Browsing by Author "Sun, Kuan-Chun"

Now showing 1 - 8 of 8

A Kinematic Human-Walking Model for the Normal-Gait-Speed Estimation Using Tri-Axial Acceleration Signals at Waist Location
(2013-08-01)
Hu, Jwu-Sheng
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Sun, Kuan-Chun
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Cheng, Chi-Yuan
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電控工程研究所
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Institute of Electrical and Control Engineering
This study aims at estimating the human walking speed using wearable accelerometers by proposing a novel virtual inverted pendulum model. This model not only keeps the important characteristics of both the biped rolling-foot and the inverted pendulum model, but also makes the speed estimation feasible using human body acceleration. Rather than using statistical methods, the proposed kinematic walking model enables calibration of the parameters during walking using only one tri-axial accelerometer on the waist that collects the user's body acceleration. In addition, this model also includes the effect of rotation of the waist within a walking cycle, which improves the estimation accuracy. Experimental results for a group of humans show a 0.58% absolute error mean and 0.72% error deviation, which is far better than the results of other known studies with accelerometers mounted on the upper body.
A Model-based Human Walking Speed Estimation Using Body Acceleration Data
(2012)
Hu, Jwu-Sheng
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Sun, Kuan-Chun
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Cheng, Chi-Yuan
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電控工程研究所
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Institute of Electrical and Control Engineering
This study aims at estimating the human walking speed using wearable accelerometers by proposing a novel virtual inverted pendulum model. This model not only keeps the important characteristic in biped rolling-foot model, but also makes the speed estimation feasible using human body acceleration. Rather than the statistical methods, the proposed kinematic walking model enables calibration of the parameters during walking using only one tri-axial accelerometer on waist that reflects the user's inertia information during walking. In addition, this model also includes the effect of rotation of waist within a walking cycle that improves the estimation accuracy. Experimental results on a group of humans show a 1.22% error mean and 2.78% deviation, which is far better than other known studies.
A Robust Orientation Estimation Algorithm Using MARG Sensors
(2015-03-01)
Hu, Jwu-Sheng
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Sun, Kuan-Chun
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電機工程學系
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電控工程研究所
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Department of Electrical and Computer Engineering
This paper presents a robust orientation estimation algorithm by using magnetic, angular rate, and gravity sensors. The robustness is achieved by two online methods: compensation of hard iron effect for the magnetometer and separation of the accelerometer signals into gravity projections and linear accelerations. Further, direct cosine matrix is used for the rotation matrix relative to the north, east, down frame. The fusion equations are solved using state-constrained Kalman filter. The simulation and experiment results show the effectiveness of the proposed algorithm in estimating the orientation under acceleration and hard iron influence.
A Single Dipole-based Localization Method in Near Magnetic Field using IMU Array
(2016)
Sun, Kuan-Chun
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Lo, Sheng-Wen
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Hu, Jwu-Sheng
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交大名義發表
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National Chiao Tung University
This study aims to find an efficient 3D localization method related to a single dipole landmark in near magnetic field using IMU (Inertial Measurement Unit) array. We derive a novel algorithm that can avoid dealing with high order nonlinearity and singularity of distance in denominator in a mathematical dipole model by using a designed structure array to approach the gradient tensor. Moreover, we apply a state constrained Kalman filter for solving an underdetermined problem and estimating the rotation matrix between sensor and landmark simultaneously. The simulation analysis and experiment result show the effectiveness of the proposed algorithm in estimating the 3D position from a single dipole magnetic landmark.
A Tri-Dipole based Location and Orientation Estimation Method in Near Magnetic Field
(2018-01-01)
Lo, Sheng-Wen
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Sun, Kuan-Chun
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Hsieh, Shih-Ho
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Hu, Jwu-Sheng
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Chang, Jen-Yuan
This paper aims to find an accurate, robust, novel location and orientation estimation method related to a landmark consist of three orthogonal electromagnets modulated with different frequencies. The solution uniqueness is explained in this paper with the fact that near magnetic field has more distinguishable features. The proposed method demodulate the signals from the landmark and analyze independent magnetic field from three electromagnets basing on magnetic dipole model. Extended Kalman filter is applied to solve the higher order nonlinear problems. The simulation and experiment result show the effectiveness of the proposed algorithm.
An Novel Landmark-based Localization Method in Mobile Platform using Magnetic Dipole
(2016-01-01)
Sun, Kuan-Chun
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Lo, Sheng-Wen
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電控工程研究所
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Institute of Electrical and Control Engineering
This study aims to find a low-cost, robust, novel localization method related to a single magnetic landmark using 9D MARG sensors (Magnetic, Angular Rate, and Gravity). We derived an algorithm that can accumulated the space magnetic information during a time period to construct the flux distribution near the landmark. This helps to locate the coordinate of the moving object with respect to the landmark. The Extended Kalman is used for solving the problem and reducing the noise effect. The simulation analysis and experiment result show the effectiveness of the proposed algorithm in estimating the localization from the magnetic landmark.
GNSS/INS Sensor Fusion Using Kalman Filter with Covariance Adaptation
(2013-01-01)
Cheng, Chi-Yuan
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Sun, Kuan-Chun
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Hu, Jwu-Sheng
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電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a new method which estimates the Kalman filter\'s measurement noise covariance matrix in a loosely coupled GNSS/INS sensor fusion system. The signals received from the satellites are heavily influenced by the environmental conditions, and will cause the noise variance to change dramatically. Thus, estimating the measurement noise to improve the fusion quality is very important. Most of the proposed methods use the moving average method to estimate the measurement noise online from the innovation sequence of the Kalman filter in the window interval. However, those methods can only have a good performance in a quasi-static environment, e. g., freeway where the signals are rarely blocked. In dynamic environments such as the urban area, the satellite signals are often blocked by the buildings, resulting in a large range of the noise variance. This paper proposes to use Savitzky-Golay filter with degree adaptation (ADSG) to estimate the measurement noise. The ADSG filter uses local polynomial regression method to fit the delayed-sample innovation sequence in the window interval, and adjust the polynomial order by the statistics F-test. Experiments show that the proposed method\'s horizontal position error is about 0.3m, which is better than several existing methods.
IMU-Assisted Monocular Visual Odometry Including the Human Walking Model for Wearable Applications
(2013)
Hu, Jwu-Sheng
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Tseng, Chin-Yuan
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Chen, Ming-Yuan
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Sun, Kuan-Chun
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電控工程研究所
In this paper, we present a novel approach for monocular visual odometry combined with an inertial measurement unit to estimate the human walking trajectory. The scale factor is computed from the walking speed estimation derived by the kinematic model of human walking. This model not only keeps the important characteristic in biped rolling-foot model, but also makes the speed estimation feasible using human body acceleration. A wearable and calibrated IMU-Camera device is made to mount on the center of human\'s waist for experimental verification. With the human walking speed and gyroscope measurement, an Unscented Kalman filter is implemented to estimate the current scale factor and refine the attitude of the camera. Experiments in an indoor environment are conducted to show the effectiveness of the proposed method.