PDR with a Foot-Mounted IMU and Ramp Detection

The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the person’s body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the person’s foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps

Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements

We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m

Design and Testing of a Multi-Sensor Pedestrian Location and Navigation Platform

Navigation and location technologies are continually advancing, allowing ever higher accuracies and operation under ever more challenging conditions. The development of such technologies requires the rapid evaluation of a large number of sensors and related utilization strategies. The integration of Global Navigation Satellite Systems (GNSSs) such as the Global Positioning System (GPS) with accelerometers, gyros, barometers, magnetometers and other sensors is allowing for novel applications, but is hindered by the difficulties to test and compare integrated solutions using multiple sensor sets. In order to achieve compatibility and flexibility in terms of multiple sensors, an advanced adaptable platform is required. This paper describes the design and testing of the NavCube, a multi-sensor navigation, location and timing platform. The system provides a research tool for pedestrian navigation, location and body motion analysis in an unobtrusive form factor that enables in situ data collections with minimal gait and posture impact. Testing and examples of applications of the NavCube are provided

Navigation Using Inertial Sensors

This tutorial provides an introduction to navigation using inertial sensors, explaining the underlying principles. Topics covered include accelerometer and gyro technology and their characteristics, strapdown inertial navigation, attitude determination, integration and alignment, zero updates, motion constraints, pedestrian dead reckoning using step detection, and fault detection

Localización de personas mediante sensores inerciales y su fusión con otras tecnologías

En el presente trabajo de Tesis se aborda el problema de la localización en entornos interiores utilizando sensores inerciales y su fusión con otras medidas para mejorar la estimación y limitar posibles derivas. Para ello, el algoritmo de localización propuesto se divide en tres partes: Una etapa de estimación del movimiento usando Pedestrian Dead Reckoning (PDR), un esquema de fusión de información que permite integrar múltiples tipos de medidas, aunque tengan relaciones no lineales, y la utilización de medidas externas (como la potencia de la señal de puntos de acceso WiFi, rangos a balizas UWB, GNSS, etc.) para limitar la deriva, proponiendo mejoras a cada una de ellas. Para mejorar el algoritmo PDR se propone la modificación del detector de apoyo utilizando un filtro de media sobre una ventana retardada. Para la estimación y corrección de errores se propone la utilización del filtro de Kalman Unscented (UKF) que simplifica los cálculos necesarios para la estimación y mejora la aproximación no lineal. Debido a la falta de información de la guiñada, una estimación PDR pura divergirá con el tiempo. Para aportar información de la orientación a la estimación se propone medir la rotación del campo magnético de acuerdo a las velocidades angulares observadas en el giróscopo. Se comprueba en varios experimentos que las mejoras evitan errores en la fase de apoyo, mejoran la estimación y disminuyen el efecto de la deriva de la orientación. Para fusionar la información del PDR con medidas externas se propone la utilización de dos esquemas: el primero, un filtro de límites que establece una distancia máxima entre 2 estimaciones, y el segundo un esquema basado en un filtro de partículas a dos etapas. El filtro de límites modifica la pdf (función de densidad de probabilidad) para evitar estimaciones muy distantes entre sí. Se comprueba que, al utilizar este método, se logra evitar la deriva un sistema PDR utilizando medidas UWB en otra parte del cuerpo. El esquema basado en un filtro de partículas utiliza la información de PDR para propagar las partículas y las medidas externas para actualizar los pesos de éstas. Se propone agregar el bias de la velocidad angular a los estados de las partículas para modelar el efecto del bias random walk (sesgo de camino aleatorio) del giróscopo. El filtro de partículas permite utilizar cualquier medida con una función de observación y una distribución de error, por lo que se estudian varios casos de estimaciones PDR fusionadas con medidas de sistemas WiFi, RFID, UWB y ZigBee. Los sistemas RF utilizados tienen un error de posicionamiento de 5 m (90 % de los casos) y la estimación PDR tiene un error creciente, pero al fusionar las estimaciones se logra un error inferior a 2 m (90 % de los casos). Por último, se utiliza el mapa del edificio para corregir las estimaciones y encauzarlas en las áreas caminables del edificio. Para ello se utiliza un método de eliminación de hipótesis (partículas) que atraviesan paredes. Este algoritmo se optimiza utilizando solo las paredes de la habitación en que se encuentra la partícula y se propone una sectorización de las operaciones para poder ser utilizada en MATLAB a tiempo real. Se demostró con señales reales que el algoritmo es capaz de auto localizar a una persona si el recorrido es no simétrico, obteniendo un nivel de error que dependerá del edificio, en nuestro caso cercano a 1 m. Si se utilizan medidas RF y el mapa, la estimación converge significativamente más rápido, y el nivel de error y el número de partículas necesarias (por ende, el tiempo de cómputo) disminuyen

