• Journal of Korean Association for Spatial Structures
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• v.7 no.6
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• pp.83-90
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• 2007

Today, many studies are progressed about source of vibration oscillator in reinforcement concrete structures. Source of vibration oscillator is load when it is happen from walking inhabitant. It is transmitted to another inhabitant through reinforcement concrete plate, and it is type of elastic wave. Those descriptions are ram wave and primary wave, secondary wave, and the are through the surface and inside plate. Analysis studies of those waves are used to piezoelectric materials. But, they are difficult to 3 axial type of transmitting elastic wave in concrete element. In this study, a fundamental study for source estimations of vibration oscillator using micro accelerometer are discussed.

• Journal of Sensor Science and Technology
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• v.20 no.2
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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.

• Journal of the Korean Society of Physical Medicine
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• v.13 no.3
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• pp.19-25
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• 2018

PURPOSE: Most gait assessment tools are expensive and require controlled laboratory environments. Tri-axial accelerometers have been used in gait analysis as an alternative to laboratory assessments. Many smartphones have added an accelerometer, making it possible to assess spatio-temporal gait parameters. This study was conducted to confirm the reliability and validity of a smartphone-based accelerometer at quantifying spatio-temporal gait parameters of stroke patients when attached to the body. METHODS: We measured gait parameters using a smartphone accelerometer and gait parameters through the GAITRite analysis system and the reliability and validity of the smartphone-based accelerometer for quantifying spatio-temporal gait parameters for stroke patients were then evaluated. Thirty stroke patients were asked to walk at self-selected comfortable speeds over a 10 m walkway, during which time gait velocity, cadence and step length were computed from smartphone-based accelerometers and validated with a GAITRite analysis system. RESULTS: Smartphone data was found to have excellent reliability ($ICC2,1{\geq}.98$) for measuring the tested parameters, with a high correlation being observed between smartphone-based gait parameters and GAITRite analysis system-based gait parameters (r = .99, .97, .41 for gait velocity, cadence, step length, respectively). CONCLUSION: The results suggest that specific opportunities exist for smartphone-based gait assessment as an alternative to conventional gait assessment. Moreover, smartphone-based gait assessment can provide objective information about changes in the spatio-temporal gait parameters of stroke subjects.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.199-206
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• 2013

An emergency monitoring system for the elderly, which uses acceleration data measured with an accelerometer, angular velocity data measured with a gyroscope, and heart rate measured with an electrocardiogram, is proposed. The proposed fall detection algorithm uses multiple parameter combinations in which all parameters, calculated using tri-axial accelerations and bi-axial angular velocities, are above a certain threshold within a time period. Further, we propose an emergency detection algorithm that monitors the movements of the fallen elderly person, after a fall is detected. The results show that the proposed algorithms can distinguish various types of falls from activities of daily living with 100% sensitivity and 98.75% specificity. In addition, when falls are detected, the emergency detection rate is 100%. This suggests that the presented fall and emergency detection method provides an effective automatic fall detection and emergency alarm system. The proposed algorithms are simple enough to be implemented into an embedded system such as 8051-based microcontroller with 128 kbyte ROM.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.94-102
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• 2005

In this paper, three axial accelerometer was used to develop a small sensor module, which was attached to human body to calculate the acceleration in gravity direction by human motion, when it was positioned in any direction. To measure its wearer's walking or running motion using the sensor module, the acquired sensor data was pre-processed to enable its quantitative analysis. The acquired digital data was transformed to orthogonal coordinate value in three dimension and calculated to be single scalar acceleration data in gravity direction and normalized to be physical unit value. The normalized sensor data was used to detect walking pattern and calculate their step counts. Developed algorithm was implemented in the form of PDA application. The accuracy of the developed sensor to detect step count was about 97% in laboratory experiment.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.79-84
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• 2011

