• The Journal of the Acoustical Society of Korea
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• v.29 no.1E
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• pp.11-27
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• 2010

Nowadays, ultrasound Doppler imaging is widely used in assessing cardiovascular functions in the human body. However, a major drawback of ultrasonic Doppler methods is that they can provide information on blood flow velocity along the ultrasound beam propagation direction only. Thus, the blood flow velocity is estimated differently depending on the angle between the ultrasound beam and the flow direction. In order to overcome this limitation, there have been many researches devoted to estimating both axial and lateral velocities. The purpose of this article is to survey various two-dimensional velocity estimation methods in the context of Doppler imaging. Some velocity vector estimation methods can also be applied to determine tissue motion as required in elastography. The discussion is mainly concerned with the case of estimating a two-dimensional in-plane velocity vector involving the axial and lateral directions.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.156-162
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• 2022

This study estimates the relative position between body segments using segment orientation and segment-to-joint center (S2J) vectors. In many wearable motion tracking technologies, the S2J vector is treated as a constant based on the assumption that rigid body segments are connected by a mechanical ball joint. However, human body segments are deformable non-rigid bodies, and they are connected via ligaments and tendons; therefore, the S2J vector should be determined as a time-varying vector, instead of a constant. In this regard, our previous study (2021) proposed a method for determining the time-varying S2J vector from the learning dataset using a regression method. Because that method uses a deformation-related variable to consider the deformation of S2J vectors, the optimal variable must be determined in terms of estimation accuracy by motion and segment. In this study, we investigated the effects of deformation-related variables on the estimation accuracy of the relative position. The experimental results showed that the estimation accuracy was the highest when the flexion and adduction angles of the shoulder and the flexion angles of the shoulder and elbow were selected as deformation-related variables for the sternum-to-upper arm and upper arm-to-forearm, respectively. Furthermore, the case with multiple deformation-related variables was superior by an average of 2.19 mm compared to the case with a single variable.

• Journal of Korea Game Society
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• v.9 no.5
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• pp.105-115
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• 2009

This article proposes a design and implementation methodology of a gesture-based interface for augmented reality games. The topic of gesture-based augmented reality games is a promising area in the immersive future games using human body motions. However, due to the instability of the current motion recognition technologies, most previous development processes have introduced many ad hoc methods to handle the shortcomings and, hence, the game architectures have become highly irregular and inefficient This article proposes an efficient development methodology for gesture-based augmented reality games through prototyping a table tennis game with a gesture interface. We also verify the applicability of the prototyping mechanism by implementing and demonstrating the augmented reality table tennis game. In the experiments, the implemented prototype has stably tracked real rackets to allow fast movements and interactions without delay.

• Science of Emotion and Sensibility
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• v.24 no.4
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• pp.129-138
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• 2021

This study aimed to determine the effects of the shape and attachment position of stretchable textile sensors coated with carbon nanotube on their performance when used to measure children's joint movements. Moreover, the child-safe requirements for fabric motion sensors are established. The child participants were advised to wear integrated clothing equipped with the sensors of various shapes (rectangular and boat-shaped) and attachment positions (at the knee and elbow joints or 4 cm below the joints). The voltage change induced by the elongation and contraction of the fabric sensors was determined for arm and leg flexion-extension motions at 60 deg/s (three measurements of 10 repeats each for 60°and 90°angles, for a total of 60 repetitions). Their dependability was determined by comparing the fabric motion sensors to the associated acceleration sensors. The experimental results indicate that the rectangular-shaped sensor affixed 4 cm below the joint is the most effective fabric motion sensor for measuring children's arm and leg motions. In this study, we designed a textile sensor capable of tracking children's joint motion and analyzed the sensor shape and attachment position on motion sensing clothing. We demonstrated that flexible fabric sensors integrated into garments may be used to detect the joint motions of the human body.

• Smart Media Journal
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• v.12 no.7
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• pp.59-67
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• 2023

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The collected dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.

• Progress in Medical Physics
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• v.23 no.2
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• pp.91-98
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• 2012

Verification of internal organ motion during treatment and its feedback is essential to accurate dose delivery to the moving target. We developed an offline based internal organ motion verification system (IMVS) using cine EPID images and evaluated its accuracy and availability through phantom study. For verification of organ motion using live cine EPID images, a pattern matching algorithm using an internal surrogate, which is very distinguishable and represents organ motion in the treatment field, like diaphragm, was employed in the self-developed analysis software. For the system performance test, we developed a linear motion phantom, which consists of a human body shaped phantom with a fake tumor in the lung, linear motion cart, and control software. The phantom was operated with a motion of 2 cm at 4 sec per cycle and cine EPID images were obtained at a rate of 3.3 and 6.6 frames per sec (2 MU/frame) with $1,024{\times}768$ pixel counts in a linear accelerator (10 MVX). Organ motion of the target was tracked using self-developed analysis software. Results were compared with planned data of the motion phantom and data from the video image based tracking system (RPM, Varian, USA) using an external surrogate in order to evaluate its accuracy. For quantitative analysis, we analyzed correlation between two data sets in terms of average cycle (peak to peak), amplitude, and pattern (RMS, root mean square) of motion. Averages for the cycle of motion from IMVS and RPM system were $3.98{\pm}0.11$ (IMVS 3.3 fps), $4.005{\pm}0.001$ (IMVS 6.6 fps), and $3.95{\pm}0.02$ (RPM), respectively, and showed good agreement on real value (4 sec/cycle). Average of the amplitude of motion tracked by our system showed $1.85{\pm}0.02$ cm (3.3 fps) and $1.94{\pm}0.02$ cm (6.6 fps) as showed a slightly different value, 0.15 (7.5% error) and 0.06 (3% error) cm, respectively, compared with the actual value (2 cm), due to time resolution for image acquisition. In analysis of pattern of motion, the value of the RMS from the cine EPID image in 3.3 fps (0.1044) grew slightly compared with data from 6.6 fps (0.0480). The organ motion verification system using sequential cine EPID images with an internal surrogate showed good representation of its motion within 3% error in a preliminary phantom study. The system can be implemented for clinical purposes, which include organ motion verification during treatment, compared with 4D treatment planning data, and its feedback for accurate dose delivery to the moving target.