Wearable-Based pedestrian localization through fusjon of inertial sensor measurements

Hoy en día existe una gran demanda de sistemas de navegación personales integrados en servicios como gestión de desastres para personal de rescate. También se demandan sistemas de navegación personales como guía en grandes superficies, por ejemplo, hospitales, aeropuertos o centros comerciales. En esta tesis doctoral los escenarios estudiados son interiores y urbanos. La navegación se realiza por medio de sensores inerciales y magnéticos, idóneos por su amplia difusión, tamaño y peso reducido y porque no necesitan infraestructura. Se llevarán a cabo investigaciones para mejorar los algoritmos de navegación ya existentes y cubrir determinados aspectos aún no resueltos. En primer lugar se ha llevado a cabo un extenso análisis sobre los beneficios de usar medidas magnéticas para compensar los errores sistemáticos de los sensores inerciales, así como su efecto en la estimación de la orientación. Para ello se han usado medidas de referencia con valores de error conocidos combinando diferentes distribuciones de campos magnéticos. Los resultados obtenidos quedan respaldados con medidas realizadas con sensores reales de medio coste. Se ha concluido que el uso de medidas magnéticas es beneficioso porque acota errores en la orientación. Sin embargo, los escenarios bajo estudio suelen presentar campos magnéticos perturbados, lo que provoca que el proceso de estimación de errores sea prohibitivamente largo. En esta tesis doctoral se proponen algoritmos alternativos para el cálculo del desplazamiento horizontal del usuario, que han sido comparados con respecto a los ya existentes, ofreciendo los propuestos un mejor rendimiento. Además se incluye un innovador algoritmo para calcular el desplazamiento vertical del usuario, haciendo por primera vez posible obtener trayectorias en 3D usando solamente sensores inerciales no colocados en el zapato. Por último se propone un novedoso algoritmo capaz de prevenir errores de posición provocados por errores de rumbo. El algoritmo está basado en puntos de referencia automáticamente detectados por medio de medidas inerciales. Los puntos de referencia elegidos para los escenarios cubiertos son escaleras y esquinas, que al revisitarse permiten calcular el error acumulado en la trayectoria. Este error es compensado consiguiendo así acotar el error de rumbo. Este algoritmo ha sido extensamente probado con medidas de referencia y medidas realizadas con sensores reales de medio coste. La compensación de este error se adapta a las características del sistema de navegación personal

Analysis and comparison of publicly available databases for urban mobility applications

The challenges of multimodal applications can be addressed with machine learning or artificial intelligence methods, for which a database with large amounts of good quality data and ground truth is crucial. Since generating and publishing such a database is a challenging endeavour, there are only a handful of them available for the community to be used. In this article, we want to analyze three of these databases and compare them. We assess these databases regarding the ground truth that they provide, e.g. labels of the means of transport, and assess how much unlabelled data they publish. We compare these databases regarding the number of hours of data, and how these hours are distributed among different means of transport and activities. Finally, we assess the data in each public database regarding crucial aspects such as the stability of the sampling frequency, the minimum sampling frequency required to observe certain means of transport or activities and, how much lost data these databases have. One of our main conclusions is that accurately labelling data and ensuring a stable sampling frequency are two of the biggest challenges to be addressed when generating a public database for urban mobility

Survey of Machine Learning Methods Applied to Urban Mobility

To increase the sustainability in urban mobility, it is necessary to optimally combine public and shared vehicles throughout a passenger's trip. In this work, we present a survey on urban mobility based on passengers' data and machine learning methods. We focus on four applications for urban mobility: public datasets, passenger localization, detection of the transport mode and pattern recognition and generation of mobility models. Public datasets lack data of multimodal trips and are in need of guidelines to facilitate the data collection and documentation processes. Passenger localization is predominantly done through fingerprinting in indoor environments; and fingerprinting relies on unsupervised learning to survey access points. The most common mean of transport detected is the bus, followed by walking and biking, while e-scooters are not included within the detected transport modes. The existing works focus on predicting the travel time of the passenger's trajectory and no machine learning method stands out to estimate the travel time. There is still a need for works that analyze how passengers make use of the urban infrastructure, which will support municipalities and transport mode operators in resource planning and service design

