신발 장착 관성 센서 기반 보행자 추측 항법의 센서 대역폭 및 샘플링 주파수에 따른 성능 분석

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 박찬국.개인의 위치를 기반으로 다양한 서비스를 제공하는 기술들에 대한 관심이 높다. 이러한 기술은 개인 항법 시스템(PNS, Personal Navigation System)이라 불리는데, 예로, 소방관이나 군인의 임무수행 중 위치 정보 제공이나 쇼핑몰 내에서 고객들의 위치 정보 제공 등 다양한 분야에서 사용되고 있다. 개인 항법 시스템 중 보행 항법 시스템(PDR, Pedestrian Dead Reckoning)은 관성 센서를 사용하여 보행자의 위치를 추정한다. 신발에 관성 센서를 부착한 보행 항법은 관성 항법을 기반으로 한다. 자이로와 가속도계이 바이어스와 백색 잡음에 의해 발생하는 추정 오차를 줄이기 위해서 관성 항법은 영속도 보정(ZUPT, zero-velocity update)과 결합된다. 영속도 보정은 보행자 신발의 속도가 접지기(stance phase)에서 0이라고 가정한다. 바이어스와 백색 잡음으로 인한 오차는 ZUPT등을 이용하여 줄일 수 있지만, 다른 원인으로 인한 오차는 여전히 남아 있다. 보행 중 신발의 움직임은 다양한 주파수 성분으로 표현된다. 특히, 신발이 지면에 닿으면서 충격이 발생하는 heel-strike에서는 자이로와 가속도계 신호가 급격히 변하면서 고주파 성분이 포함될 것이다. 센서는 대역폭과 샘플링 주파수에 의해 측정할 수 있는 주파수 범위가 결정된다. 대역폭 또는 샘플링 주파수가 작을 경우, 이를 초과하는 주파수 성분이 측정되지 않아 측정 정확도가 낮아지고 위치 오차가 발생한다. 본 논문에서는 보행 주기 동안 센서가 측정한 신호를 주파수 분석을 하고 측정 신호의 정확도의 저하를 방지하기 위한 주파수 범위를 제안한다. 다양한 대역폭과 샘플링 주파수에 따라 보행 항법 시스템의 위치 추정 성능을 비교하여 제안한 주파수 범위의 타당성을 검증한다.There is a high interest in technologies that provide various services based on personal location. These service is called personal navigation system (PNS), and it is used in a variety of fields such as providing location information during the mission of firefighters or soldiers, and providing customers location in shopping malls. Pedestrian dead reckoning (PDR), a personal navigation system, uses an inertial sensor to estimate the position of a pedestrian. PDR with foot-mounted IMU is based on inertial navigation system (INS). In order to reduce the estimation error caused by gyro and accelerometer bias and white noise, INS is combined with zero-velocity update (ZUPT). The ZUPT assumes that the velocity of the pedestrian shoe is zero in the stance phase. Error due to bias and white noise can be reduced by using ZUPT, but error still remain due to other factors. The movement of the shoe during walking is represented by various frequency components. In particular, the gyro and accelerometer signal change suddenly and contain high-frequency components in the heel-strike, when the impact occurs as the shoe touches the ground. The IMU determines the frequency range that can be measured by the bandwidth and the sampling rate. If the bandwidth or sampling rate is narrow, the frequency components exceeding boundary can not measured and position error occurs because the measurement accuracy is lowered. In this paper, I propose a frequency range to analyze the signal measured by the sensor during the gait cycle and to prevent the degradation of the accuracy of the signal. The validity of the proposed frequency range is verified by comparing the position estimation performance of the PDR system with various bandwidth and sampling rate.1. 서 론 1 1.1. 연구 배경 1 1.2. 연구 목적 및 내용 4 2. 보행 항법 시스템 6 2.1. 서론 6 2.2. 적분 기반 보행 항법 7 2.2.1. 스트랩다운 관성 항법 9 2.2.2. 확장 칼만 필터 15 2.2.3. INS-EKF-ZUPT 18 2.3. 일반적인 보행 항법 시스템의 한계 24 2.3.1. 불충분한 센서 대역폭 25 3. 센서 대역폭과 샘플링 주파수에 대한 보행 항법의 성능 27 3.1. 보행자 동작의 주파수 성분 27 3.1.1. 데이터 수집을 위한 실험 환경 27 3.1.2. 에너지 스펙트럼의 누적 분포 30 3.1.3. 단시간 푸리에 변환 36 3.2. 센서 대역폭과 샘플링 주파수에 대한 보행 항법의 성능 비교 41 3.2.1. 성능 비교를 위한 실험 환경 41 3.2.2. 대역폭이 보행 항법 시스템에 미치는 영향 43 3.2.3. 샘플링 주파수가 보행 항법 시스템에 미치는 영향 45 3.3. 요약 47 4. 결론 49 참 고 문 헌 52 ABSTRACT 59Maste