Off-line evaluation of mobile-centric indoor positioning systems: the experiences from the 2017 IPIN competition

The development of indoor positioning solutions using smartphones is a growing activity with an enormous potential for everyday life and professional applications. The research activities on this topic concentrate on the development of new positioning solutions that are tested in specific environments under their own evaluation metrics. To explore the real positioning quality of smartphone-based solutions and their capabilities for seamlessly adapting to different scenarios, it is needed to find fair evaluation frameworks. The design of competitions using extensive pre-recorded datasets is a valid way to generate open data for comparing the different solutions created by research teams. In this paper, we discuss the details of the 2017 IPIN indoor localization competition, the different datasets created, the teams participating in the event, and the results they obtained. We compare these results with other competition-based approaches (Microsoft and Perf-loc) and on-line evaluation web sites. The lessons learned by organising these competitions and the benefits for the community are addressed along the paper. Our analysis paves the way for future developments on the standardization of evaluations and for creating a widely-adopted benchmark strategy for researchers and companies in the field.We would like to thank Topcon Corporation for sponsoring the competition track with an award for the winning team. We are also grateful to Francesco Potorti, Sangjoon Park, Hideo Makino, Nobuo Kawaguchi, Takeshi Kurata and Jesus Urena for their invaluable help in organizing and promoting the IPIN competition and conference. Many thanks to Raul Montoliu, Emilio Sansano, Marina Granel and Luis Alisandra for collecting the databases in the UJITI building. Parts of this work were carried out with the financial support received from projects and grants: REPNIN network (TEC2015-71426-REDT), LORIS (TIN2012-38080-C04-04), TARSIUS (TIN2015-71564-C4-2-R (MINECO/FEDER)), SmartLoc (CSIC-PIE Ref. 201450E011), "Metodologias avanzadas para el diseno, desarrollo, evaluacion e integracion de algoritmos de localizacion en interiores" (TIN2015-70202-P), GEO-C (Project ID: 642332, H2020-MSCA-ITN-2014-Marie Sklodowska-Curie Action: Innovative Training Networks), and financial support from the Ministry of Science and Technology, Taiwan (106-3114-E-007-005 and 105-2221-E-155-013-MY3). The HFTS team has been supported in the frame of the German Federal Ministry of Education and Research programme "FHprofUnt2013" under contract 03FH035PB3 (Project SPIRIT). The UMinho team has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT-Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013. G.M. Mendoza-Silva gratefully acknowledges funding from grant PREDOC/2016/55 by Universitat Jaume I.info:eu-repo/semantics/publishedVersio

Human gait modelling with step estimation and phase classification utilising a single thigh mounted IMU for vision impaired indoor navigation

This research is focused on human gait modelling for infrastructure free inertial navigation for vision impaired. A pedometer based on a single thigh mounted gyroscope, an efficient algorithm to estimate thigh flexion and extension, gait models for level walking, a model to estimate step length and a technique to detect gait phases based on a single thigh mounted Inertial Measurement Unit (IMU) were developed and confirmed higher accuracies

A Drift Eliminated Attitude & Position Estimation Algorithm In 3D

Inertial wearable sensors constitute a booming industry. They are self contained, low powered and highly miniaturized. They allow for remote or self monitoring of health-related parameters. When used to obtain 3-D position, velocity and orientation information, research has shown that it is possible to draw conclusion about issues such as fall risk, Parkinson disease and gait assessment. A key issues in extracting information from accelerometers and gyroscopes is the fusion of their noisy data to allow accurate assessment of the disease. This, so far, is an unsolved problem. Typically, a Kalman filter or its nonlinear, non-Gaussian version are implemented for estimating attitude â?? which in turn is critical for position estimation. However, sampling rates and large state vectors required make them unacceptable for the limited-capacity batteries of low-cost wearable sensors. The low-computation cost complementary filter has recently been re-emerging as the algorithm for attitude estimation. We employ it with a heuristic drift elimination method that is shown to remove, almost entirely, the drift caused by the gyroscope and hence generate a fairly accurate attitude and drift-eliminated position estimate. Inertial sensor data is obtained from the 10-axis SP-10C sensor, attached to a wearable insole that is inserted in the shoe. Data is obtained from walking in a structured indoor environment in Votey Hall

