• Journal of Sensor Science and Technology
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• v.16 no.6
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• pp.467-474
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• 2007

The real-time monitoring about the activity of the human provides useful information about the activity quantity and ability. The present study implemented a small-size and low-power acceleration monitoring system for convenient monitoring of activity quantity and recognition of emergent situations such as falling during daily life. For the wireless transmission of acceleration sensor signal, we developed a wireless transmission system based on a wireless sensor network. In addition, we developed a program for storing and monitoring wirelessly transmitted signals on PC in real-time. The performance of the implemented system was evaluated by assessing the output characteristic of the system according to the change of posture, and parameters and acontext recognition algorithm were developed in order to monitor activity volume during daily life and to recognize emergent situations such as falling. In particular, recognition error in the sudden change of acceleration was minimized by the application of a falling correction algorithm

• 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.

• Journal of Sensor Science and Technology
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• v.25 no.2
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• pp.79-85
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• 2016

This paper proposes a threshold-based fall recognition algorithm to discriminate between falls and activities of daily living (ADL) using a tri-axial accelerometer and a bi-axial gyroscope sensor mounted on the upper sternum. The experiment was executed ten times according to the proposed experimental protocol. The output signals of the tri-axial accelerometer and the bi-axial gyroscope were measured during eight falls and eleven ADL action sequences. The threshold values of the signal vector magnitude (SVM_Acc), angular velocity (${\omega}_{res}$), and angular variation (${\theta}_{res}$) parameter were calculated using MATLAB. From the preliminary study, three thresholds (TH1, TH2, and TH3) were set so that the falls could be distinguished from ADL. When the parameter SVM_Acc is greater than 2.5 g (TH1), ${\omega}_{res}$ is greater than 1.75 rad/s (TH2), and ${\theta}_{res}$ is greater than 0.385 rad (TH3), these action sequences are recognized as falls. If at least one or more of these conditions is not satisfied, the sequence is classified as ADL.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.5
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• pp.591-598
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• 2011

Tip position control of a flexible cantilever is difficult due to the non-minimum phase dynamics that result from the finite propagating speed of a mechanical wave along the cantilever. In this paper, we propose a method for the tip position control using a light and cheap accelerometer that does not bring any significant change to the dynamics of the cantilever system. The linear system identification model of the flexible cantilever is obtained with measurements by a laser displacement sensor. A Kalman estimator is designed with this model and calculates the estimated tip position with the acceleration data of the accelerometer that is attached on the tip of the cantilever. To verify reliability of the estimator, the estimated tip position is used to the feedback control system that uses a fuzzy logic controller. The control results are compared with those of the fuzzy control system where the real tip position is measured by a laser displacement sensor. Also, the performance of the estimator with the accelerometer is presented and discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1300-1305
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• 2012

In this research, we propose a momentum measurement system using the accelerometer sensor, MCU, and Bluetooth to measure the exact momentum. The proposed system can figure out information for the real time travel distance. We performed various experiments, and analyzed the results using the proposed momentum measurement system. In the simulation experiments, we compared the reliability and accuracy for the existing momentum measurement systems from the analyzed results. The proposed system showed travel distance error of less than 8% as compared to the existing system with the error of approximately 13%. We expect that the proposed system apply to the commercial products.

• International Journal of Automotive Technology
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• v.8 no.6
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• pp.753-760
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• 2007

In this paper, a roll and pitch estimation algorithm using a set of accelerometers and wireless sensor networks(S/N) is presented for use in a passenger vehicle. While an inertial measurement unit(IMU) is generally used for roll/pitch estimation, performance may be degraded in the presence of longitudinal acceleration and yaw motion. To compensate for this performance degradation, a new roll and pitch estimation algorithm is proposed that uses an accelerometer array, global positioning system(GPS) and in-vehicle networks to get information from yaw rate and roll rate sensors. Angular acceleration and roll and pitch approximation are first calculated based on vehicle kinematics. A discrete Kalman filter is then applied to estimate both roll and pitch more precisely by reducing noise from the running engine and from road disturbance. Finally, the feasibility of the proposed algorithm is shown by comparing its performance experimentally with that of an IMU in the framework of an indoor test platform as well as a test vehicle.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.6
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• pp.851-858
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• 2012

In this paper, we design a seismic data acquisition system(SDAS) and implement it. This system is essential for development of a noble local earthquake disaster preventing system in population center. In the system, we choose a proper MEMS-type triaxial accelerometer as a sensor, and FPGA and ARM processor are used for implementing the system. In the SDAS, each module is realized by Verilog HDL and C Language. We carry out the ModelSim simulation to verify the performances of important modules. The simulation results show that the FPGA-based data acquisition module can guarantee an accurate time-synchronization for the measured data from each axis sensor. Moreover, the FPGA-ARM based embedded technology in system hardware design can reduce the system cost by the integration of data logger, communication sever, and facility control system. To evaluate the data acquisition performance of the SDAS, we perform experiments for real seismic signals with the exciter. Performances comparison between the acquired data of the SDAS and the reference sensor shows that the data acquisition performance of the SDAS is valid.

• ETRI Journal
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• v.45 no.6
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• pp.1079-1089
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• 2023

Stroke is one of the leading causes of long-term disability worldwide, placing huge burdens on individuals and society. Further, automatic human activity recognition is a challenging task that is vital to the future of healthcare and physical therapy. Using a baseline long short-term memory recurrent neural network, this study provides a novel dataset of stretching, upward stretching, flinging motions, hand-to-mouth movements, swiping gestures, and pouring motions for improved model training and testing of stroke-affected patients. A MATLAB application is used to output textual and audible prediction results. A wearable sensor with a triaxial accelerometer is used to collect preprocessed real-time data. The model is trained with features extracted from the actual patient to recognize new actions, and the recognition accuracy provided by multiple datasets is compared based on the same baseline model. When training and testing using the new dataset, the baseline model shows recognition accuracy that is 11% higher than the Activity Daily Living dataset, 22% higher than the Activity Recognition Single Chest-Mounted Accelerometer dataset, and 10% higher than another real-world dataset.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.4
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• pp.71-80
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• 2006

Strapdown Inertial navigation systems consist of an inertial sensor assembly(ISA), electronic modules to process sensor data, and a navigation computer to calculate attitude, velocity and position. In the ISA, most gryoscopes such as RLGs and FOGs, have digital output, but typical accelerometers use current as an analog output. For a high precision inertial navigation system, sufficient stability and resolution of the accelerometer board converting the analog accelerometer output into digital data needs to be guaranteed. To achieve this precision, the asymmetric error and A/D reset scale error of the accelerometer board must be properly compensated. If the relation between the acceleration error and the errors of boards are exactly known, the compensation and estimation techniques for the errors may be well developed. However, the A/D Reset scale error consists of a pulse-train type term with a period inversely proportional to an input acceleration additional to a proportional term, which makes it difficult to estimate. In this paper, the effects on the acceleration output for auto-pilot situations and the effects of A/D reset scale errors during horizontal alignment are qualitatively analyzed. The result can be applied to the development of the real-time compensation technique for A/D reset scale error and the derivation of the design parameters for accelerometer board.

• 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.