• Journal of Acupuncture Research
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• v.30 no.4
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• pp.25-33
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• 2013

Objectives : To assess the test-retest reliability and the intratest repeatability in measuring the cervical range of motion of healthy subjects with wireless microelectromechanical system inertial measurement unit(MEMS-IMU) system and to discuss the feasibility of this system in the clinical setting to evaluate the cervical spine musculoskeletal. Methods : 12 healthy people who were evaluated as no- or mild-disability with neck disability index were participated. Their cervical motion were measured with IMU twice in consecutive two days for the test-retest reliability study. Intratest repeatability was calculated in the two tests separately. The calculated intraclass correlation coefficients(ICC) were discussed and compared with the those of the previous studies. Results : Cervical range of motion data were acquired and statistically processed: left rotation($61.64^{\circ}$), right rotation($65.12^{\circ}$), extension($61.98^{\circ}$), flexion($52.81^{\circ}$), left bending($39.31^{\circ}$), right bending($41.08^{\circ}$). ICCs were 0.77~0.98(intratest repeatability) and 0.74~0.93 (test-retest reliability) in the primary motion. In the coupling motion, intratest repeatability ICCs were 0.93~ 0.99(transverse primary plane), 0.88~0.97(saggital primay plane), and 0.77~0.93(coronal primary plane). Test-retest reliability of coupling motion were 0.90~0.97(transverse primary plane), 0.00~0.72(saggital primary plane), and 0.04~0.76(coronal primary plane). Conclusions : Several types of range-of-motion devices are now on use in many fields including medicine, but the practicality of the devices in clinical use is questionable for the convenient and economical aspects. In this study, we presented the reliability of cervical range of motion test with the developed wireless MEMS-IMU system and discussed its potential utility in clinical use.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1632-1640
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• 2021

In order to improve target tracking performance of the conventional electro-optical tracking system (EOTS) in the high speed and high maneuvering vehicle, an EOTS navigation algorithm is proposed, in which an inertial measurement unit(IMU) is included and navigation results of the vehicle are used. The proposed algorithm integrates vehicle's navigation results and the IMU and the time-delay and the scale factor errors are augmented into the integrated Kalman filter. In order to evaluate the proposed navigation algorithm, a land vehicle navigation experiments were performed a navigation grade navigation system, TALIN4000 and a tactical grade IMU, LN-200 and a equipment for roll motion were loaded on the land vehicle. The performance evaluation results show that the proposed algorithm effecting works in high maneuvering environment and for the time-delay.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.2
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• pp.71-81
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• 2022

In order for electro-optical tracking system (EOTS) to have accurate target coordinate, accurate navigation results are required. If an integrated navigation system is configured using an inertial measurement unit (IMU) of EOTS and the vehicle's navigation results, navigation results with high rate can be obtained. Due to the time-delay of the navigation results of the vehicle in the EOTS and scale factor errors of the EOTS IMU in high-speed and high dynamic operation of the vehicle, it is much more difficult to have accurate navigation results. In this paper, an integrated navigation system of EOTS which compensates time-delay and scale factor error is proposed. The proposed integrated navigation system consists of vehicle's navigation system which provides time-delayed navigation results, an EOTS IMU, an inertial navigation system (INS), an augmented Kalman filter and integration Kalman filter. The augmented Kalman filter outputs navigation results, in which the time-delay of the vehicle's navigation results is compensated. The integration Kalman filter estimates position, velocity, attitude error of the EOTS INS and accelerometer bias, accelerometer scale factor error, gyro bias and gyro scale factor error from the difference between the output of the augmented Kalman filter and the navigation result of the EOTS INS. In order to check performance of the proposed integrated navigation system, simulations for output data of a measurement generator and land vehicle experiments were performed. The performance evaluation results show that the proposed integrated navigation system provides more accurate navigation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.757-770
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• 2020

We propose a hand gesture recognition method that is compatible with a head-up display (HUD) including small processing resource. For fast link adaptation with HUD, it is necessary to rapidly process gesture recognition and send the minimum amount of driver hand gesture data from the wearable device. Therefore, we use a method that recognizes each hand gesture with an inertial measurement unit (IMU) sensor based on revised correlation matching. The method of gesture recognition is executed by calculating the correlation between every axis of the acquired data set. By classifying pre-defined gesture values and actions, the proposed method enables rapid recognition. Furthermore, we evaluate the performance of the algorithm, which can be implanted within wearable bands, requiring a minimal process load. The experimental results evaluated the feasibility and effectiveness of our decomposed correlation matching method. Furthermore, we tested the proposed algorithm to confirm the effectiveness of the system using pre-defined gestures of specific motions with a wearable platform device. The experimental results validated the feasibility and effectiveness of the proposed hand gesture recognition system. Despite being based on a very simple concept, the proposed algorithm showed good performance in recognition accuracy.

