• The Journal of Korean Physical Therapy
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• v.29 no.3
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• pp.145-151
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• 2017

Purpose: The purpose of this study was to investigate the validity and reliability of the measurement of shoulder joint motions using an inertial measurement unit (IMU). Methods: For this study, 33 participants (32 females and 1 male) were recruited. The subjects were passively positioned with the shoulder placed at specific angles using a goniometer (shoulder flexion $0^{\circ}-170^{\circ}$, abduction $0^{\circ}-170^{\circ}$, external rotation $0^{\circ}-90^{\circ}$, and internal rotation $0^{\circ}-60^{\circ}$ angles). Kinematic data on the shoulder joints were simultaneously obtained using IMU three-dimensional (3D) angular measurement (MyoMotion) and photographic measurement. Test-retest reliability and concurrent validity were examined. Results: The MyoMotion system provided good to very good relative reliability with small standard error of measurement (SEM) and minimal detectable change (MDC) values from all three planes. It also presented acceptable validity, except for some of shoulder flexion, shoulder external rotation, and shoulder abduction. There was a trend for the shoulder joint measurements to be underestimated using the IMU 3D angular measurement system compared to the goniometer and photo methods in all planes. Conclusion: The IMU 3D angular measurement provided a reliable measurement and presented acceptable validity. However, it showed relatively low accuracy in some shoulder positions. Therefore, using the MyoMotion measurement system to assess shoulder joint angles would be recommended only with careful consideration and supervision in all situations.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.3
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• pp.261-270
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• 2017

Bad sitting postures are known to cause for a variety of diseases or physical deformation. However, it is not easy to fit right sitting posture for long periods of time. Therefore, methods of distinguishing and inducing good sitting posture have been constantly proposed. Proposed methods were image processing, using pressure sensor attached to the chair, and using the IMU (Internal Measurement Unit). The method of using IMU has advantages of simple hardware configuration and free of various constraints in measurement. In this paper, we researched on distinguishing sitting postures with a small amount of data using just one IMU. Feature extraction method was used to find data which contribution is the least for classification. Machine learning algorithms were used to find the best position to classify and we found best machine learning algorithm. Used feature extraction method was PCA(Principal Component Analysis). Used Machine learning models were five : SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model). As a result of research, back neck is suitable position for classification because classification rate of it was highest in every model. It was confirmed that Yaw data which is one of the IMU data has the smallest contribution to classification rate using PCA and there was no changes in classification rate after removal it. SVM, KNN are suitable for classification because their classification rate are higher than the others.

• Journal of Mechanical Science and Technology
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• v.17 no.5
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• pp.629-636
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• 2003

This paper introduces the developed geometry PIG (Pipeline Inspection Gauge), one of several ILI (In-Line Inspection) tools, which provide a full picture of the pipeline from only single pass, and has compact size of the electronic device with not only low power consumption but also rapid response of sensors such as calipers, IMU and odometer. This tool is equipped with the several sensor systems. Caliper sensors measure the pipeline internal diameter, ovality and dent size and shape with high accuracy. The IMU (Inertial Measurement Unit) measures the precise trajectory of the PIG during its traverse of the pipeline. The IMU also provide three-dimensional coordination in space from measurement of inertial acceleration and angular rate. Three odometers mounted on the PIG body provide the distance moved along the line and instantaneous velocity during the PIG run. The datum measured by the sensor systems are stored in on-board solid state memory and magnetic tape devices. There is an electromagnetic transmitter at the back end of the tool, the transmitter enables the inspection operators to keep tracking the tool while it travels through the pipeline. An experiment was fulfilled in pull-rig facility and was adopted from Incheon LT (LNG Terminal) to Namdong GS (Governor Station) line, 13 km length.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.68-77
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• 2021

In this paper, a GPS/MEMS IMU integrated navigation receiver module capable of operating in a high dynamic environment is designed and fabricated, and the results is confirmed. The designed module is composed of RF receiver unit, inertial measurement unit, signal processing unit, correlator, and navigation S/W. The RF receiver performs the functions of low noise amplification, frequency conversion, filtering, and automatic gain control. The inertial measurement unit collects measurement data from a MEMS class IMU applied with a 3-axis gyroscope, accelerometer, and geomagnetic sensor. In addition, it provides an interface to transmit to the navigation S/W. The signal processing unit and the correlator is implemented with FPGA logic to perform filtering and corrrelation value calculation. Navigation S/W is implemented using the internal CPU of the FPGA. The size of the manufactured module is 95.0×85.0×.12.5mm, the weight is 110g, and the navigation accuracy performance within the specification is confirmed in an environment of 1200m/s and acceleration of 10g.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.216-223
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• 2015

The autonomous underwater vehicle (AUV) is being widely researched in order to achieve superior performance when working in hazardous environments. This research focuses on using image processing techniques to estimate the AUV's egomotion and the changes in orientation, based on image frames from different time frames captured from a single high-definition web camera attached to the bottom of the AUV. A visual odometry application is integrated with other sensors. An internal measurement unit (IMU) sensor is used to determine a correct set of answers corresponding to a homography motion equation. A pressure sensor is used to resolve image scale ambiguity. Uncertainty estimation is computed to correct drift that occurs in the system by using a Jacobian method, singular value decomposition, and backward and forward error propagation.