Improving Foot-Mounted Inertial Navigation Through Real-Time Motion Classification

We present a method to improve the accuracy of a foot-mounted, zero-velocity-aided inertial navigation system (INS) by varying estimator parameters based on a real-time classification of motion type. We train a support vector machine (SVM) classifier using inertial data recorded by a single foot-mounted sensor to differentiate between six motion types (walking, jogging, running, sprinting, crouch-walking, and ladder-climbing) and report mean test classification accuracy of over 90% on a dataset with five different subjects. From these motion types, we select two of the most common (walking and running), and describe a method to compute optimal zero-velocity detection parameters tailored to both a specific user and motion type by maximizing the detector F-score. By combining the motion classifier with a set of optimal detection parameters, we show how we can reduce INS position error during mixed walking and running motion. We evaluate our adaptive system on a total of 5.9 km of indoor pedestrian navigation performed by five different subjects moving along a 130 m path with surveyed ground truth markers.Comment: In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN'17), Sapporo, Japan, Sep. 18-21, 201

Master of Science

thesisComputing and data acquisition have become an integral part of everyday life. From reading emails on cell phones to kids playing with motion sensing game consoles, we are surrounded with sensors and mobile computing devices. As the availability of powerful computing devices increases, applications in previously limited environments become possible. Training devices in rehabilitation are becoming increasingly common and more mobile. Community based rehabilitative devices are emerging that embrace these mobile advances. To further the flexibility of devices used in rehabilitation, research has explored the use of smartphones as a means to process data and provide feedback to the user. In combination with sensor embedded insoles, smartphones provide a powerful tool for the clinician in gathering data and as a standalone training tool in rehabilitation. This thesis presents the continuing research of sensor based insoles, feedback systems and increasing the capabilities of the Adaptive Real-Time Instrumentation System for Tread Imbalance Correction, or ARTISTIC, with the introduction of ARTISTIC 2.0. To increase the capabilities of the ARTISTIC an Inertial Measurement Unit (IMU) was added, which gave the system the ability to quantify the motion of the gait cycle and, more specifically, determine stride length. The number of sensors in the insole was increased from two to ten, as well as placing the microprocessor and a vibratory motor in the insole. The transmission box weight was reduced by over 50 percent and the volume by over 60 percent. Stride length was validated against a motion capture system and found the average stride length to be within 2.7 ± 6.9 percent. To continue the improvement of the ARTISTIC 2.0, future work will include implementing real-time stride length feedback

Map matching and heuristic elimination of gyro drift for personal navigation systems in GPS-denied conditions

This paper introduces a method for the substantial reduction of heading errors in inertial navigation systems used under GPS-denied conditions. Presumably, the method is applicable for both vehicle-based and personal navigation systems, but experiments were performed only with a personal navigation system called 'personal dead reckoning' (PDR). In order to work under GPS-denied conditions, the PDR system uses a foot-mounted inertial measurement unit (IMU). However, gyro drift in this IMU can cause large heading errors after just a few minutes of walking. To reduce these errors, the map-matched heuristic drift elimination (MAPHDE) method was developed, which estimates gyro drift errors by comparing IMU-derived heading to the direction of the nearest street segment in a database of street maps. A heuristic component in this method provides tolerance to short deviations from walking along the street, such as when crossing streets or intersections. MAPHDE keeps heading errors almost at zero, and, as a result, position errors are dramatically reduced. In this paper, MAPHDE was used in a variety of outdoor walks, without any use of GPS. This paper explains the MAPHDE method in detail and presents experimental results.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90785/1/0957-0233_22_2_025205.pd

Map matching by using inertial sensors: literature review

This literature review aims to clarify what is known about map matching by using inertial sensors and what are the requirements for map matching, inertial sensors, placement and possible complementary position technology. The target is to develop a wearable location system that can position itself within a complex construction environment automatically with the aid of an accurate building model. The wearable location system should work on a tablet computer which is running an augmented reality (AR) solution and is capable of track and visualize 3D-CAD models in real environment. The wearable location system is needed to support the system in initialization of the accurate camera pose calculation and automatically finding the right location in the 3D-CAD model. One type of sensor which does seem applicable to people tracking is inertial measurement unit (IMU). The IMU sensors in aerospace applications, based on laser based gyroscopes, are big but provide a very accurate position estimation with a limited drift. Small and light units such as those based on Micro-Electro-Mechanical (MEMS) sensors are becoming very popular, but they have a significant bias and therefore suffer from large drifts and require method for calibration like map matching. The system requires very little fixed infrastructure, the monetary cost is proportional to the number of users, rather than to the coverage area as is the case for traditional absolute indoor location systems.Siirretty Doriast