• Special Issue of the Society of Naval Architects of Korea
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• 2008.09a
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• pp.68-75
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• 2008

This paper describes the design and implementation of a low cost inertial navigation system(INS) using an inertial measurement unit(IMU), a digital compass, GPS, and an embedded system. The system has been developed for a transporter that load and unload ship blocks in a shipbuilding yard. When the transporter would move from place to place, they would periodically pass under obstructions that would obscure the GPS signal. This increases the error when estimating the position. Thus the INS has been used to improve position accuracy. INS is also capable of providing continuous estimates of the transporter's position and orientation. Even though IMU is typically very expensive, this INS is made of "low cost" components and the indirect Kalman filtering algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1822-1829
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• 2008

Calibration of an MEMS inertial measurement unit is very important process for obtaining precise navigation performance. In this paper, one method is proposed to overcome a limitations on cost and efficiency using a relatively higher grade sensor and a rate table. The same dynamic input is applied to both the reference and the target sensors during and after calibration process, then the results are analyzed. The experimental results show that the proposed method is very effective and useful in practice.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.299-305
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• 2014

In this paper, a simple version of the hybrid navigation system using Kalman filter is proposed. The implemented hybrid navigation system is composed of a GPS to measure the position and the velocity, and a IMU(inertial measurement unit) to measure the acceleration and the posture of a mobile robot. A discrete Kalman filter is applied to provide the position of the robot by fusing both of the sensor data. When GPS signal is available, the navigation system estimates the position of the robot from the Kalman filter using position and velocity from GPS, and acceleration from IMU. During the interval until next GPS signal arrives, the system calculates the position of the robot using acceleration from IMU and velocity obtained at the previous step. Performance of the navigation system is verified by comparing the real path and the estimated path of the mobile robot. From experiments, we conclude that the navigation system is acceptable for the mobile robot.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.86-92
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• 2018

In recent times, motion capture technology using inertial measurement unit (IMU) sensors has been actively used in sports. In this study, we developed a canoe paddle, installed with an IMU and a water level sensor, as a system tool for training and calibration purposes in water sports. The hardware was fabricated to control an attitude heading reference system (AHRS) module, a water level sensor, a communication module, and a wireless charging circuit. We also developed an application program for the mobile device that processes paddling motion data from the paddling operation and also visualizes it. An AHRS module with acceleration, gyro, and geomagnetic sensors each having three axes, and a resistive water level sensor that senses the immersion depth in the water of the paddle represented the paddle motion. The motion data transmitted from the paddle device is internally decoded and classified by the application program in the mobile device to perform visualization and to operate functions of the mobile training/correction system. To conclude, we tried to provide mobile knowledge service through paddle sport data using this technique. The developed system works reasonably well to be used as a basic training and posture correction tool for paddle sports; the transmission delay time of the sensor system is measured within 90 ms, and it shows that there is no complication in its practical usage.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.12-18
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• 2012

This paper presents a computation method of LOS(Line Of Sight) angle using IMU(Inertial Measurement Unit) for an antenna on aerial vehicle to point to a stationary satellite. In the overall system, the antenna is located at the front of the vehicle, and an IMU is introduced to account for body flexure dynamic. And using the differences between the position and velocity of the IMU based navigation and those of GPS/INS at the vehicle center. Kalman filter is designed to suppress Strapdown INS drift errors.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.373-380
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• 2005

Mobile Mapping Systems are effective systems to acquire the position and image data using vehicle equipped with the GPS (Global Positioning System), IMU (Inertial Measurement Unit), and CCD camera. They are used in various fields of road facility management, map update, and etc. In the general photogrammetry such as aerial photogrammetry, GCP (Ground Control Point)s are needed to compute the image exterior orientation elements (the position and attitude of camera). These points are measured by field survey at the time of data acquisition. But it costs much time and money. Moreover, it is not possible to make sufficient GCP as much as we want. However Mobile Mapping Systems are more efficient both in time and money because they can obtain the position and attitude of camera at the time of photographing. That is, Image Orientation Technique must use GCP to compute the image exterior orientation elements, but on the other hand Direct Georeferencing can directly compute the image exterior orientation elements by GPS/INS. In this paper, we analyze about the positional accuracy comparison of ground point using the Image Orientation Technique and Direct Georeferencing Technique.