• Journal of Information Processing Systems
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• v.6 no.3
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• pp.347-358
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• 2010

This paper presents a measurement system for 3D hand-drawn gesture motion. Many pen-type input devices with Inertial Measurement Units (IMU) have been developed to estimate 3D hand-drawn gesture using the measured acceleration and/or the angular velocity of the device. The crucial procedure in developing these devices is to measure and to analyze their motion or trajectory. In order to verify the trajectory estimated by an IMU-based input device, it is necessary to compare the estimated trajectory to the real trajectory. For measuring the real trajectory of the pen-type device, a PHANToMTM haptic device is utilized because it allows us to measure the 3D motion of the object in real-time. Even though the PHANToMTM measures the position of the hand gesture well, poor initialization may produce a large amount of error. Therefore, this paper proposes a calibration method which can minimize measurement errors.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.64-76
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• 1998

In this paper, we propose the dynamic modeling, path planning and tracking algorithms of 4-wheeled 2-d.o.f.(degree of freedom) mobile robot(WMR). The gaussian functions are applied to design the smooth path of WMR. To calculate the WMR position in real time, we use three components of inertial measurement units(IMU). These units have initial error because of the rotation rate of earth, gravity acceleration and so on. Therefore we derive the initial error model of IMU, and compare the fitness diagnosis about probability characteristics of real data adn estimated data. The performance of IMU with error model and Kalman filter is compared to that without filter and error model. The simulation results show that the proposed dynamic model, path planning and tracking algorithms are more useful than the conventional control algorithm.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.649-653
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• 1988

The inertial measurement units in Strapdown System are characterized by the fact that sensors directly mounted to the vehicle frame. So the sensors are subjected to the translatory and rotation dynamics of the vehicle. The sensor outputs involve many error terms. We must compensate the error terms for accuracy improvement. The method which identify the error parameter is studied and suggested.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.141-148
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• 2003

This paper presents a rotating ann test for assessment of an underwater hybrid navigation system for a semi-autonomous underwater vehicle. The navigation system consists of an inertial measurement unit (IMU), an ultra-short baseline (USBL) acoustic navigation sensor and a doppler velocity log (DVL) accompanying a magnetic compass. The errors of inertial measurement units increase with time due to the bias errors of gyros and accelerometers. A navigational system model is derived to include the error model of the USBL acoustic navigation sensor and the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 25 in the order. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors and correct the state equation when the measurements are available. The rotating ann tests are conducted in the Ocean Engineering Basin of KRISO, KORDI to generate circular motion in laboratory, where the USBL system was absent in the basin. The hybrid underwater navigation system shows good tracking performance against the circular planar motion. Additionally this paper checked the effects of the sampling ratio of the navigation system and the possibility of the dead reckoning with the DVL and the magnetic compass to estimate the position of the vehicle.

• Aerospace Engineering and Technology
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• v.1 no.1
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• pp.28-41
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• 2002

The purpose of this paper is to show and define the performance, the system mechanization and the algorithm of the Strapdown Inertial Navigation System(SDINS). First, navigation equations are derived in the Earth Fixed mechanization and this mechanization apply to the two kinds of inertial measurement units which consist of same fiber optic gyros and different accelerometers(SDINS-1 and SDINS-2). Those two accelerometers have the different bias. To evaluate its performance, two kinds of tests have been performed - static motionless test, and rectangle-route moving test. The results of the moving test are compared with the results of Differential GPS which has an accuracy with ±2.0mm. and are presented in this paper.

• 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.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.80-88
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• 2017

Although hazard identification is one of the most important steps of safety management process, numerous hazards remain unidentified in the construction workplace due to the dynamic environment of the construction site and the lack of available resource for visual inspection. To this end, our previous study proposed the collective sensing approach for safety hazard identification and showed the feasibility of identifying hazards by capturing collective abnormalities in workers' walking patterns. However, workers generally performed different activities during the construction task in the workplace. Thereby, an additional process that can identify the worker's walking activity is necessary to utilize the proposed hazard identification approach in real world settings. In this context, this study investigated the feasibility of identifying walking activities during construction task using Wearable Inertial Measurement Units (WIMU) attached to the worker's ankle. This study simulated the indoor masonry work for data collection and investigated the classification performance with three different machine learning algorithms (i.e., Decision Tree, Neural Network, and Support Vector Machine). The analysis results showed the feasibility of identifying worker's activities including walking activity using an ankle-attached WIMU. Moreover, the finding of this study will help to enhance the performance of activity recognition and hazard identification in construction.

• PNF and Movement
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• v.18 no.2
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• pp.287-296
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• 2020

Purpose: The study aims were to develop a wearable inertial sensor-based gait analysis device that uses machine learning algorithms, and to validate this novel device using temporal gait parameters. Methods: Thirty-four healthy young participants (22 male, 12 female, aged 25.76 years) with no musculoskeletal disorders were asked to walk at three different speeds. As they walked, data were simultaneously collected by a motion capture system and inertial measurement units (Reseed®). The data were sent to a machine learning algorithm adapted to the wearable inertial sensor-based gait analysis device. The validity of the newly developed instrument was assessed by comparing it to data from the motion capture system. Results: At normal speeds, intra-class correlation coefficients (ICC) for the temporal gait parameters were excellent (ICC [2, 1], 0.99~0.99), and coefficient of variation (CV) error values were insignificant for all gait parameters (0.31~1.08%). At slow speeds, ICCs for the temporal gait parameters were excellent (ICC [2, 1], 0.98~0.99), and CV error values were very small for all gait parameters (0.33~1.24%). At the fastest speeds, ICCs for temporal gait parameters were excellent (ICC [2, 1], 0.86~0.99) but less impressive than for the other speeds. CV error values were small for all gait parameters (0.17~5.58%). Conclusion: These results confirm that both the wearable inertial sensor-based gait analysis device and the machine learning algorithms have strong concurrent validity for temporal variables. On that basis, this novel wearable device is likely to prove useful for establishing temporal gait parameters while assessing gait.