrate the benefits of using the proposed approach, showing that the proposed method is able to reduce the localization error in almost one meter with respect to a well-known solution.

This paper presents a study of indoor positioning in public zones of the Parc Taulí Hospital in Sabadell. It is a challenging scenario because: (1) it combines wide spaces with middle sized and narrow spaces; (2) it is a shielded zone where no signals are available, and therefore, no WiFi signal can be used for positioning; and (3) it is not possible to deploy beacons for positioning. The goal of this work is to test whether it is possible to get indoor positioning in a real and challenging scenario by using only the magnetic field. The positioning precision requires to locate the part of the hospital where the user is. The proposed solution defines “virtual corridors” to improve positioning in wide areas. To validate the work, magnetic field data have been recorded from the scenario, using different smartphones and by different persons. The obtained magnetic data curves have been compared by using dynamic time warping distance. Results show that it is possible to characterize every path with the magnetic field. The main contributions of the present paper are: (1) defining “virtual corridors” as a way to position using magnetic field in 2D spaces; and (2) showing that even in wide spaces, like the hall of a hospital, it is possible to find magnetic anomalies linked to positions.

This paper presents the IndoorLoc Platform, a public repository for comparing and evaluating indoor positioning algorithms and sharing datasets. The proposed web platform can be used to download datasets, learn how some well-known algorithms work, study the implementation of those algorithms, test the methods, and even upload indoor positioning estimations of the user's methods to check the accuracy when comparing against the results provided by other methods already included in a ranking, among other functionalities. This paper also presents a comparative study of the accuracy of two well-known fingerprinting-based indoor localization algorithms using the datasets included in the platform. This comparative study can be performed using the tools included in the platform.

This paper presents a novel methodology to obtain a measure of the difficulty of a scenario to obtain accurate localization results when testing an indoor positioning method. The variables used to measure indoor localization methods' accuracy are strongly dependent on the radio map used to test them. This makes it hard to compare different methods' performance. The proposed RMID indicator can be used to obtain a difficulty measure from a fingerprinting data set. This indicator will show if the precision obtained with a positioning method, using that data set, can be considered a reliable measurement of the method performance, by estimating the inherent difficulty of the radio map on which the accuracy has been reported.

The race to achieve smart cities is producing a continuous effort to adapt new developments and knowledge, for administrations and citizens. Information and Communications Technology are called on to be one of the key players to get these cities to use smart devices and sensors (Internet of Things) to know at every moment what is happening within the city, in order to make decisions that will improve the management of resources. The proliferation of these “smart things” is producing significant deployment of networks in the city context. Most of these devices are proprietary solutions, which do not offer free access to the data they provide. Therefore, this prevents the interoperability and compatibility of these solutions in the current smart city developments. This paper presents how to embed an open sensorized platform for both hardware and software in the context of a smart city, more specifically in a university campus. For this integration, GIScience comes into play, where it offers different open standards that allow full control over “smart things” as an agile and interoperable way to achieve this. To test our system, we have deployed a network of different sensorized platforms inside the university campus, in order to monitor environmental phenomena.

The main objective of this paper is to obtain an answer to the research question: Is it feasible to use a WiFi fingerprint-based indoor localization method for in-home monitoring? This question is highly relevant in fields such as Aging in Place or remote healthcare where continuous monitoring is essential. To answer this question, exhaustive experiments using expert systems and machine learning techniques have been performed in seven different real scenarios. The results showed success rate of 96% in estimating the location of a person inside his/her home in the best case, and an average of 89% in the seven studied scenarios. WiFi fingerprint-based location for in-home monitoring provides a precise location inside user's home, and it is robust enough to work even without an own WiFi access point, which in turn means a very affordable solution for in-home monitoring problems.

In this paper, A Bag of Words based method is presented to test a magnetic field based indoor positioning method. The Indoor positioning problem is solved as a pattern recognition problem, where each reference point is a different class. Feature vectors are constructed using a simplified bag of words methodology allowing user speed invariance. Several well known classifiers have been used to test the proposed method obtaining promising results when recognition the position of the user.

Selecting the appropriate parameters for an indoor positioning system may be a difficult task due to the large number of parameter combinations. It is more complex in realistic multi-building multi-floor environments, where severe wrong building and floor errors occur but they are not highlighted in the main evaluation metric. Moreover, a selected parameter configuration, that may seem appropriate in the system validation, may not have the expected behaviour in a real deployment. In order to address these issues, an ensemble of indoor positioning systems is introduced. A base estimator with 2.332 parameter combinations has been used. According to the results, this model simplifies the parameter selection and provides more robust systems.

