1,766 research outputs found

    DeepWalking: Enabling Smartphone-based Walking Speed Estimation Using Deep Learning

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    Walking speed estimation is an essential component of mobile apps in various fields such as fitness, transportation, navigation, and health-care. Most existing solutions are focused on specialized medical applications that utilize body-worn motion sensors. These approaches do not serve effectively the general use case of numerous apps where the user holding a smartphone tries to find his or her walking speed solely based on smartphone sensors. However, existing smartphone-based approaches fail to provide acceptable precision for walking speed estimation. This leads to a question: is it possible to achieve comparable speed estimation accuracy using a smartphone over wearable sensor based obtrusive solutions? We find the answer from advanced neural networks. In this paper, we present DeepWalking, the first deep learning-based walking speed estimation scheme for smartphone. A deep convolutional neural network (DCNN) is applied to automatically identify and extract the most effective features from the accelerometer and gyroscope data of smartphone and to train the network model for accurate speed estimation. Experiments are performed with 10 participants using a treadmill. The average root-mean-squared-error (RMSE) of estimated walking speed is 0.16m/s which is comparable to the results obtained by state-of-the-art approaches based on a number of body-worn sensors (i.e., RMSE of 0.11m/s). The results indicate that a smartphone can be a strong tool for walking speed estimation if the sensor data are effectively calibrated and supported by advanced deep learning techniques.Comment: 6 pages, 9 figures, published in IEEE Global Communications Conference (GLOBECOM

    Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

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    The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

    Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective

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    Blind people have limited access to information about their surroundings, which is important for ensuring one's safety, managing social interactions, and identifying approaching pedestrians. With advances in computer vision, wearable cameras can provide equitable access to such information. However, the always-on nature of these assistive technologies poses privacy concerns for parties that may get recorded. We explore this tension from both perspectives, those of sighted passersby and blind users, taking into account camera visibility, in-person versus remote experience, and extracted visual information. We conduct two studies: an online survey with MTurkers (N=206) and an in-person experience study between pairs of blind (N=10) and sighted (N=40) participants, where blind participants wear a working prototype for pedestrian detection and pass by sighted participants. Our results suggest that both of the perspectives of users and bystanders and the several factors mentioned above need to be carefully considered to mitigate potential social tensions.Comment: The 2020 ACM CHI Conference on Human Factors in Computing Systems (CHI 2020

    적분 및 매개변수 기법 융합을 이용한 스마트폰 다중 동작에서 보행 항법

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    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

    Advanced Map Matching Technologies and Techniques for Pedestrian/Wheelchair Navigation

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    Due to the constantly increasing technical advantages of mobile devices (such as smartphones), pedestrian/wheelchair navigation recently has achieved a high level of interest as one of smartphones’ potential mobile applications. While vehicle navigation systems have already reached a certain level of maturity, pedestrian/wheelchair navigation services are still in their infancy. By comparing vehicle navigation systems, a set of map matching requirements and challenges unique in pedestrian/wheelchair navigation is identified. To provide navigation assistance to pedestrians and wheelchair users, there is a need for the design and development of new map matching techniques. The main goal of this research is to investigate and develop advanced map matching technologies and techniques particular for pedestrian/wheelchair navigation services. As the first step in map matching, an adaptive candidate segment selection algorithm is developed to efficiently find candidate segments. Furthermore, to narrow down the search for the correct segment, advanced mathematical models are applied. GPS-based chain-code map matching, Hidden Markov Model (HMM) map matching, and fuzzy-logic map matching algorithms are developed to estimate real-time location of users in pedestrian/wheelchair navigation systems/services. Nevertheless, GPS signal is not always available in areas with high-rise buildings and even when there is a signal, the accuracy may not be high enough for localization of pedestrians and wheelchair users on sidewalks. To overcome these shortcomings of GPS, multi-sensor integrated map matching algorithms are investigated and developed in this research. These algorithms include a movement pattern recognition algorithm, using accelerometer and compass data, and a vision-based positioning algorithm to fill in signal gaps in GPS positioning. Experiments are conducted to evaluate the developed algorithms using real field test data (GPS coordinates and other sensors data). The experimental results show that the developed algorithms and the integrated sensors, i.e., a monocular visual odometry, a GPS, an accelerometer, and a compass, can provide high-quality and uninterrupted localization services in pedestrian/wheelchair navigation systems/services. The map matching techniques developed in this work can be applied to various pedestrian/wheelchair navigation applications, such as tracking senior citizens and children, or tourist service systems, and can be further utilized in building walking robots and automatic wheelchair navigation systems

    Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation

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    The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals. Obtaining accurate navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-the-art performance, compared to model-based methods. In this paper we review multidisciplinary, data-driven based navigation algorithms developed and experimentally proven at the Autonomous Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for human and animal applications, varied autonomous platforms, and multi-purpose navigation and fusion approachesComment: 22 pages, 13 figure

    Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft

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    The explosive growth of the location-enabled devices coupled with the increasing use of Internet services has led to an increasing awareness of the importance and usage of geospatial information in many applications. The mobile navigation apps (often called “Maps”), use a variety of available data sources to calculate and predict the travel time for different modes. This paper evaluates the pedestrian mode of Maps apps in three major smartphone operating systems (Android, iOS and Windows Phone). We will demonstrate that the Maps apps on iOS, Android and Windows Phone in pedestrian mode, predict travel time without learning from the individual’s movement profile. Then, we will exemplify that those apps suffer from a specific data quality issue (the absence of information about location and type of pedestrian crossings). Finally, we will illustrate learning from movement profile of individuals using predictive analytics models to improve the accuracy of travel time estimation for each user (personalization)

    사용자 상황인지 딥러닝을 사용한 GPS 반송파 / 관성 센서 결합 스마트폰 보행자 항법

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    학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 여재익.본 논문에서는 스마트폰 Galaxy S8 환경에서 GPS / INS 결합 보행자 항법을 수행하였으며, 스마트폰 센서의 특성을 자세히 분석하였다. 이에 최근 공개된 Android GNSS API 를 사용하여 GPS 원시데이터를 항법에 이용하면서, Cycle slip 을 보정한 Carrier phase 를 이용한 속도 결정법을 사용하였다. 이로 인해 기존의 NMEA GPS 를 사용한 방식의 스마트폰 보행자 항법보다 정밀한 위치, 속도 항법이 가능하였고, 성능을 향상 시켰다. 또한 사용자 상황 분석이 가능한 분류 딥러닝 기법을 사용하여 각 보행 상황에 따른 분류감지가 가능하였음 보였으며, LSTM 의 입력부분을 변화한 몇가지 딥러닝 모델의 성능을 비교하였다. 이를 통해서 사용자의 보행 상황에 따른 적응적 보행자 항법 파라메터 결정이 가능함의 가능성을 보였다.In this research, the overall construction of the smartphone GPS / INS pedestrian dead reckoning system is detaily described with considering the smartphone sensor measurement properties. Also, the recent android GNSS API which can provide the raw GPS measurement is used. With carrier phase, the cycleslip compensated velocity determination is considered. As a result, the carrier phase /INS integrated pedestrian dead reckoning shows the more precise navigation accuracy than NMEA. Moreover, The deep learning approach is applied in the user context classification to change the parameters in the pedestrian dead reckoning system. The author compares the effect of several transformed inputs for the LSTM model and validate each classification performances.Abstract i Contents ii List of Figures iv List of Tables vii Chapter 1. Introduction 1 1.1 Motivation and Backgrounds 1 1.2 Research Purpose and Contribution 3 1.3 Contents and Methods of Research 3 Chapter 2. Smartphone GPS / INS measurements analysis 4 2.1 Smartphone GNSS measurements 4 2.1.1 Android Raw GNSS Measurements API 4 2.1.2 Raw GPS Measurements Properties 7 2.1.3 Smartphone NMEA Location Provider 8 2.1.4 Pseudorange Based Position Estimation 10 2.1.5 Position Determination Experiment 11 2.2 Smartphone INS Measurements 12 2.2.1 Android Sensor Manager API 12 2.2.2 INS Measurements Properties 13 2.2.3 Noise level, Constant bias, Scale factor, Calibration 14 2.2.4. Accelerometer, Gyroscope Calibration Experiment 17 2.2.5 Magnetometer Ellipse Fitting Calibration 22 2.2.6 Random Bias, Allan Variance Exiperiment 25 2.3 Developed Android Smartphone App 30 Chapter 3. Pedestrian Dead Reckoning 31 3.1 Pedestrian Dead-Reckoning System 31 3.1.1 Attitude Determination Quaternion Kalman Filter 32 3.1.2 Attitude Determination Simulation , Experiment 35 3.1.3 Walking Detection 39 3.1.4 Step Counting, Stride Length 41 3.1.5 Pedestrian Dead Reckoning Experiment 45 Chapter 4. Carrier phase / INS integrated Pedestrian Dead Reckoning 50 4.1 Carrier phase Cycleslip Compensation & Velocity Determination 50 4.1.1 Carrier phase Cycleslip Compensation 50 4.1.2 Android Environment Cycle slip Detection 51 4.1.3 False Alarm & Miss Detection Analysis 55 4.1.4 Doppler, Carrier Based Velocity Estimation 56 4.1.5 Cycle slip Compensation & Velocity Determination Experiment 58 4.2 Raw GPS / INS Integrated Pedestrian Dead Reckoning 63 4.2.1 GPS / INS Integration 63 4.2.2 Position Determination Extended Kalman Filter 65 4.2.3 Raw GPS / INS Integrated Pedestrian Dead Reckoning Experiment 66 Chapter 5. User Context Classification Deep learning for Adaptive PDR 69 5.1 Smartphone Location / Walking Context Classification 69 5.1.1 Smartphone Location / Walking Context Dataset 69 5.1.2 Deep Learning Models 71 5.1.3 Comparison of Input Transformations 72 Chapter 6. Conclusion & Future work 76 Chapter 7. Bibliography 77Maste