An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19, 1.68, 2.08, and 1.23, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics

Constructing a reference standard for sports science and clinical movement sets using IMU-based motion capture technology

Motion analysis has improved greatly over the years through the development of low-cost inertia sensors. Such sensors have shown promising accuracy for both sport and medical applications, facilitating the possibility of a new reference standard to be constructed. Current gold standards within motion capture, such as high-speed camera-based systems and image processing, are not suitable for many movement-sets within both sports science and clinical movement analysis due to restrictions introduced by the movement sets. These restrictions include cost, portability, local environment constraints (such as light level) and poor line of sight accessibility. This thesis focusses on developing a magnetometer-less IMU-based motion capturing system to detect and classify two challenging movement sets: Basic stances during a Shaolin Kung Fu dynamic form, and severity levels from the modified UPDRS (Unified Parkinson’s Disease Rating Scale) analysis tapping exercise. This project has contributed three datasets. The Shaolin Kung Fu dataset is comprised of 5 dynamic movements repeated over 350 times by 8 experienced practitioners. The dataset was labelled by a professional Shaolin Kung Fu master. Two modified UPDRS datasets were constructed, one for each of the two locations measured. The modified UPDRS datasets comprised of 5 severity levels each with 100 self-emulated movement samples. The modified UPDRS dataset was labelled by a researcher in neuropsychological assessment. The errors associated with IMU systems has been reduced significantly through a combination of a Complementary filter and applying the constraints imposed by the range of movements available in human joints. Novel features have been extracted from each dataset. A piecewise feature set based on a moving window approach has been applied to the Shaolin Kung Fu dataset. While a combination of standard statistical features and a Durbin Watson analysis has been extracted from the modified UPDRS measurements. The project has also contributed a comparison of 24 models has been done on all 3 datasets and the optimal model for each dataset has been determined. The resulting models were commensurate with current gold standards. The Shaolin Kung Fu dataset was classified with the computational costly fine decision tree algorithm using 400 splits, resulting in: an accuracy of 98.9%, a precision of 96.9%, a recall value of 99.1%, and a F1-score of 98.0%. A novel approach of using sequential forward feature analysis was used to determine the minimum number of IMU devices required as well as the optimal number of IMU devices. The modified UPDRS datasets were then classified using a support vector machine algorithm requiring various kernels to achieve their highest accuracies. The measurements were repeated with a sensor located on the wrist and finger, with the wrist requiring a linear kernel and the finger a quadratic kernel. Both locations achieved an accuracy, precision, recall, and F1-score of 99.2%. Additionally, the project contributed an evaluation to the effect sensor location has on the proposed models. It was concluded that the IMU-based system has the potential to construct a reference standard both in sports science and clinical movement analysis. Data protection security and communication speeds were limitations in the system constructed due to the measured data being transferred from the devices via Bluetooth Low Energy communication. These limitations were considered and evaluated in the future works of this project

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