The increasing demand for services and higher comfort levels inside buildings, together with the rise in time spent indoor, ensure an upward trend in indoor localization demand for the future. Evaluation of indoor localization systems is particularly challenging due to the complexity of such systems and to the variety of solutions adopted and services offered. EvAAL is an international competition aimed at evaluating and assessing indoor localization systems. The fifth edition of EvAAL promotes competitions on indoor localization in large environments. This paper describes its technical aspects, the competing systems and the results.

Indoor localization is a key topic for mobile computing. However, it is still very difficult for the mobile sensing community to compare state-of-art Indoor Positioning Systems due to the scarcity of publicly available databases. Magnetic field-based methods are becoming an important trend in this research field. Here, we present UJIIndoorLoc-Mag database, which can be used to compare magnetic field-based indoor localization methods. It consists of 270 continuous samples for training and 11 for testing. Each sample comprises a set of discrete captures taken along a corridor with a period of 0.1 seconds. In total, there are 40,159 discrete captures, where each one contains features obtained from the magnetometer, the accelerometer and the orientation sensor of the device. The accuracy results obtained using two baseline methods are also presented to show the suitability of the presented database for further comparisons.

A Smart City relies on six key factors: Smart Governance, Smart People, Smart Economy, Smart Environment, Smart Living and Smart Mobility. This paper focuses on Smart Mobility by improving one of its key components: positioning. We developed and deployed a novel indoor positioning system (IPS) that is combined with an outdoor positioning system to support seamless indoor and outdoor navigation and wayfinding. The positioning system is implemented as a service in our broader cartography-based smart university platform, called SmartUJI, which centralizes access to a diverse collection of campus information and provides basic and complex services for the Universitat Jaume I (Spain), which serves as surrogate of a small city. Using our IPS and based on the SmartUJI services, we developed, deployed and evaluated two end-user mobile applications: the SmartUJI APP that allows users to obtain map-based information about the different facilities of the campus, and the SmartUJI AR that allows users to interact with the campus through an augmented reality interface. Students, university staff and visitors who tested the applications reported their usefulness in locating university facilities and generally improving spatial orientation.

Although indoor localization is a key topic for mobile computing, it is still very difficult for the mobile sensing community to compare state-of-art localization algorithms due to the scarcity of databases. Thus, a multi-building and multi-floor localization database based on WLAN fingerprinting is presented in this work, being its public access granted for the research community. The here proposed database not only is the biggest database in the literature but it is also the first publicly available database. Among other comprehensively described features, full raw information taken by more than 20 users and by means of 25 devices is provided.

Animals are known to alter their behavior in response to changes in their environments. Therefore, automatic visual monitoring of animal behavior is currently of great interest because of its many applications. In this paper, a video-based system is proposed for analyzing the swimming patterns of fishes so that the presence of toxic in the water can be inferred. This problem is challenging, among other reasons, because how fishes react when swimming in contaminated water is neither really known nor well defined. A novel use of recurrence plots is proposed, and very compact and simple descriptors based on these recurrence representation are found to be highly discriminative between videos of fishes in clean and polluted water.

In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Mobile phones have been used as sensors to obtain location information from users’ real lives. Two levels of clustering are used to obtain places of interest. First, user location points are grouped using a time-based clustering technique which discovers stay points while dealing with missing location data. The second level performs clustering on the stay points to obtain stay regions. A grid-based clustering algorithm has been used for this purpose. To obtain more user location points, a client-server system has been installed on the mobile phones, which is able to obtain location information by integrating GPS, Wifi, GSM and accelerometer sensors, among others. An extensive set of experiments have been performed to show the benefits of using the proposed framework, using data from the real life of 8 users over 5 continuous months of natural phone usage.

This article presents a new framework for the motion segmentation and estimation task on sequences of two gray images without a priori information of the number of moving regions present in the sequence. The proposed algorithm uses temporal information, by using an accurate Generalized Least-Squares motion estimation process, and spatial information, by using an iterative region-growing algorithm that classifies regions of pixels into the different motion models present in the sequence. The initial regions of pixels are obtained from a given gray-level segmentation process. The performance of the algorithm is tested on synthetic and real images with multiple objects undergoing different types of motion. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 577–590, 2005.

This paper presents a motion estimation and segmentation algorithm based on multiple parametric model estimation that determines the a priori unknown number of motion models present in the data. The algorithm applies a quasi-simultaneous parametric model fitting method based on a general least square fitting. Some experiments are showed in order to demonstrate the results obtained using the proposed algorithm.