• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.998-1013
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• 2013

Wireless wearable sensor networks have emerged as a promising technique for human motion tracking due to the flexibility and scalability. In such system several wireless sensor nodes being attached to human limb construct a wearable sensor network, where each sensor node including MEMS sensors (such as 3-axis accelerometer, 3-axis magnetometer and 3-axis gyroscope) monitors the limb orientation and transmits these information to the base station for reconstruction via low-power wireless communication technique. Due to the energy constraint, the high fidelity requirement for real time rendering of human motion and tiny operating system embedded in each sensor node adds more challenges for the system implementation. In this paper, we discuss such challenges and experiences in detail during the implementation of such system with wireless wearable sensor network which includes COTS wireless sensor nodes (Imote 2) and uses TinyOS 1.x in each sensor node. Since our system uses the COTS sensor nodes and popular tiny operating system, it might be helpful for further exploration in such field.

• 대한안전경영과학회지
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• 제14권1호
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• pp.87-93
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• 2012

The purpose of this study suggests a personalized algorithm of physical activity energy expenditure prediction through comparison and analysis of individual physical activity. The research for a 3-axial accelerometer sensor has increased the role of physical activity in promoting health and preventing chronic disease has long been established. 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). A total of 10 participants(5 males and 5 females aged between 20 and 30 years). The activities 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.

• Imaging Science in Dentistry
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• 제32권3호
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• pp.159-165
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• 2002

Purpose: The aim of this study was to introduce a method of obtaining the oscillation graphs of the dental x-ray tube heads relative to time using an accelerometer. Materials and Methods: An Accelerometer, Piezotron type 8704B25 (Kistler Instrument Co., Amherst, NY, USA) was utilized to measure the horizontal oscillation of the x-ray tube head immediately after positioning the tube head for an intraoral radiograph. The signal from the sensor was transferred to a dynamic signal analyzer, which displayed the magnitude of the acceleration on the Y-axis and time lapse on the X -axis. The horizontal oscillation of the tube head was measured relative to time, and the settling time was also determined on the basis of the acceleration graphs for 6 wall type, 5 floor-fixed type, and 4 mobile type dental x-ray machines. Results : The oscillation graphs showed that tube head movement decreased rapidly over time. The settling time varied with x-ray machine types. Wall-type x-ray machines had a settling time of up to 6 seconds, 5 seconds for fixed floor-types, and 1 I seconds for the mobile-types. Conclusion: Using an accelerometer, we obtained the oscillation graphs of the dental x-ray tube head relative to time. The oscillation graph with time can guide the operator to decide upon the optimum exposure moment after x-ray tube head positioning for better radiographic resolution.

• 한국전기전자재료학회논문지
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• 제24권1호
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• pp.81-88
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• 2011

A real time monitoring system from a PC has been developed which can be accessed through transmitted data, which incorporates an established low powered transport system equipped with a single chip combined with wireless sensor network technology from a three-axis acceleration sensor. In order to distinguish between static posture and dynamic posture, the extracted parameter from the rapidly transmitted data needs differentiation of movement and activity structures and status for an accurate measurement. When results interpret a static formation, statistics referring to each respective formation, known as the K-mean algorithm is utilized to carry out a determination of detailed positioning, and when results alter towards dynamic activity, fuzzy algorithm (fuzzy categorizer), which is the relationship between speed and ISVM, is used to categorize activity levels into 4 stages. Also, the ISVM is calculated with the instrumented acceleration speed on the running machine according to various speeds and its relationship with kinetic energy goes through correlation analysis. With the evaluation of the proposed system, the accuracy level stands at 100% at a static formation and also a 96.79% accuracy with kinetic energy and we can easily determine the energy consumption through the relationship between ISVM and kinetic energy.

