• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.33-39
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• 2019

In this paper, we propose a parametric model for analyzing the motion information obtained from the acceleration sensors to measure the activity of the human body. The motion of the upper body and the lower body does not occur at the same time, and the motion analysis method using a single motion sensor involves a lot of errors. In this study, the 3-axis accelerometer is attached to the arms and legs, the body's activity data are measured, the momentum of the arms and legs are calculated for each channel, and the linear predictive coefficient is obtained for each channel. The periodicity of the upper body and the lower body is determined by analyzing the correlation between the channels. The linear predictive coefficient and the periodic value are used as data to measure the type of exercise and the amount of exercise. In the proposed method, we measured four types of movements such as walking, stair climbing, slow hill climbing, and fast hill descending. In order to verify the usefulness of the parameters, the recognition results are presented using the linear predictive coefficient and the periodic value for each motion as the neural network input.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1649-1654
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• 2021

In this paper, we introduce a regularization of long short-term memory (LSTM) based fall detection system using TensorFlow that can detect falls that can occur in the elderly. Fall detection uses data from a 3-axis acceleration sensor attached to the body of an elderly person and learns about a total of 7 behavior patterns, each of which is a pattern that occurs in daily life, and the remaining 3 are patterns for falls. During training, a normalization process is performed to effectively reduce the loss function, and the normalization performs a maximum-minimum normalization for data and a L2 regularization for the loss function. The optimal regularization conditions of LSTM using several falling parameters obtained from the 3-axis accelerometer is explained. When normalization and regularization rate λ for sum vector magnitude (SVM) are 127 and 0.00015, respectively, the best sensitivity, specificity, and accuracy are 98.4, 94.8, and 96.9%, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.149-156
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• 2013

With a rapid growth of smartphone technologies, various applications are developed and diffused actively nowadays. Especially, interesting applications using camera module in a smartphone are developed continuously, mobile users are able to use various useful mobile services in humdrum life. In this paper, we design and implement measurement algorithms which precisely measure the object taken by the camera module in a smartphone. We use 3-axis gyro accelerometer sensor in a smartphone to get the distance, incline and rotation angle in a real time when we take a picture of shooting object and can obtain precise size of it in the picture image. The measurement algorithms proposed in this paper are analyzed and evaluated by a simulation study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.510-516
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• 2019

In this paper, a snowboard simulator that measures the user's motion and makes the user feel physical changes and enjoy actual snowboarding was developed. The speed and direction of the snowboard are determined by the user's center of gravity. The developed simulator is equipped with four springs on the snowboard plate, so that the slope can change according to the change in the user's weight center and be felt directly. The slope due to the change in the center of gravity of the user is measured using a three-axis acceleration sensor. The friction of the slope generated by the rotation of the snowboard is made possible by the user using the BLDC motor, and the rotation of the snowboard is measured using the hole sensor. For rapid data processing of the simulator, two MCUs are used to transfer the measured data to the PC using the acceleration sensor and motor separately. The developed simulator can experience slopes and friction of the slope directly, and wear measured data and HMD to enjoy more realistic snowboarding.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2105-2110
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• 2008

Embedding the compact wearable units to monitor the health status of a person has been analysed as a convenient solution for the home health care. This paper presents a method to detect fall from the other activities of daily living and also to classify those activities. This kind of ambulatory monitoring of the elderly and people with limited mobility can not only provide their general health status but also alarms whenever an emergency such as fall or gait has been occurred and a help is needed. A timely assistance in such a situation can reduce the loss of life. This work shows a detailed analysis of the data received from a chest worn sensor unit embedding a 3-axis accelerometer and depicts which features are important for the classification of human activities. How to arrange and reduce the features to a new feature set so that it can be classified using a simple classifier and also improving the classification resolution. Principal component analysis (PCA) has been used for modifying the feature set and afterwards for reducing the size of the same. Finally a Neural network classifier has been used to analyse the classification accuracies. The accuracy for detection of fall events was found to be 86%. The overall accuracy for the classification of Activities or daily living (ADL) and fall was around 94%.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.6
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• pp.182-190
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• 2004

This paper presents reconstruction analysis of vehicle trajectory using records of a developed black box, and results of validation tests. For reconstruction of vehicle trajectory, the black box records the longitudinal and lateral accelerations and yaw-rate of vehicle during a pre-defined time period before and after the accident. One 2-axis accelerometer is used for measuring accelerations, and one vibrating structure type gyroscope is used for measuring yaw-rate of vehicle. The vehicle's planar trajectory can be reconstructed by integrating twice accelerations along longitudinal and lateral directions with yaw-rate values. However, there may be many kinds of errors in sensor measurements. The causes of errors are as follows: mis-alignment, low frequency offset drift, high frequency noise, and projecting 3-dimensional motion into 2-dimensional motion. Therefore, some procedures are taken for error compensation. In order to evaluate the reliability and the accuracy of trajectory reconstruction results, the black box was mounted on a passenger car. The vehicle was driven and tested along various specified lanes. Through the tests, the accuracy and usefulness of the reconstruction analysis have been validated.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.53-62
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• 2017