    Integração de localização baseada em movimento na aplicação móvel EduPARK

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    More and more, mobile applications require precise localization solutions in a variety of environments. Although GPS is widely used as localization solution, it may present some accuracy problems in special conditions such as unfavorable weather or spaces with multiple obstructions such as public parks. For these scenarios, alternative solutions to GPS are of extreme relevance and are widely studied recently. This dissertation studies the case of EduPARK application, which is an augmented reality application that is implemented in the Infante D. Pedro park in Aveiro. Due to the poor accuracy of GPS in this park, the implementation of positioning and marker-less augmented reality functionalities presents difficulties. Existing relevant systems are analyzed, and an architecture based on pedestrian dead reckoning is proposed. The corresponding implementation is presented, which consists of a positioning solution using the sensors available in the smartphones, a step detection algorithm, a distance traveled estimator, an orientation estimator and a position estimator. For the validation of this solution, functionalities were implemented in the EduPARK application for testing purposes and usability tests performed. The results obtained show that the proposed solution can be an alternative to provide accurate positioning within the Infante D. Pedro park, thus enabling the implementation of functionalities of geocaching and marker-less augmented reality.Cada vez mais, as aplicações móveis requerem soluções de localização precisa nos mais variados ambientes. Apesar de o GPS ser amplamente usado como solução para localização, pode apresentar alguns problemas de precisão em condições especiais, como mau tempo, ou espaços com várias obstruções, como parques públicos. Para estes casos, soluções alternativas ao GPS são de extrema relevância e veem sendo desenvolvidas. A presente dissertação estuda o caso do projeto EduPARK, que é uma aplicação móvel de realidade aumentada para o parque Infante D. Pedro em Aveiro. Devido à fraca precisão do GPS nesse parque, a implementação de funcionalidades baseadas no posionamento e de realidade aumentada sem marcadores apresenta dificuldades. São analisados sistemas relevantes existentes e é proposta uma arquitetura baseada em localização de pedestres. Em seguida é apresentada a correspondente implementação, que consiste numa solução de posicionamento usando os sensores disponiveis nos smartphones, um algoritmo de deteção de passos, um estimador de distância percorrida, um estimador de orientação e um estimador de posicionamento. Para a validação desta solução, foram implementadas funcionalidades na aplicação EduPARK para fins de teste, e realizados testes com utilizadores e testes de usabilidade. Os resultados obtidos demostram que a solução proposta pode ser uma alternativa para a localização no interior do parque Infante D. Pedro, viabilizando desta forma a implementação de funcionalidades baseadas no posicionamento e de realidade aumenta sem marcadores.EduPARK é um projeto financiado por Fundos FEDER através do Programa Operacional Competitividade e Internacionalização - COMPETE 2020 e por Fundos Nacionais através da FCT - Fundação para a Ciência e a Tecnologia no âmbito do projeto POCI-01-0145-FEDER-016542.Mestrado em Engenharia Informátic
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