• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.770-776
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• 2021

Positioning systems have been actively researched and developed over the past few years and have been used in many applications. This paper presents a method to determine a location in a large space using a sensor system consisting of an accelerometer and a single-axis gyroscope. In particular, to consider usability, a sensor device was loosely worn on the waist so that the experimental data could be used in practical applications. Based on the experimental results of circular tracks with radiuses of 1m and 3m, in this paper, an algorithm using the threshold of rotation angle was proposed and applied to the experimental results. A tracking experiment was performed on the grid-pattern track model. For raw sensor data, the average deviation between the final tracking point and the target point was approximately 15.2 m, which could be reduced to approximately 4.0 m using an algorithm applying the rotation angle threshold.

• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.27-32
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• 2012

Balance measurement is widely used in fields of physiotherapy, neurology, sport medicine and rehabilitation medicine. This paper presents the development of balance measurement and rehabilitation training system. To this end, we have developed a prototype system using an 3-axis accelerometer sensor attached to the under surface of balance board. As a results, the system is stable and shows a good degree of balance function. Also, the various patterns for rehabilitation training can be added easily. This system can be used for daily balance monitoring and will contribute to increasing the effectiveness of training.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.63-70
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• 2011

A bicycle is different from vehicles in the structure that a rider is fully exposed to the surrounding environment. Therefore, it needs to make use of prior information about local weather, air quality, trail road condition. Moreover, since it depends on human power for moving, it should acquire route property such as hill slope, winding, and road surface to improve its efficiency in everyday use. Recent mobile applications which are to be used during bicycle riding let us aware of the necessity of development of intelligent bicycles. This study aims to develop a riding state (up-hill, down-hill, accelerating, braking) recognition algorithm using a low-power wrist watch type embedded system which has 3-axis accelerometer and wireless communication capability. The developed algorithm was applied to 19 experimental riding data and showed more than 95% of correct recognition over 83.3% of the total dataset. The altitude and temperature sensor also in the embedded system mounted on the bicycle is being used to improve the accuracy of the algorithm. The developed riding state recognition algorithm is expected to be a platform technology for intelligent bicycle interface system.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.103-111
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• 2011

The research has increased the role of physical activity in promoting health and preventing chronic disease. Estimating algorithm of physical activity energy expenditure was implemented by using a tri-axial accelerometer motion detector of the SVM(Signal Vector Magnitude) of 3-axis(x, y, z). COUNT method has been proven through experiments of validity Freedson, Hendelman, Leenders, Yngve was implemented by applying the SVM method. A total of 10 participants(5 males and 5 females aged between 20 and 30 years). The activity protocol consisted of three types on treadmill; participants performed three treadmill activity at three speeds(3, 5, 8 km/h). These activities were repeated four weeks. Customized estimating algorithm for energy expenditure of physical activities were implemented with COUNT and SVM correlation between the data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.391-393
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• 2021

In this paper, we introduce a long short-term memory (LSTM)-based fall detection system using TensorFlow that can detect falls occurring in the elderly in daily living. 3-axis accelerometer data are aggregated for fall detection, and then three types of parameter are calculated. 4 types of activity of daily living (ADL) and 3 types of fall situation patterns are classified. The parameterized data applied to LSTM. Learning proceeds until the Loss value becomes 0.5 or less. The results are calculated for each parameter θ, SVM, and GSVM. The best result was GSVM, which showed Sensitivity 98.75%, Specificity 99.68%, and Accuracy 99.28%.

• Journal of Advanced Navigation Technology
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• v.13 no.2
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• pp.262-271
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• 2009

The purpose of this study was to propose the method to recognize gestures based on inertia sensor which recognizes the movements of the user using inertia sensor and lets the user enjoy the game by comparing the recognized movements with the pre-defined movements for the game contents production. Additionally, it was tried to provide users with various data entry methods by letting them wear small controllers using three-axis accelerator sensor. Users can proceed the game by moving according to the action list printed on the screen. They can proceed the experiential games according to the accuracy and timing of their movements. If they use multiple small wireless controllers together wearing them on the major parts of hands and feet and utilize the proposed methods, they will be more interested in the game and be absorbed in it.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.99-102
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• 2010