• 센서학회지
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• 제20권2호
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• pp.137-144
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• 2011

Wireless sensor network(WSN) has the potential to greatly effect many aspects of u-healthcare. By outfitting the potential with WSN, wearable sensor node can collects real-time data on physiological status and transmits through base station to server PC. However, there is a significant gap between WSN and healthcare. WSN has the limited resource about computing capability and data transmission according to bio-sensor sampling rates and channels to apply healthcare system. If a wearable node transmits ECG and accelerometer data of 4 channel sampled at 100 Hz, these data may occur high loss packets for transmitting human activity and ECG to server PC. Therefore current wearable sensor nodes have to solve above mentioned problems to be suited for u-healthcare system. Most WSN based activity and ECG monitoring system have been implemented some algorithms which are applied for signal vector magnitude(SVM) algorithm and ECG noise algorithm in server PC. In this paper, A wearable sensor node using integrated ECG and 3-axial accelerometer based on wireless sensor network is designed and developed. It can form multi-hop network with relay nodes to extend network range in WSN. Our wearable nodes can transmit 1-channel activity data processed activity classification data vector using SVM algorithm to 3-channel accelerometer data. ECG signals are contaminated with high frequency noise such as power line interference and muscle artifact. Our wearable sensor nodes can remove high frequency noise to clear original ECG signal for healthcare monitoring.

• 한국정밀공학회지
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• 제28권11호
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• pp.1330-1338
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• 2011

Social activities of the elderly have been increasing as our society progresses toward an aging society. As their activities increase, so does the occurrence of falls that could lead to fractures. Falls are serious health hazards to the elderly. Therefore, development of a device that can detect fall accidents and prevent fracture is essential. In this study, we developed a portable fall detection system for the fracture prevention system of the elderly. The device is intended to detect a fall and activate a second device such as an air bag deployment system that can prevent fracture. The fall detection device contains a 3-axis acceleration sensor and two 2-axis tilt sensors. We measured acceleration and tilt angle of body during fall and activities of daily(ADL) living using the fall detection device that is attached on the subjects'. Moving mattress which is actuated by a pneumatic system was used in fall experiments and it could provide forced falls. Sensor data during fall and ADL were sent to computer and filtered with low-pass filter. The developed fall detection device was successful in detecting a fall about 0.1 second before a severe impact to occur and detecting the direction of the fall to provide enough time and information for the fracture preventive device to be activated. The fall detection device was also able to differentiate fall from ADL such as walking, sitting down, standing up, lying down, and running.

• 재활복지공학회논문지
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• 제6권1호
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• pp.1-8
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• 2012

본 논문에서는 3축 가속도 동작 감지기를 이용하여 신체활동 에너지 소비량 예측 알고리즘을 구현 하였다. 피험자 33명(남성: 15, 여성: 18명)을 대상으로 트레드밀에서 호흡가스분석기, 3축 가속도 동작 감지기(피트미터)를 허리와 손목에 착용 후 2 km/h ~ 11 km/h 까지 각 단계별 2분 수행 후, 1 km/h 씩 증가 시키면 실험을 진행하였다. 3축 가속도 동작 감지기의 x, y, z축 출력 값을 하나의 대표 값으로 처리하는 신호벡터크기(Signal Vector Magnitude: SVM)와 산소소비량과의 회귀분석을 통하여 신체활동 에너지 소비량 예측 알고리즘을 구현 하였다. 허리, 손목, 허리와 손목의 3축 가속도 동작 감지기 착용 위치에 따라 알고리즘을 구현하고 각각의 알고리즘 별로 비교하여 신체활동의 특성에 따라 선택적으로 이용할 수 있도록 구현 하였다.