Smartphone-Based Localization for Passengers Commuting in Traffic Hubs

Passengers commute between different modes of transportation in traffic hubs, and the passenger localization is a key component for the well-funtioning of these spaces. The smartphone-based localization system presented in this work is based on the 3D step&heading approach, which is adapted depending on the position of the smartphone, i.e. held in the hand or in the front pocket of the trousers. We use the accelerometer, gyroscope and barometer embedded in the smartphone to detect the steps and the direction of movement of the passenger. To correct the accumulated error, we detect landmarks, particularly staircases and elevators. To test our localization algorithm, we have recorded real-world mobility data in out test station in Munich city center where we have ground truth points. We achieve a 3D position accuracy of 12 meters for a smartphone held in the hand and 10 meters when the phone is placed in the front pocket of the trousers

Advanced Pedestrian Positioning System to Smartphones and Smartwatches

In recent years, there has been an increasing interest in the development of pedestrian navigation systems for satellite-denied scenarios. The popularization of smartphones and smartwatches is an interesting opportunity for reducing the infrastructure cost of the positioning systems. Nowadays, smartphones include inertial sensors that can be used in pedestrian dead-reckoning (PDR) algorithms for the estimation of the user's position. Both smartphones and smartwatches include WiFi capabilities allowing the computation of the received signal strength (RSS). We develop a new method for the combination of RSS measurements from two different receivers using a Gaussian mixture model. We also analyze the implication of using a WiFi network designed for communication purposes in an indoor positioning system when the designer cannot control the network configuration. In this work, we design a hybrid positioning system that combines inertial measurements, from low-cost inertial sensors embedded in a smartphone, with RSS measurements through an extended Kalman filter. The system has been validated in a real scenario, and results show that our system improves the positioning accuracy of the PDR system thanks to the use of two WiFi receivers. The designed system obtains an accuracy up to 1.4 m in a scenario of 6000 m2

Modelling the impact of weather and context data on transport mode choices: A case study of GPS trajectories from Beijing

Over the years, researchers have been studying the effect of weather and context data on the transport mode choice. The majority of these works are based on survey data, however the accuracy of their findings relies on how respondents give accurate and honest answers. In this paper, the potential of using GPS trajectories as an alternative to travel surveys in studying the impact of weather and context data on transport mode choices is investigated in Beijing city. In the analysis, we apply both descriptive and statistical models such as the MNL and MNP models. Our findings indicate that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 C, the walking share increases by 27% and the bike share reduces by 21%, which is line with the results from several survey studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous as people are encouraged to use environmentally friendly travel mode choices such as the bike instead of the bus and car, which are known CO2 emitters. Moreover, due to a series of traffic restrictions introduced by the Beijing government during the 2008 summer Olympics, a decrease of 17.5% in the car share and an increase of 13% and 10% in the walking and bus shares, respectively are observed. These findings provide a scientific basis for effective transport regulation and planning purpose

Feasibility study: Magnetic-based passenger localization in train stations

Train stations are a key element of any transport network because they concentrate a large amount of passenger traffic on a daily basis. Passenger localization in train stations is though limited nowadays by the lack of satellite reception indoors and underground. A possible solution could be to use magnetometers, since they are embedded in today’s smartphones and are available in all urban environments. One of the most extended algorithms to perform magnetic localization is magnetic fingerprinting, however magnetic fingerprinting has not yet been proved viable in train stations. The aim of this article is to present a feasibility study of the possibility to apply magnetic fingerprinting in train stations to locate passengers. We have measured and analyzed the magnetic maps of different train stations in Munich, Germany. Our results show that, the functioning of the trains and the electric topology of the stations hinder the passenger localization using magnetic fingerprinting

Inertial pedestrian localization with soft constraints based on biomechanical models

To this day, inertial localization systems are failing to incorporate biomechanical constraints in the position tracking process. The goal of this paper is to define biomechanical constraints on the attitude estimates of an inertial localization system. To that end, a biomechanical study of the human leg is carried out in order to model the attitude as a set of Gaussian distributions. The latter impose soft constraints on the attitude that can be optimally integrated in a Kalman filter. Therefore, the constraints are integrated in the respective unscented Kalman filters (UKF) of two inertial navigation systems (INS): a thigh-mounted INS and a foot-mounted INS. The effect of these constraints in the position estimation is evaluated with a dataset of, approximately, 5h. The results show that the biomechanical constraints improve the coherence of the roll and pitch estimated by the thigh-mounted localization system. The impact of these constraints is reflected in the height error of the thigh-mounted system, which is improved by, approximately, 82%. The biomechanical constraints make the roll and pitch of an inertial localization system coherent with respect to human motion. Nevertheless, the constraints do not improve the heading estimated by the localization systems

Wearable-based pedestrian localization through fusion of inertial sensor measurements