Signal analysis tool to investigate walking abnormalities

Abstract. This thesis presents a signal analysis tool, which has been designed to investigate walking abnormalities which are related to foot rolling movements during walking; interaction of foot with ground which is called stance phase. They would cause a wide range of severe anatomical damages such as ankle, leg, heel and back pain in the long-term. Comparing to the conventional data acquisition setups of biomechanical researches, inertial measurement sensors (IMU), which are being used widely as an appropriate alternative setup recently, facilitate monitoring human movement for a long-term period out of laboratory. This justifies the growing trend of improving the IMU-based algorithms which are designed for events detection, position calculation, and rotation estimation. Therefore, a set of 4 IMUs, placed on shank and foot of both legs, has been used for data collection. In data processing stage, two novel algorithms have been developed and implemented as the backbone of the designed software aiming to detect and integrate stance phases. The first algorithm was developed to detect stance phases in gait cycle data. Even though the detection of events in gait cycles has been the topic of a majority of biomechanical researches, stance phase as the interval between two consecutive events has not been studied sufficiently. The second algorithm, sensor alignment, generates a rotation matrix which is used to align IMU sensors placed on the same foot and shank. This alignment of the two sensors enables us to add or subtract the data point-wisely to make a more meaningful interpretation of the data regarding thought-out walking abnormalities during phase stances. The visualized results of the thesis can be considered as an early stage of a more comprehensive research which might lead to quantitative results corresponding to different walking abnormalities

Adaptive Indoor Pedestrian Tracking Using Foot-Mounted Miniature Inertial Sensor

This dissertation introduces a positioning system for measuring and tracking the momentary location of a pedestrian, regardless of the environmental variations. This report proposed a 6-DOF (degrees of freedom) foot-mounted miniature inertial sensor for indoor localization which has been tested with simulated and real-world data. To estimate the orientation, velocity and position of a pedestrian we describe and implement a Kalman filter (KF) based framework, a zero-velocity updates (ZUPTs) methodology, as well as, a zero-velocity (ZV) detection algorithm. The novel approach presented in this dissertation uses the interactive multiple model (IMM) filter in order to determine the exact state of pedestrian with changing dynamics. This work evaluates the performance of the proposed method in two different ways: At first a vehicle traveling in a straight line is simulated using commonly used kinematic motion models in the area of tracking (constant velocity (CV), constant acceleration (CA) and coordinated turn (CT) models) which demonstrates accurate state estimation of targets with changing dynamics is achieved through the use of multiple model filter models. We conclude by proposing an interactive multiple model estimator based adaptive indoor pedestrian tracking system for handling dynamic motion which can incorporate different motion types (walking, running, sprinting and ladder climbing) whose threshold is determined individually and IMM adjusts itself adaptively to correct the change in motion models. Results indicate that the overall IMM performance will at all times be similar to the best individual filter model within the IMM

Design, Development and Implementation of the Position Estimator Algorithm for Harmonic Motion on the XY Flexural Mechanism for High Precision Positioning

This article presents a novel concept of the position estimator algorithm for voice coil actuators used in precision scanning applications. Here, a voice coil motor was used as an actuator and a sensor using the position estimator algorithm, which was derived from an electro-mechanical model of a voice coil motor. According to the proposed algorithm, the position of coil relative to the fixed magnet position depends on the current drawn, voltage across coil and motor constant of the voice coil motor. This eliminates the use of a sensor that is an integral part of all feedback control systems. Proposed position estimator was experimentally validated for the voice coil actuator in integration with electro-mechanical modeling of the flexural mechanism. The experimental setup consisted of the flexural mechanism, voice coil actuator, current and voltage monitoring circuitry and its interfacing with PC via a dSPACE DS1104 R&D microcontroller board. Theoretical and experimental results revealed successful implementation of the proposed novel algorithm in the feedback control system with positioning resolution of less than ±5 microns at the scanning speed of more than 5 mm/s. Further, proportional-integral-derivative (PID) control strategy was implemented along with developed algorithm to minimize the error. The position determined by the position estimator algorithm has an accuracy of 99.4% for single direction motion with the experimentally observed position at those instantaneous states

Master of Science

thesisGait analysis is an important tool for diagnosing a wide variety of disorders, with its increasingly accepted benefits culminating in the widespread adoption of motion analysis laboratories. A modern analysis laboratory consists of a multicamera marker tracking system for 3D reconstruction of kinematics and multiple high-fidelity load transducers to determine ground reaction force and enable inverse-dynamics for biomechanics. There is a need for an alternative motion analysis system which does not require a fixed laboratory setting and is lower in cost; freeing the motion capture from the laboratory and reducing the technology costs would enable long-term, home-based, natural monitoring of subjects. This thesis describes two contributions to the end goal of an inexpensive, mobile, insole-based motion analysis laboratory. First is the application of an inertialmeasurement-unit calibration routine and zero-velocity-update algorithm to improve position and orientation tracking. Second is the development, from basic sensor to prototype, of an insole capable of measuring 3 degree-of-freedom ground reaction force. These contributions represent a proof-of-concept that quantitative gait analysis, complete with dynamics, is possible with an insole-based system