• 재활복지공학회논문지
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• 제5권1호
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• pp.27-33
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• 2011

3축 가속도 동작감지기를 이용하여 x, y, z축에 대한 가속도 합인 SVM(Signal Vector Magnitude)를 적용한 신체활동 에너지 소비량과 신체활동 강도 예측 알고리즘을 구현하였다. 신체 건강한 20~30대 성인 남성 5명, 여성 5명을 대상으로 골반 장골능에 엑티그라프(LLC, USA)와 피트미터(Fit.life. korea)를 착용하고 트레드밀위에서 3단계 신체활동(걷기: 3km/h, 빨리 걷기: 5km/h, 러닝: 8km/h)을 수행하였다. 각 신체활동 단계별로 7분간 신체활동을 수행하고 5분간 휴식을 통하여 각 신체활동 단계별로 안정화된 상태에서 실험하였다. 이러한 실험을 1주 간격으로 4주간 반복 실험을 진행하였다. 실험결과 얻어진 엑티그라프와 피트미터의 600여개 데이터 상관관계를 분석하여 METs와 kcal 그리고 신체활동 강도를 구분하는 알고리즘을 구현하였다.

• 한국콘텐츠학회논문지
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• 제11권12호
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• pp.103-111
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• 2011

최근 만성질환을 예방하고 건강을 증진시킬 목적으로 신체활동에 대한 중요성이 인식되면서 신체활동 연구가 활발히 진행되고 있다. 본 연구에서는 3축 가속도 동작감지기 x, y, z축에 대한 $cm/s^2$의 가속도 합인 SVM(Signal Vector Magnitude)를 이용하여 신체활동 에너지 소비량 알고리즘을 구현하였다. 기존 실험을 통해 타당도가 입증된 COUNT 방식의 Freedson, Hendelman, Leenders, Yngve 알고리즘에 SVM 방식을 적용하여 구현 하였다. COUNT와 SVM 상관관계 분석을 위하여 총 10명의(성인 남성 5명, 여성 5명, 20 ~ 30 대) 피험자를 대상으로 실험을 진행하였다. 피험자는 트레드밀위에서 3단계 신체활동 (걷기: 3km/h, 빨리 걷기: 5km/h, 러닝: 8km/h)을 1주 간격으로 4주 간 반복 실험을 진행하였다. 실험결과 얻어진 COUNT와 SVM의 간의 상관관계를 분석하여 다양한 신체활동에 따른 맞춤형 에너지 측정 알고리즘을 구현하였다.

• 한국운동역학회지
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• 제19권1호
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• pp.159-166
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• 2009

본 연구의 목적은 실외 보행 실험 시 사용하기에 간편한 가속도계를 이용하여 보행 이벤트를 자동으로 검출하는 알고리즘을 개발하고 검증하는 것이다. 개발된 알고리즘은 신발의 발등에 부착된 3축 가속도계의 가속도의 총합과 보행 진행 방향(x축) 가속도를 이용하였다. 가속도 총합은 착지 시점의 검출에, x축 가속도는 이지 시점의 검출에 각각 사용되었다. 7명의 피험자가 느린 보행 속도 선호 보행 속도 빠른 보행 속도로 보행 실험을 수행하였고, 개발된 알고리즘의 검증을 위해 지면반력기를 포함한 3차원동작분석시스템과 동시에 실시되었다. 지면반력기를 이용한 보행 시점을 기준으로, 기존에 발표된 동작 자료만을 이용한 알고리즘을 통하여 얻어진 보행 시점도 함께 비교 하였다. 그 결과, 고안된 알고리즘의 정확도는 지면반력기를 이용한 값에 평균, 착지시점은 $22.33{\pm}17.45ms$, 이지시점은 $26.82{\pm}14.78ms$의 차이가 났고, 그 오차의 패턴이 일관적으로 20ms가량 먼저 검출되는 경향이 있었다. 일반적으로 트레드밀 실험에서 많이 사용되는 동작데이터를 통한 보행 시점의 비교에서도 크게 차이를 보이지 않았다. 그러므로 개발된 알고리즘은 실외 실험의 보행 시점 검출에 이용할 수 있을 것으로 판단된다. 추후 연구로는 현재의 가속도계만으로 보행 시점뿐 만아니라, 중력가속도 성분을 제거하여 보행 공간변인의 추출이 필요할 것이다.