The purpose of this study was to investigate the relevance of the prediction equations derived from the relationship between metabolic energy expenditure and kinetic energy, for different speeds of walking and running over the treadmill. Seven male subjects participated in this study. A tri-axial accelerometer was attached on between the left and right posterior superior iliac spines. Kinetic energy was calculated by the integration of acceleration data and compared with the metabolic energy measured by a gas analyzer. Correlation coefficients were determined to find a relationship between the kinetic energy and the metabolic energy expenditure. Also, the difference between measured and predicted values was used to find the relevance for individual and group equations. Results showed a relatively good correlation between the measured metabolic energy and the calculated kinetic energy. In addition, a dramatic increase in kinetic energy was observed at the transition speed of walking and running (6 km/h). There was no difference in how to predict the kinetic energy expenditure for individual and group even though people have different physical characteristics. This study would be useful to predict metabolic energy expenditures by the regression analysis with acceleration data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.703-706
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• 2007

The real-time monitoring about the activity of the human provides useful information about the activity quantity and an ability. This study developed a system for human physical activity assessment in ambulatory monitoring using portable sensing device combining a tri-axial accelerometer and wireless sensor node. This real-time system is able to identify several postures, posture transitions and movements with classification algorithm. In addition, this system also features fall detection capability. The results of the assessment for evaluating the performance of the system show high identification accuracy.

• Korean Journal of Applied Biomechanics
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• v.19 no.1
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• pp.179-186
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• 2009

This works studied to compare gas analyzer with accelerometer and the estimation of energy expenditure based on different attaching position of tri-axial accelerometer such as waist and top of the foot Based on the fact that oxygen intake increases more radically linearly during walking more than 8.0km/hr. 9 male subjects performed walking and running on the treadmill with speed of $1.5{\sim}8.5km$/hr and $4.5{\sim}13.0km$/hr, respectively. Commercially available Nike + iPod Sports kit was used to compare energy expenditure with sensor module attached to their foot. Actual energy expenditure was determined by a continuous direct gas analyzer and two multiple regression equations of walking and running mode for different attaching position were developed. Results showed that estimation accuracy of energy expenditure using waist mounted accelerometer was higher than that of the top of the foot and Nike + iPod Sports kit. Results of energy expenditure based on waist and top of the foot showed that the crossover state of energy expenditure occurred at 7.5km/hr. But Nike + iPod Sports kit could not find intersection of energy expenditure in all nine subjects. Therefore the sensor module attached to the waist and separate multi regression equation by walking and running mode was the best to estimate more accurate prediction.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.495-496
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• 2013

본 논문에서는 패치형 3축 가속도 센서와 스마트 디바이스를 활용하여 유산소 운동에 따라 가속도 센서를 통해 얻은 데이터의 특징을 분석하고 유산소 운동을 분류하는 모바일 시스템을 설계하였다. 제안하는 시스템을 이용하여 사용자가 하고 있는 유산소 운동을 스마트 디바이스에서 실시간으로 분류하고 분류된 운동에 따라 운동 횟수와 사용자의 움직임을 분석하여 사용자에게 편의성과 운동 정보를 제공 할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1336-1339
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• 2009

산업화가 진행된 세계 주요 선진국들은 의학의 발달과 평균 수명의 증가로 고령화의 위기를 겪고 있다. 인구 고령화에 따라 치매 인구도 크게 증가하였다. 치매 인구의 증가는 국가와 가정의 물질적, 인적 비용을 증가시키고 있다. 이와 같은 사회문제를 해결하고 효율적인 치매 환자 관리를 위한 방법이 필요하다. 관찰 대상자가 치매 증상과 비슷하게 행동한다면 치매를 의심해 볼 수 있다. 본 논문에서는 3축 가속도 센서를 사용하여 대상자의 행위 정보를 수집하고 디지털화한다. 디지털화 된 행위정보를 치매 증상의 행동 패턴과 비교하여 관찰 대상자의 행동이 치매 증상인지 정상적인 활동인지 판단할 수 있는 방법을 소개한다.