• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.937-943
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• 2016

Vertical velocity is critical in many areas, such as the control of unmanned aerial vehicles, fall detection, and virtual reality. Conventionally, the integration of GPS (Global Positioning System) with an IMU (Inertial Measurement Unit) was popular for the estimation of vertical components. However, GPS cannot work well indoors and, more importantly, has low accuracy in the vertical direction. In order to overcome these issues, IMU-barometer integration has been suggested instead of IMU-GPS integration. This paper proposes a new complementary filter for the estimation of vertical velocity based on IMU-barometer integration. The proposed complementary filter is designed to minimize drift error in the estimated velocity by adding PID control in addition to a zero velocity update technique.

• Journal of Biomedical Engineering Research
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• v.43 no.3
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• pp.177-184
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• 2022

The purpose of this study was to present the IMU sensor based trunk stabilization exercise and to evaluate the changes in the muscle activity and thickness with non-specific low back pain patients (N=30). They were classified into two groups; lumbar stabilization exercise using IMU sensor (ILS), (n₁=20) and general lumbar stabilization exercise (GLS), (n₂=10). By comparing the difference between pre and post intervention via trunk muscle activity and muscle thickness, the significant differences were identified. Muscle activity was measured on external oblique (EO), internal oblique (IO), and multifidus (MF) by using surface electromyography (sEMG). Muslce thickness was measured on external oblique, internal oblique, transverse abdominis (TrA), and multifidus (MF) by using ultrasonography. sEMG activity was recorded at right side-bridge position. Each group performed the proposed lumbar stabilization exercise for 30 minutes a day, 5 times a week for 4 weeks. Trunk muscle activity was observed with a significant increase in the IO of ILS (p<.05) and a decrease in the MF of GLS (p<.05). Trunk muscle thickness was significantly increased in left EO and both IO of GLS (p<.05), and also significant increased right EO, both IO, both TrA, and both MF of the ILS (p<.05). In the future, a convergence approach of rehabilitation and engineering is needed to select a sensor suitable for rehabilitation purposes, study the validity and reliability of data, and produce appropriate rehabilitation contents.

• Physical Therapy Korea
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• v.30 no.2
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• pp.102-109
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• 2023

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model's performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.119-126
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• 2020

The purpose of this study is to identify trends in the type and method of Inertial Measurement Unit (IMU) by investigating studies on the type and method of convergence study of the IMU by systematic review. The study was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. 23 studies that meet the selection criteria were selected from 630 studies identified by three databases. As a result of this study, showed that various research using IMU was being conducted around the world, and the type of IMU was strap, full body suit, belt, wrist watch, shoes and glove. Among them, the number of strap-type IMUs was the largest at 11. The IMU's strengths were simplicity, real-time data collection and ease of application, which were used as measurement methods such as task, walking, and range of joint. The result of this study is expected to be used as basic data for experts in the medical and rehabilitation fields that conduct IMU research.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.18-20
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• 2021

In order to prevent collision accidents of ships, which has been increasing recently, research on artificial intelligence-based autonomously operated ships (Maritime Autonomous Surface Ship, MASS) is underway. However, most of the studies related to autonomous ships mainly target medium-to-large ships due to the size and cost of the autonomous navigation system, and the sensors used here have a problem in that it is difficult to mount them on small ships. Therefore, this paper provides a path tracking system equipped with GPS and IMU sensors for autonomous operation of small ships. GPS and IMU sensors are utilized to determine the exact position of the vessel, which allows the proposed system to manually control the small vessel model to create a path and then when the small vessel travels the same path. Use the Pure Pursuit algorithm to follow the path. As a result, In this research, it is expected that a lightweight and low-cost sensor can be used to develop an autonomous operation system for small ships at low cost.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.661-662
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• 2008

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.1
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• pp.15-21
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• 2015

In this paper, a behavior notification system of the harness of a guide dog is implemented for a blind person to get helps for environmental and situational awareness while walking with the guide dog. IMU modules is attached on the guide dog's harness saddle and the acceleration sensor belt is mounted on its thigh. Gait estimation and behavior judgement are performed by recording and analyzing the outputs of the sensors. Performance analysis for seven different kinds of behaviors has been done. The seven different behaviors, which the guide dog recognizes, are descending stairs, climbing stairs, uphill, downhill, stop, flat road, and selective disobedience. Results for the performance analysis show that the average success rate of the behavior rule estimation of harness of the guide dog is 92.78% and the behavior notification system can be effectively used in real situations.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.81-83
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• 2015

본 논문은 방송의 가상스튜디오 제작 환경에서 많이 사용되는 회전 또는 스크롤 메뉴를 진행자가 직접 휠(회전보드)을 움직여, 진행자와 그래픽과의 자연스러운 상호작용이 가능한 인터랙티브 가상현실 방송 제작 어플리케이션을 제안한다. 이를 위해, 우리는 물리적인 휠의 움직임을 인지할 수 있도록 관성측정장치(IMU: Inertial Measurement Unit)를 사용하였으며, IMU 센서가 부착된 휠을 크로마키로 처리하기 위해 푸른색의 페인팅된 물리적인 휠을 사용하였다. 본 어플리케이션을 통해서 가상스튜디오의 연기자는 물리적인 휠의 움직임을 느끼면서 휠을 회전시킴으로써 별도의 연습이나 훈련 없이도 직관적으로 회전하는 여러 타입의 가상 그래픽 메뉴를 제어할 수 있다. 우리는 상하 스크롤, 원형 회전, 스크롤 연동형 메뉴 어플리케이션들을 개발하였으며, 이것을 방송에 적용하여, 연기자와 휠에 연동한 그래픽과의 인터랙션이 자연스럽게 합성됨을 확인하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.10
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• pp.921-927
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• 2013

This paper presents a user interface for vision based indirect teaching of a robotic manipulator with Kinect and IMU (Inertial Measurement Unit) sensors. The user interface system is designed to control the manipulator more easily in joint space, Cartesian space and tool frame. We use the skeleton data of the user from Kinect and Wrist-mounted IMU sensors to calculate the user's joint angles and wrist movement for robot control. The interface system proposed in this paper allows the user to teach the manipulator without a pre-programming process. This will improve the teaching time of the robot and eventually enable increased productivity. Simulation and experimental results are presented to verify the performance of the robot control and interface system.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.800-808
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• 2008

This paper presents a localization algorithm of the outdoor wheeled mobile robot using the sensor fusion method based on indirect Kalman filter(IKF). The wheeled mobile robot considered with in this paper is approximated to the two wheeled mobile robot. The mobile robot has the IMU and encoder sensor for inertia positioning system and GPS. Because the IMU and encoder sensor have bias errors, divergence of the estimated position from the measured data can occur when the mobile robot moves for a long time. Because of many natural and artificial conditions (i.e. atmosphere or GPS body itself), GPS has the maximum error about $10{\sim}20m$ when the mobile robot moves for a short time. Thus, the fusion algorithm of IMU, encoder sensor and GPS is needed. For the sensor fusion algorithm, we use IKF that estimates the errors of the position of the mobile robot. IKF proposed in this paper can be used other autonomous agents (i.e. UAV, UGV) because IKF in this paper use the position errors of the mobile robot. We can show the stability of the proposed sensor fusion method, using the fact that the covariance of error state of the IKF is bounded. To evaluate the performance of proposed algorithm, simulation and experimental results of IKF for the position(x-axis position, y-axis position, and yaw angle) of the outdoor wheeled mobile robot are presented.