적분 및 매개변수 기법 융합을 이용한 스마트폰 다중 동작에서 보행 항법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 기계항공공학부, 2020. 8. 박찬국.In this dissertation, an IA-PA fusion-based PDR (Pedestrian Dead Reckoning) using low-cost inertial sensors is proposed to improve the indoor position estimation. Specifically, an IA (Integration Approach)-based PDR algorithm combined with measurements from PA (Parametric Approach) is constructed so that the algorithm is operated even in various poses that occur when a pedestrian moves with a smartphone indoors. In addition, I propose an algorithm that estimates the device attitude robustly in a disturbing situation by an ellipsoidal method. In addition, by using the machine learning-based pose recognition, it is possible to improve the position estimation performance by varying the measurement update according to the poses. First, I propose an adaptive attitude estimation based on ellipsoid technique to accurately estimate the direction of movement of a smartphone device. The AHRS (Attitude and Heading Reference System) uses an accelerometer and a magnetometer as measurements to calculate the attitude based on the gyro and to compensate for drift caused by gyro sensor errors. In general, the attitude estimation performance is poor in acceleration and geomagnetic disturbance situations, but in order to effectively improve the estimation performance, this dissertation proposes an ellipsoid-based adaptive attitude estimation technique. When a measurement disturbance comes in, it is possible to update the measurement more accurately than the adaptive estimation technique without considering the direction by adjusting the measurement covariance with the ellipsoid method considering the direction of the disturbance. In particular, when the disturbance only comes in one axis, the proposed algorithm can use the measurement partly by updating the other two axes considering the direction. The proposed algorithm shows its effectiveness in attitude estimation under disturbances through the rate table and motion capture equipment. Next, I propose a PDR algorithm that integrates IA and PA that can be operated in various poses. When moving indoors using a smartphone, there are many degrees of freedom, so various poses such as making a phone call, texting, and putting a pants pocket are possible. In the existing smartphone-based positioning algorithms, the position is estimated based on the PA, which can be used only when the pedestrian's walking direction and the device's direction coincide, and if it does not, the position error due to the mismatch in angle is large. In order to solve this problem, this dissertation proposes an algorithm that constructs state variables based on the IA and uses the position vector from the PA as a measurement. If the walking direction and the device heading do not match based on the pose recognized through machine learning technique, the position is updated in consideration of the direction calculated using PCA (Principal Component Analysis) and the step length obtained through the PA. It can be operated robustly even in various poses that occur. Through experiments considering various operating conditions and paths, it is confirmed that the proposed method stably estimates the position and improves performance even in various indoor environments.본 논문에서는 저가형 관성센서를 이용한 보행항법시스템 (PDR: Pedestrian Dead Reckoning)의 성능 향상 알고리즘을 제안한다. 구체적으로 보행자가 실내에서 스마트폰을 들고 이동할 때 발생하는 다양한 동작 상황에서도 운용될 수 있도록, 매개변수 기반 측정치를 사용하는 적분 기반의 보행자 항법 알고리즘을 구성한다. 또한 타원체 기반 자세 추정 알고리즘을 구성하여 외란 상황에서도 강인하게 자세를 추정하는 알고리즘을 제안한다. 추가적으로 기계학습 기반의 동작 인식 정보를 이용, 동작에 따른 측정치 업데이트를 달리함으로써 위치 추정 성능을 향상시킨다. 먼저 스마트폰 기기의 이동 방향을 정확하게 추정하기 위해 타원체 기법 기반 적응 자세 추정을 제안한다. 