• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.129-141
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• 2010

In this paper, we present a wearable intelligent device based on multi-sensor for monitoring human activity. In order to recognize multiple activities, we developed activity recognition algorithms utilizing an image sensor and a 3-axis accelerometer sensor. We proposed a grid?based optical flow method and used a SVM classifier to analyze data acquired from multi-sensor. We used the direction and the magnitude of motion vectors extracted from the image sensor. We computed the correlation between axes and the magnitude of the FFT with data extracted from the 3-axis accelerometer sensor. In the experimental results, we showed that the accuracy of activity recognition based on the only image sensor, the only 3-axis accelerometer sensor, and the proposed multi-sensor method was 55.57%, 89.97%, and 89.97% respectively.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.4
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• pp.53-58
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• 2009

In recently years, the research and application for sensor has increased in each field. In this paper, the system which can perceive and detect using 3-axis accelerometer sensor and serial communication is proposed. Also, the user has GUI environment for monitor in real-time. In order to reduce unstable data and error defect of electronic rechargeable liquid tilt sensor used digital 3-axis accelerometer sensor which has AD convertor. Therefore, this system provide exact data and a problem of objects for user more easier.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.33-37
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• 2024

Recently, wearable virtual reality devices are widely used. These instruments include a 3-axis accelerometer. User's heart rate information in virtual reality contents can be useful for measuring user experience. In this paper, we propose a method to measure the heart rate through a 3-axis accelerometer based on the principle of ballistocardiography without additional sensors. The angular velocity was successively measured in a time series by the 3-axis accelerometer mounted to the head. The frequency of the maximum magnitude is determined as the heart rate through frequency transform and band pass filtering of the time series signal. For verification, the heart rate calculated from photoplethysmography sensors acquired at the same time was compared as ground-truth. In the virtual reality, the user's heart rate information can be extracted without additional heart rate sensor, and the emotional state and fatigue can be measured.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.82-89
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• 2014

Recently, inertial sensors are popularly used for the location and position recognition of small devices for M2M/IoT. In this paper, we designed low power, low noise, small sized 6-axis inertial sensor IC for mobile applications, which uses a 3-axis piezo-electric gyroscope sensor and a 3-axis piezo-resistive accelerometer sensor. Proposed IC is composed of 3-axis gyroscope readout circuit, two gyroscope sensor driving circuits, 3-axis accelerometer readout circuit, 16bit sigma-delta ADC, digital filter and control circuit and memory. TSMC $0.18{\mu}m$ mixed signal CMOS process was used. Proposed IC reduces 27% of the current consumption of LSM330.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.2
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• pp.179-185
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• 2015

Although an accelerometer is a sensor that measures acceleration, it cannot be used by itself to measure the acceleration when the orientation of the sensor changes. This paper introduces a Kalman filter for the estimation of a sensor acceleration based on a six-axis inertial sensor (i.e., a three-axis accelerometer and three-axis gyroscope). The novelty of the proposed Kalman filter lies in the fact that its state vector includes not only the tilt angle variable but also the sensor acceleration. Thus, the filter can explicitly estimate the latter with a high accuracy. The accuracy of acceleration estimates were validated experimentally under three different dynamic conditions, using an optical motion capture system. It could be concluded that the performance of the proposed Kalman filter was comparable to that of the state-of-the-art estimation algorithm employed by the Xsens MTw. The proposed algorithm may be more suitable than inertial/magnetic sensor-based algorithms for various applications adopting six-axis inertial sensors.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.3
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• pp.199-206
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• 2016

Most Koreans walk having their toes in or out, because of their sedentary lifestyles. In addition, using smartphone while walking makes having a desirable walking posture even more difficult. The goal of this study is to make a simple system which easily analyze and inform any person his or her personal walking habit. To discriminate gait patterns, we developed a gait monitoring system using a 3-axis accelerometer and a foot pressure monitoring system. The developed system, with an accelerometer and a few pressure sensors, can acquire subject's foot pressure and how tilted his or her torso is. We analyzed the relationship between type of gate and sensor data using this information. As the result of analysis, we could find out that statistical parameters like standard deviation and root mean square are good for discriminating among torso postures, and k-nearest neighbor algorithm is good at clustering gait patterns. The developed system is expected to be applicable to medical or athletic fields at a low price.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2427-2432
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• 2009

This paper proposes an monitoring system of elder and feeble person's motion activity using an object's motion activity data. The proposed system used wireless 3-axis sensor module, product by Freescale(Wireless Sensing Triple Axis Reference Design Board (ZSTAR)). We distribute sensing data into three classes using Neural Network System SVM. We find performance of proposed system that simulate some case about walk, past walk, fallen. Classify result data and graph of sensing data present succes rate 80%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.105-111
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• 2010

The purpose of this study was to detect early children with hyperactivity which is one of the symptoms of Attention Deficit-Hyperactivity Disorder (ADHD). This study used two methods: K-CBCL and observation of children's behavior. K-CBCL was done online by parents at home. For observation of children's behavior, the school asked children to wear a 3-axis accelerometer on their wrists. The data from K-CBCL and 3-axis accelerometer were analyzed and clustered to separate hypersensitive children from ordinary children. This experiment confirmed that 3-axis accelerometer which is one of Ubiquitous techniques and the K-CBCL questionnaire were helpful for detection of hypersensitive children.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.12
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• pp.1082-1089
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• 2013

3-Axis sensor measurement system is needed for measuring ride quality of elevator. But because 3-Axis piezoelectric accelerometer is expensive. We developed 3-Axis sensor system which is suitable for measuring ride quality of elevator using cheap MEMS sensor. There are two types of MEMS sensor that are piezoresistive and capacitive type. The excellence of piezoresistive type in characteristic of frequency response and noise is confirmed compare to capacitive type as a result of this paper's experiment and reference. 3-Axis system using MEMS sensor needs MEMS's proper frequency response characteristic. Additionally noise characteristic of sensor and circuit, stiffness of assembly are needed for deciding frequency range and accuracy of amplitude.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.632-637
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• 2012

This paper introduces a simple and efficient calibration method for three-axis accelerometers and three-axis gyroscopes using three-axis motion rate table. Usually, the performance of low cost MEMS-based inertial sensors is affected by scale and bias errors significantly. The calibration of these errors is a bothersome problem, but the previous calibration methods cannot propose simple and efficient method to calibrate the errors of three-axis inertial sensors. This paper introduces a new simple and efficient method for the calibration of accelerometer and gyroscope. By using a three-axis motion rate table, this method can calibrate the accelerometer and gyroscope simultaneously and simply. Experimental results confirm the performance of the proposed method.