• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.116.5-116
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• 2001

When inertial navigation system(INS) employ more sensors that mutually orthogonal sets to three, the redundant sensor system can have improved reliability and accuracy. For the redundant system the placement of redundant sensors is related to the system performance and also the number and proper orientation of sensors are important. We consider INS sensor configurations using two IMUs comprised mutually orthogonal sets of three. We suggest several configurations using two IMUs and analyze the system performance and the FDI(fault detection and isolation) properties from suggested configurations.

• ETRI Journal
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• v.40 no.4
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• pp.522-530
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• 2018

In this study, we developed a robotic walker that actively controls its speed and direction of movement according to the user's gait intention. Sensor fusion between a low-cost light detection and ranging (LiDAR) sensor and inertia measurement units (IMUs) helps determine the user's gait intention. The LiDAR determines the walking direction by detecting both knees, and the IMUs attached on each foot obtain the angular rate of the gait. The user's gait intention is given as the directional angle and the speed of movement. The two motors in the robotic walker are controlled with these two variables, which represent the user's gait intention. The estimated direction angle is verified by comparison with a Kinect sensor that detects the centroid trajectory of both the user's feet. We validated the robotic walker with an experiment by controlling it using the estimated gait intention.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.30-35
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• 2019

Inertial measurement units (IMUs) are widely used for wearable motion-capturing systems in the fields of biomechanics and robotics. When the IMUs are combined with optical motion sensors (hereafter, OPTs) for their complementary capabilities, it is necessary to align the coordinate system orientations between the IMU and OPT. In this study, we compare the application of two coordinate transformation-based orientation alignment methods between two coordinate systems. The first method (M1) applies angular velocity coordinate transformation, while the other method (M2) applies gyroscopic angle coordinate transformation. In M1 and M2, the angular velocities and angles, respectively, are acquired during random movement for a least-square algorithm to determine the alignment matrix between the two coordinate systems. The performance of each method is evaluated under various conditions according to the type of motion during measurement, number of data points, amount of noise, and the alignment matrix. The results show that M1 is free from drift errors, while drift errors are present in most cases where M2 is applied. Thus, this study indicates that M1 has a far superior performance than M2 for the alignment of IMU and OPT coordinate systems for motion analysis.

• Journal of Integrative Natural Science
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• v.17 no.3
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• pp.113-122
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• 2024

Advancements in sensor technology, particularly Inertial Measurement Units (IMU), are crucial in modern pose estimation. IMUs typically consist of accelerometers and gyroscopes (6-axis), with some models including magnetometers (9-axis). This study investigates the impact of sensor frequency on pose estimation accuracy using data from a 256Hz IMU sensor. The data sets analyzed include "spiralStairs," "stairsAndCorridor," and "straightLine," with frequencies varied to 128Hz, 64Hz, and 32Hz, and conditions categorized as stationary or dynamic. The results indicate that sensitivity remains high at lower frequencies under stationary conditions but declines in dynamic conditions. Performance comparison, based on Root Mean Square Error (RMSE) values, showed that lower frequencies lead to increased RMSE, thus diminishing model accuracy. Additionally, the Extended Kalman Filter (EKF) was tested as an alternative to Madgwick's algorithm but faced challenges due to insufficient sensor noise data.

• Journal of Biomedical Engineering Research
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• v.38 no.3
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• pp.129-136
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• 2017

The exoskeleton robot is a technology developed to be used in various fields such as military, industry and medical treatment. The exoskeleton robot works by sensing the movement of the wearer. By recognizing the wearer's daily activities, the exoskeleton robot can assist the wearer quickly and efficiently utilize the system. In this study, LDA, QDA, and kNN are used to classify gait types through kinetic data obtained from subjects. Walking was selected from general walking and stair walking which are mainly performed in daily life. Seven IMUs sensors were attached to the subject at the predetermined positions to measure kinematic factors. As a result, LDA was classified as 78.42%, QDA as 86.16%, and kNN as 87.10% ~ 94.49% according to the value of k.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.62-66
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• 2023