스마트폰의 도입으로 인하여 증강현실이 널리 알려짐에 따라 대중들의 관심은 이에 집중되고 있으며 휴대성으로 인하여 모바일 기기에서의 증강현실 연구가 하나의 흐름으로 자리 잡고 있다. 기존의 증강 현실 관련 응용 기술들이 많이 연구되고 있지만 실제 게임에서 사용되고 있는 인공 지능과 결합된 연구는 이루어지고 있지 않다. 본 논문에서는 스마트 폰의 기능중 하나인 3축 가속도 센서를 이용하여 증강 현실 환경에서 3D 에이전트의 상태를 동적으로 변환하는 인공 지능 알고리즘을 제안한다. 인공지능이 적용된 에이전트의 상태를 제어하기 위한 전통적인 방식으로서 사용자가 직접 입력해 주거나 이를 인식하는데 마커를 사용하여 해결하였다. 본 논문에서는 증강 현실 구현을 위해 마커리스 추적 기술을 사용하였고 3축 가속도 센서를 이용하여 동적으로 에이전트의 상태를 변환하도록 하였다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.5
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• pp.21-27
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• 2008

Despite recent studies on development of pulse diagnosis systems and needs for commercializing them, the reproducibility is one of the most controversial issues as ever. Because the pulse pressure value, which is one of the important parameters to evaluate reproducibility, is very vulnerable to moving artifacts, the reproducibility can not be obtained easily. In this paper, we suggested a moving artefacts detection system for a pulse diagnosis system so that a pulse diagnosis system can be robust to theses kinds of artefacts by excluding the contaminated parts from the pulse wave signal to be analyzed. This moving artifacts detection system was designed to consist of a three-axis accelerometer, an electromyography amplifier and a two-axis tilt sensor. To assess the suitability of the system, we examined the characteristics of each sensor's output signals with regard to the three specific motions such as extension, flexion and rotation. And, we also examined the each sensor's response to the high-frequency and low-frequency moving artifacts while the pulse wave signal was acquired from a pressure sensor for the pulse diagnosis. From these results, we could find that the response to subject's motions would be reflected in electromyography signal first, in accelerometer signals and in tilt sensor sequently. And, the facts that a stable pulse wave can be acquired in two seconds after high frequency or low frequency motions ended, were also found. Consequently, based on these findings, we set up some rules on the moving artifacts detection and designed an algorithm which is fit for our moving artifacts detection system.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.127-135
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• 2020

The purpose of this study is to develop a human activity recognition (HAR) system, which distinguishes 13 activities, including five activities commonly dealt with in conventional HAR researches and eight activities from the Eastern-Asian culture. The eight special activities include floor-sitting/standing, chair-sitting/standing, floor-lying/up, and bed-lying/up. We used a 3-axis accelerometer sensor on the wrist for data collection and designed a machine learning model for the activity classification. Data clustering through preprocessing and feature extraction/reduction is performed. We then tested six machine learning algorithms for recognition accuracy comparison. As a result, we have achieved an average accuracy of 99.7% for the 13 activities. This result is far better than the average accuracy of current HAR researches based on a smartwatch (89.4%). The superiority of the HAR system developed in this study is proven because we have achieved 98.7% accuracy with publically available 'pamap2' dataset of 12 activities, whose conventionally met the best accuracy is 96.6%.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1372-1374
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• 2015

본 논문에서는 농구 경기에서의 대표적 자세 중 Standing shoot, Jump shoot, Pass, Dribble, Lay-up shoot, 총 5가지 자세를 인식하기 위해 각 자세와 3축 가속도 값과의 상관관계를 보여주고 있다. 스마트폰에 내장되어 있는 가속도 센서로부터 데이터를 생성해주는 어플리케이션인 Sensor log를 활용하여 얻은 3축 가속도 값으로 수직, 수평축과 3축 가속도의 크기를 구해 Instance로 사용하였다. 위 데이터는 대표적인 데이터 마이닝 도구인 Weka tool을 이용하여 각 모션과 데이터 값의 상관관계를 확인하였고, 실험 결과 10-fold에서 평균 59.8%를 보였으나 Training set과 Test set의 결과 80.8%를 보였다.