A Personal Dead Reckoning RFID Waypoint Updated System for Footstep Level Resolution Path Tracking to Enable Research Into Early Diagnosis of Alzheimer’s

The problem of footstep level resolution indoor path tracking and gait velocity lacks a good solution. The current solutions are either prohibitively expensive [4], extremely invasive to deploy [1], or too inaccurate to achieve the sub meter level resolution required to track foot paths. By combining existing pedestrian dead reckoning (PDR) using an inertial measurement unit (IMU) techniques with radio frequency identification (RFID) location updates, one can solve the problem of indoor path tracking. RFID tags placed in the building act as waypoints to correct the error that accumulates over time with PDR, thus yielding a system that can be used for arbitrary amounts of time with a resolution high enough to track individual footsteps

Assessment of Foot Signature Using Wearable Sensors for Clinical Gait Analysis and Real-Time Activity Recognition

Locomotion is one of the most important abilities of humans. Actually, gait locomotion provides mobility, and symbolizes freedom and independence. However, gait can be affected by several pathologies, due to aging, neurodegenerative disease, or trauma. The evaluation and treatment of mobility diseases thus requires clinical gait assessment, which is commonly done by using either qualitative analysis based on subjective observations and questionnaires, or expensive analysis established in complex motion laboratories settings. This thesis presents a new wearable system and algorithmic methods for gait assessment in natural conditions, addressing the limitations of existing methods. The proposed system provides quantitative assessment of gait performance through simple and precise outcome measures. The system includes wireless inertial sensors worn on the foot, that record data unobtrusively over long periods of time without interfering with subject's walking. Signal processing algorithms are presented for the automatic calibration and online virtual alignment of sensor signals, the detection of temporal parameters and gait phases, and the estimation of 3D foot kinematics during gait based on fusion methods and biomechanical assumptions. The resulting 3D foot trajectory during one gait cycle is defined as Foot Signature, by analogy with hand-written signature. Spatio-temporal parameters of interest in clinical assessment are derived from foot signature, including commonly parameters, such as stride velocity and gait cycle time, as well as original parameters describing inner-stance phases of gait, foot clearance, and turning. Algorithms based on expert and machine learning methods have been also adapted and implemented in real-time to provide input features to recognize locomotion activities including level walking, stairs, and ramp locomotion. Technical validation of the presented methods against gold standard systems was carried out using experimental protocols on subjects with normal and abnormal gait. Temporal aspects and quantitative estimation of foot-flat were evaluated against pressure insoles in subjects with ankle treatments during long-term gait. Furthermore, spatial parameters and foot clearance were compared in young and elderly persons to data obtained from an optical motion capture system during forward gait trials at various speeds. Finally, turning was evaluated in children with cerebral palsy and people with Parkinson's disease against optical motion capture data captured during timed up and go and figure-of-8 tests. Overall, the results demonstrated that the presently proposed system and methods were precise and accurate, and showed agreement with reference systems as well as with clinical evaluations of subjects' mobility disease using classical scores. Currently, no other methods based on wearable sensors have been validated with such precision to measure foot signature and subsequent parameters during unconstrained walking. Finally, we have used the proposed system in a large-scale clinical application involving more than 1800 subjects from age 7 to 77. This analysis provides reference data of common and original gait parameters, as well as their relationship with walking speed, and allows comparisons between different groups of subjects with normal and abnormal gait. Since the presented methods can be used with any foot-worn inertial sensors, or even combined with other systems, we believe our work to open the door to objective and quantitative routine gait evaluations in clinical settings for supporting diagnosis. Furthermore, the present studies have high potential for further research related to rehabilitation based on real-time devices, the investigation of new parameters' significance and their association with various mobility diseases, as well as for the evaluation of clinical interventions