With the advancement in technology and rapid increase in the demand for microelectromechanical systems (MEMS) based inertial measurement units (IMUs), high-volume production and test system remain a major challenge for the MEMS industry. To compete with the challenging market of Industry 4.0, here we developed an automatic test system to evaluate the performance of the ovenized IMU sensors as well as analyze the data. The automatic test system was developed by interfacing a commercial MEMS IMU (BMI 088) using LabVIEW. The BMI 088 was tested experimentally for long-term bias stability, ON/OFF bias repeatability, and root mean square (rms) noise. Furthermore, the data was analyzed through the developed test system. The results show that the automatic test system has improved the test time and reduced human effort. The developed automatic test system is a significant approach to MEMS research and development (R&D) to increase and improve the mass production of IMUs.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1648-1656
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• 2018

The configuration of redundant inertial sensors are very important when considering navigation performance and fault detection and isolation (FDI) performance. By constructing a redundant sensor system using multiple inertial sensors, it is possible to improve the navigation performance and fault detection and isolation performance, which are highly related to the sensor configuration and allocation. In order to deploy multiple MEMS inertial measurement units effectively, a configuration and allocation methods considering navigation performance, fault detection and isolation performance, and lever arm effect in one plane are presented, and the performance is analyzed through simulation in this research. From the results, it is confirmed that the proposed configuration and allocation method can improve navigation, FDI, and lever arm effect rejection performances more effectively by more than 70%.

• Journal of Korea Game Society
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• v.19 no.5
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• pp.91-102
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• 2019

In this paper, we propose an improved method of plane tracking in developing AR games for smartphones using magnetic field sensor. The previous method based on ARCore is a VIO method using a mixture of SLAM and IMU of smartphones. The disadvantages of accelerometers and gyroscopes in IMUs cause errors in tracking the plane. We propose an improved method of planar tracking by adding the magnetic field sensor as well as the existing IMU sensors. Experimental results shows that our method reduces the error of the smartphone posture estimation.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.83-89
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• 2016

Internet of Things (IoT) is widely used for biomechanics in sports activities and AHRS(Attitude and Heading Reference System) is a more cost effective solution than conventional high-grade IMUs (Inertial Measurement Units) that only integrate gyroscopes. In this paper, we attach the AHRS to the snowboard to measure the motion data like Air To Fakie, Caballerial and Free Style. In order to reduce the measurement error, we have adopted the sensors equipped with Kalman filtering and also used Euler angle to quaternion conversion to reduce the Gimbal-lock effect. We have tested and evaluated the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it on the basic motions of Snowboarding from the 9-axis trajectory information which is gathered from AHRS sensor. With the result, PCA, ICA have low accuracy, but SVM have good accuracy to use for recognition of basic motions of Snowboarding.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.263-273
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• 2008

The precision of sensors' position is particularly important in the application of road extraction or digital map generation. In general, the various ranging solution systems such as GPS, Total Station, and Laser Ranger have been employed for the position of the sensor. Basically, the ranging solution system has problems that the signal may be blocked or degraded by various environmental circumstances and has low temporal resolution. To overcome those limitations a IMU/range integrated system could be introduced. In this paper, after pointing out the limitation of extended Kalman filter which has been used for workhorse in navigation and geodetic community, the two sampling based nonlinear filters which are sigma point Kalman filter using nonlinear transformation and carefully chosen sigma points and particle filter using the non-gaussian assumption are implemented and compared with extended Kalman filter in a simulation test. For the ranging solution system, the GPS and Total station was selected and the three levels of IMUs(IMU400C, HG1700, LN100) are chosen for the simulation. For all ranging solution system and IMUs the sampling based nonlinear filter yield improved position result and it is more noticeable that the superiority of nonlinear filter in low temporal resolution such as 5 sec. Therefore, it is recommended to apply non-linear filter to determine the sensor's position with low degree position sensors.