Improving Displacement Measurement for Evaluating Longitudinal Road Profiles

2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper introduces a half-wavelength peak matching (HWPM) model, which improves the accuracy of vehicle based longitudinal road profilers used in evaluating road unevenness and mega-textures. In this application, the HWPM model is designed for profilers which utilize a laser displacement sensor with an accelerometer for detecting surface irregularities. The process of converting acceleration to displacement by double integration (which is used in most rofilers) is error-prone, and although there are techniques to minimize the effect of this error, this paper proposes a novel approach for improving the generated road profile results. The technique amends the vertical displacement derived from the accelerometer samples, by using data from the laser displacement sensor as a reference. The vehicle based profiler developed for this experiment (which uses the HWPM model) shows a huge improvement in detected longitudinal irregularities when compared with pre-processed results, and uses a 3-m rolling straight edge as a benchmark.Peer reviewe

Human activity detection based on mobile devices

Aquesta tesi se centra en la detecció d'activitat humana a partir de dispositius mòbils i portàtils. Escollim Hexiwear com el nostre dispositiu portàtil per recollir les dades de l'activitat humana diària, com ara l'acceleració de tres eixos, l'orientació de tres eixos, la velocitat angular i la posició de tres eixos. Aquest projecte consisteix en el desenvolupament d'una aplicació per a telèfon intel·ligent per a l'usuari en l'anàlisi de dades, la visualització de dades i la generació de resultats. L'objectiu és construir un prototip obert i modular que pugui servir d'exemple o plantilla per al desenvolupament d'altres projectes. L'aplicació està desenvolupada amb JAVA per Android Studio. L'aplicació permet a l'usuari connectar-se amb el dispositiu portàtil i reconèixer la seva activitat diària. Per a l'algorisme de classificació de l'activitat diària, hem utilitzat dos mètodes diferents, el primer és mitjançant l'establiment de diferents llindars, el segon és mitjançant l'aprenentatge automàtic. L'aplicació es va provar i els resultats van ser satisfactoris, ja que l'aplicació generada va funcionar correctament. Malgrat les òbvies limitacions, la feina feta és un punt de partida per a desenvolupaments futurs。Esta tesis se centra en la detección de actividad humana basada en dispositivos móviles y portátiles. Elegimos Hexiwear como nuestro dispositivo portátil para recopilar los datos de la actividad humana diaria, como la aceleración de tres ejes, la orientación de tres ejes, la velocidad angular de tres ejes y la posición. Este proyecto implica la creación de una aplicación de teléfono para usuarios de análisis de datos, visualización de datos y generación de resultados. El objetivo es construir un prototipo abierto y modular que pueda servir como ejemplo o plantilla para el desarrollo de otros proyectos. La aplicación está desarrollada usando JAVA por Android Studio. La aplicación permite al usuario conectarse con el dispositivo portátil y reconocer su actividad diaria. Para el algoritmo de clasificación de la actividad diaria, usamos dos métodos diferentes, el primero es establecer umbrales diferentes, el segundo es usar el aprendizaje automático. La aplicación fue probada y los resultados fueron satisfactorios, ya que la aplicación generada funcionó correctamente. A pesar de las limitaciones evidentes, el trabajo realizado es un punto de partida para futuros desarrollos.  This thesis focuses on human activity detection based on mobile and wearable devices. We choose Hexiwear as our wearable device to collect the human daily activity data, like tri-axis acceleration, tri-axis orientation, tri-axis angular velocity and position. This project consists in the development of a smartphone application for the user in data analysis， data visualization and generates results. The objective is to build an open and modular prototype that can serve as an example or template for the development of other projects. The application is developed using JAVA by Android Studio. The application allows the user to connect with the wearable device, and recognize their daily activity. For the daily activity classify algorithm, we used two different methods, the first one is by set different thresholds, the second is by using the machine learning. The application was tested and the results were satisfactory, as the generated application worked properly. Despite the obvious limitations, the work done is a starting point for future developments