In pedestrian localization, fusion techniques have been exploited to address the weaknesses of different types of localization systems. Inertial localization is a particular case where the community has attempted to combine inertial technology with other technologies like satellite-based navigation or WiFi. Less explored are the approaches that combine multiple inertial sensors mounted on the human body. We refer to these approaches as multi-inertial measurement unit (IMU) localization systems. In this work, we want to study multi-IMU localization systems for pedestrian localization. The overall research objective of this thesis is pedestrian localization by means of inertial sensor fusion. We aim to determine the benefits, in distance accuracy, heading accuracy and height accuracy, of combining two inertial sensors. They will be mounted on the pocket and the foot of the same leg, respectively. The reason for choosing these body locations is that they have characteristic features during walking. Moreover, the fact that we consider the same leg will allow us to observe possible correlations between the measurements of the foot IMU and the pocket IMU during the walk. Our first step is the design of the evaluation methodology, which is inspired by the methodologies implemented in indoor localization competitions. The evaluation methodology allows us to quantitatively assess the performance of the two inertial localization systems based on a pocket IMU and a foot IMU. With our study, we identify the strengths and weaknesses of each of these systems, which helps us proposing the multi-IMU localization systems that we develop later on. Our proposed multi-IMU localization systems focus on three main challenges of inertial localization. The first challenge is related to the need for calibration of the parameters of step length models, which are used in non-foot-mounted inertial localization systems. The second challenge is the height drift that affects foot-mounted inertial systems. Finally, the last challenge is the heading drift, which is a challenge common to all inertial localization systems. The proposed calibration method automatically estimates the parameters of a step length model of the pocket inertial navigation system (INS). We explain how the optimal value of the parameters of the step length model are estimated, given the step length from the foot INS and the pitch amplitude of the pocket INS. We make two proposals: the first one calibrates only one parameter of the step length model and the second one calibrates both parameters. The proposed calibration method has the advantage of the automatization. That is, there is no need for an external operator to manually calibrate the step length model of the pocket INS. In addition, the automatic calibration can be carried out in real time, which is not possible with either of the state of the art calibration methods. %One of the main disadvantages of inertial-based localization is the drift in the heading, i.e. the error accumulation in the heading estimation. Next, we address the challenges related to the height drift and the heading drift. We study two different alternatives: a loose coupling system and a tight coupling system. The proposed loose coupling system combines the outputs of the foot INS and the pocket INS. The goal is to obtain an improved position estimation with respect to the single-IMU localization systems. The first contribution of this system is the development of an algorithm to determine how accurate the heading of the foot INS is with respect to the heading of the pocket INS. The second contribution of the loose coupling system is that it leverages the complementary strengths of the single-IMU localization systems regarding the height estimation. The height error of the loose coupling system outperforms the height error of the foot INS and the pocket INS in 75% and 87%, respectively. The last multi-IMU localization system we develop is the tight coupling system, which combines the raw measurements of the pocket IMU and the foot IMU. We develop a biomechanical model of the human leg which is used to analyze the typical motion, in terms of the roll and pitch, of the leg limbs. We compare the typical motion of the leg limbs to the one derived from the inertial roll and inertial pitch. In this way, we are able to observe the errors of an inertial localization system without the need of a reference trajectory. In order to characterize the heading from the biomechanical point of view, we compare the heading of the thigh to the heading of the foot. The advantage of this analysis is that we can observe incoherences in the relative heading between the two body limbs, which is equivalent to the relative heading between the pocket IMU and the foot IMU, respectively. The findings of the biomechanical study are then integrated in a tight coupling system. A highlight is that we avoid the use of hard constraints by modelling the roll and pitch angles of the pocket IMU and foot IMU as Gaussian distributions. With our proposal, it is possible to keep the behaviour of these angles coherent with respect to human motion. Regarding the relative heading, we introduce a pseudo-measurement update on the slope of the relative heading. The tight coupling system reduces its heading error in 70% and 72% with respect to the heading error of the pocket INS and the foot INS, respectively. Moreover, the height error of the tight coupling system is reduced in 87% and 75% with respect to the height error of the pocket INS and the foot INS, respectively. The evaluation of the proposed methods leads to interesting observations. For instance, all the inertial localization systems have approximately the same average distance error. In contrast, the tight coupling system outperforms all the other inertial localization systems regarding the heading estimation. One of the highlights of the loose coupling system is the reduction of the height error. This decrease is the result of sampling the height of the foot INS only when the user is walking the stairs. We close our work by stating that multi-IMU localization systems are more accurate than single-IMU localization systems. This accuracy is reflected in a considerable improvement of the heading error and the height error. Therefore, we recommend the use of multiple IMUs placed on different parts of the body to improve the accuracy of an inertial localization system