자세 추정 기법 (AHRS: Attitude and Heading Reference System)은 자이로를 기반으로 자세를 계산하고 자이로 센서오차에 의해 발생하는 드리프트를 보정하기 위해 측정치로 가속도계와 지자계를 사용한다. 일반적으로 가속 및 지자계 외란 상황에서는 자세 추정 성능이 떨어지는데, 추정 성능을 효과적으로 향상시키기 위해 본 논문에서는 타원체 기반 적응 자세 추정 기법을 제안한다. 측정치 외란이 들어오는 경우, 외란의 방향을 고려하여 타원체 기법으로 측정치 공분산을 조정해줌으로써 방향을 고려하지 않은 적응 추정 기법보다 정확하게 측정치 업데이트를 할 수 있다. 특히 외란이 한 축으로만 들어오는 경우, 제안한 알고리즘은 방향을 고려해 나머지 두 축에 대해서는 업데이트 해줌으로써 측정치를 부분적으로 사용할 수 있다. 레이트 테이블, 모션 캡쳐 장비를 통해 제안한 알고리즘의 자세 성능이 향상됨을 확인하였다. 다음으로 다양한 동작에서도 운용 가능한 적분 및 매개변수 기법을 융합하는 보행항법 알고리즘을 제안한다. 스마트폰을 이용해 실내를 이동할 때에는 자유도가 크기 때문에 전화 걸기, 문자, 바지 주머니 넣기 등 다양한 동작이 발생 가능하다. 기존의 스마트폰 기반 보행 항법에서는 매개변수 기법을 기반으로 위치를 추정하는데, 이는 보행자의 진행 방향과 기기의 방향이 일치하는 경우에만 사용 가능하며 일치하지 않는 경우 자세 오차로 인한 위치 오차가 크게 발생한다. 이러한 문제를 해결하기 위해 본 논문에서는 적분 기반 기법을 기반으로 상태변수를 구성하고 매개변수 기법을 통해 나오는 위치 벡터를 측정치로 사용하는 알고리즘을 제안한다. 만약 기계학습을 통해 인식한 동작을 바탕으로 진행 방향과 기기 방향이 일치하지 않는 경우, 주성분 분석을 통해 계산한 진행방향을 이용해 진행 방향을, 매개변수 기법을 통해 얻은 보폭으로 거리를 업데이트해 줌으로써 보행 중 발생하는 여러 동작에서도 강인하게 운용할 수 있다. 다양한 동작 상황 및 경로를 고려한 실험을 통해 위에서 제안한 방법이 다양한 실내 환경에서도 안정적으로 위치를 추정하고 성능이 향상됨을 확인하였다.Chapter 1 Introduction 1 1.1 Motivation and Background 1 1.2 Objectives and Contribution 5 1.3 Organization of the Dissertation 6 Chapter 2 Pedestrian Dead Reckoning System 8 2.1 Overview of Pedestrian Dead Reckoning 8 2.2 Parametric Approach 9 2.2.1 Step detection algorithm 11 2.2.2 Step length estimation algorithm 13 2.2.3 Heading estimation 14 2.3 Integration Approach 15 2.3.1 Extended Kalman filter 16 2.3.2 INS-EKF-ZUPT 19 2.4 Activity Recognition using Machine Learning 21 2.4.1 Challenges in HAR 21 2.4.2 Activity recognition chain 22 Chapter 3 Attitude Estimation in Smartphone 26 3.1 Adaptive Attitude Estimation in Smartphone 26 3.1.1 Indirect Kalman filter-based attitude estimation 26 3.1.2 Conventional attitude estimation algorithms 29 3.1.3 Adaptive attitude estimation using ellipsoidal methods 30 3.2 Experimental Results 36 3.2.1 Simulation 36 3.2.2 Rate table experiment 44 3.2.3 Handheld rotation experiment 46 3.2.4 Magnetic disturbance experiment 49 3.3 Summary 53 Chapter 4 Pedestrian Dead Reckoning in Multiple Poses of a Smartphone 54 4.1 System Overview 55 4.2 Machine Learning-based Pose Classification 56 4.2.1 Training dataset 57 4.2.2 Feature extraction and selection 58 4.2.3 Pose classification result using supervised learning in PDR 62 4.3 Fusion of the Integration and Parametric Approaches in PDR 65 4.3.1 System model 67 4.3.2 Measurement model 67 4.3.3 Mode selection 74 4.3.4 Observability analysis 76 4.4 Experimental Results 82 4.4.1 AHRS results 82 4.4.2 PCA results 84 4.4.3 IA-PA results 88 4.5 Summary 100 Chapter 5 Conclusions 103 5.1 Summary of the Contributions 103 5.2 Future Works 105 국문초록 125 Acknowledgements 127Docto

Indoor Inertial Waypoint Navigation for the Blind

Indoor navigation technology is needed to support seamless mobility for the visually impaired. This paper describes the construction and evaluation of an inertial dead reckoning navigation system that provides real-time auditory guidance along mapped routes. Inertial dead reckoning is a navigation technique coupling step counting together with heading estimation to compute changes in position at each step. The research described here outlines the development and evaluation of a novel navigation system that utilizes information from the mapped route to limit the problematic error accumulation inherent in traditional dead reckoning approaches. The prototype system consists of a wireless inertial sensor unit, placed at the users’ hip, which streams readings to a smartphone processing a navigation algorithm. Pilot human trials were conducted assessing system efficacy by studying route-following performance with blind and sighted subjects using the navigation system with real-time guidance, versus offline verbal directions