Enhanced Indoor Localization System based on Inertial Navigation

An algorithm for indoor localization of pedestrians using an improved Inertial Navigation system is presented for smartphone based applications. When using standard inertial navigation algorithm, errors in sensors due to random noise and bias result in a large drift from the actual location with time. Novel corrections are introduced for the basic system to increase the accuracy by counteracting the accumulation of this drift error, which are applied using a Kalman filter framework. A generalized velocity model was applied to correct the walking velocity and the accuracy of the algorithm was investigated with three different velocity models which were derived from the actual velocity measured at the hip of walking person. Spatial constraints based on knowledge of indoor environment were applied to correct the walking direction. Analysis of absolute heading corrections from magnetic direction was performed . Results show that the proposed method with Gaussian velocity model achieves competitive accuracy with a 30\% less variance over Step and Heading approach proving the accuracy and robustness of proposed method. We also investigated the frequency of applying corrections and found that a 4\% corrections per step is required for improved accuracy. The proposed method is applicable in indoor localization and tracking applications based on smart phone where traditional approaches such as GNSS suffers from many issues

An inertial motion capture framework for constructing body sensor networks

Motion capture is the process of measuring and subsequently reconstructing the movement of an animated object or being in virtual space. Virtual reconstructions of human motion play an important role in numerous application areas such as animation, medical science, ergonomics, etc. While optical motion capture systems are the industry standard, inertial body sensor networks are becoming viable alternatives due to portability, practicality and cost. This thesis presents an innovative inertial motion capture framework for constructing body sensor networks through software environments, smartphones and web technologies. The first component of the framework is a unique inertial motion capture software environment aimed at providing an improved experimentation environment, accompanied by programming scaffolding and a driver development kit, for users interested in studying or engineering body sensor networks. The software environment provides a bespoke 3D engine for kinematic motion visualisations and a set of tools for hardware integration. The software environment is used to develop the hardware behind a prototype motion capture suit focused on low-power consumption and hardware-centricity. Additional inertial measurement units, which are available commercially, are also integrated to demonstrate the functionality the software environment while providing the framework with additional sources for motion data. The smartphone is the most ubiquitous computing technology and its worldwide uptake has prompted many advances in wearable inertial sensing technologies. Smartphones contain gyroscopes, accelerometers and magnetometers, a combination of sensors that is commonly found in inertial measurement units. This thesis presents a mobile application that investigates whether the smartphone is capable of inertial motion capture by constructing a novel omnidirectional body sensor network. This thesis proposes a novel use for web technologies through the development of the Motion Cloud, a repository and gateway for inertial data. Web technologies have the potential to replace motion capture file formats with online repositories and to set a new standard for how motion data is stored. From a single inertial measurement unit to a more complex body sensor network, the proposed architecture is extendable and facilitates the integration of any inertial hardware configuration. The Motion Cloud’s data can be accessed through an application-programming interface or through a web portal that provides users with the functionality for visualising and exporting the motion data

Security of GPS/INS based On-road Location Tracking Systems

Location information is critical to a wide-variety of navigation and tracking applications. Today, GPS is the de-facto outdoor localization system but has been shown to be vulnerable to signal spoofing attacks. Inertial Navigation Systems (INS) are emerging as a popular complementary system, especially in road transportation systems as they enable improved navigation and tracking as well as offer resilience to wireless signals spoofing, and jamming attacks. In this paper, we evaluate the security guarantees of INS-aided GPS tracking and navigation for road transportation systems. We consider an adversary required to travel from a source location to a destination, and monitored by a INS-aided GPS system. The goal of the adversary is to travel to alternate locations without being detected. We developed and evaluated algorithms that achieve such goal, providing the adversary significant latitude. Our algorithms build a graph model for a given road network and enable us to derive potential destinations an attacker can reach without raising alarms even with the INS-aided GPS tracking and navigation system. The algorithms render the gyroscope and accelerometer sensors useless as they generate road trajectories indistinguishable from plausible paths (both in terms of turn angles and roads curvature). We also designed, built, and demonstrated that the magnetometer can be actively spoofed using a combination of carefully controlled coils. We implemented and evaluated the impact of the attack using both real-world and simulated driving traces in more than 10 cities located around the world. Our evaluations show that it is possible for an attacker to reach destinations that are as far as 30 km away from the true destination without being detected. We also show that it is possible for the adversary to reach almost 60-80% of possible points within the target region in some cities

Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected

An Indoor and Outdoor Navigation System for Visually Impaired People

In this paper, we present a system that allows visually impaired people to autonomously navigate in an unknown indoor and outdoor environment. The system, explicitly designed for low vision people, can be generalized to other users in an easy way. We assume that special landmarks are posed for helping the users in the localization of pre-defined paths. Our novel approach exploits the use of both the inertial sensors and the camera integrated into the smartphone as sensors. Such a navigation system can also provide direction estimates to the tracking system to the users. The success of out approach is proved both through experimental tests performed in controlled indoor environments and in real outdoor installations. A comparison with deep learning methods has been presented