In this paper, we present the score classification accuracy of BBS(Berg Balance Scale) which is the most commonly used balance evaluation tool using machine learning. Data acquisition was performed using the Noraxon system and an inertial sensor of Noraxon system was attached to the body in 8 locations (left and right ankle, left and right upper buttocks, left and right wrists, back, forehead). Based on the 3-axis accelerometer of the inertial sensor, the feature vector STFT(Short Time Fourier Transform) and SAM(Signal Area Magnitude) were extracted. Then, the items of the BBS were divided into static movement and dynamic movement depending on the operation characteristics, and the feature vectors were selected according to the sensor attachment positions which affect the score for each item of the BBS. Feature vectors selected for each item of BBS were classified using GMM(Gaussian Mixture Model). As a result of the accuracy calculation for 40 subjects, 55.5%, 72.2%, 87.5%, 50%, 35.1%, 62.5%, 43.3%, 58.6%, 60.7%, 33.3%, 44.8%, 89.2%, 51.8%, 85.1%, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.199-206
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• 2012

Attempt to use as a marker of natural objects directly in the real world, but there is a way to use the accelerometer of the smartphone, to convert the Frontal-View virtual, because it asks only the pitch of the camera, from the side there is a drawback that can not be converted to images. The proposed algorithm, to obtain the rotation matrix of axis 3 pitch, roll, yaw, we set the reference point of the yaw of the target image. Then, to compensate for the rotation matrix to determine Myon'inji any floor, wall, the ceiling of the target image. Finally, to obtain the homography matrix for obtaining the Frontal-View to account for the difference between the gyro sensor coordinate system and image coordinate system, so we can get the Frontal-View from the captured images through the projection transformation was designed. Was tested to convert Frontal-View the picture was taken in an environment smartphone environment surrounding floor, walls and ceiling in order to evaluate the conversion program Frontal-View has been implemented, in this paper, design and The conversion algorithm implementation, it was confirmed that to convert a regular basis Frontal-View footage taken from multiple angles.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.76-83
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• 2022

With the recent development of IT technology, research and interest in various biosignal measuring devices is increasing. As an aging society is in full swing, research on the elderly population using IT-related technologies is continuously developing. This study is about the development of life pattern detection and fall detection algorithm, which is one of the medical service areas for the elderly, who are rapidly developing as they enter a super-aged society. This study consisted of a system using a 3-axis accelerometer and an electrocardiogram sensor, collected data, and then analyzed the data. It was confirmed that behavioral patterns could be classified from the actual research results. In order to evaluate the usefulness of the human activity monitoring system implemented in this study, experiments were performed under various conditions, such as changes in posture and walking speed, and signal magnitude range and signal vector magnitude parameters reflecting the acceleration of gravity of the human body and the degree of human activity. was extracted. And the possibility of discrimination according to the condition of the subject was examined by these parameter values.

• Journal of Animal Science and Technology
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• v.65 no.4
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• pp.759-766
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• 2023

Visual estrus observation can only be confirmed at a rate of 50%-60%, which is lower than that obtained using a biosensor. Thus, the use of biosensors provides more opportunities for artificial insemination because it is easier to confirm estrus than by visual observation. This study determines the accuracy of estrus prediction using a ruminoreticular biosensor by analyzing ruminoreticular temperature during the estrus cycle and measuring changes in body activity. One hundred and twenty-five Hanwoo cows (64 with a ruminal biosensor in the test group and 61 without biosensors in the control group) were studied. Ruminoreticular temperatures and body activities were measured every 10 min. The first service of artificial insemination used gonadotropin-releasing hormone (GnRH)-based fixed-time artificial insemination protocol in the control and test groups. The test group received artificial insemination based on the estrus prediction made by the biosensor, and the control group received artificial insemination according to visual estrus observation. Before artificial insemination, the ruminoreticular temperature was maintained at an average of 38.95 ± 0.05℃ for 13 h (-21 to -9 h), 0.73℃ higher than the average temperature observed at -48 h (38.22 ± 0.06℃). The body activity, measured using an indwelling 3-axis accelerometer, averaged 1502.57 ± 27.35 for approximately 21 h from -4 to -24 h before artificial insemination, showing 203 indexes higher body activity than -48 hours (1299 ± 9.72). Therefore, using an information and communication techonology (ICT)-based biosensor is highly effective because it can reduce the reproductive cost of a farm by accurately detecting estrus and increasing the rate of estrus confirmation in cattle.