• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.385-395
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• 2017

A new digitized decoupled dual-axis micro dynamically tuned gyroscope with three equilibrium rings (TMDTG) is proposed which can eliminate the constant torque disturbance (CTD) caused by the double rotation frequency of a driving shaft with a micro dynamically tuned gyroscope with one equilibrium ring (MDTG). A mechanical and kinematic model of the TMDTG is theoretically analyzed and the structure parameters are optimized in ANSYS to demonstrate reliability. By adjusting the thickness of each equilibrium ring, the CTD can be eliminated. The digitized model of the TMDTG system is then simulated and examined using MATLAB. Finally, a digitized prototype based on FPGA is created. The gyroscope can be dynamically tuned by adjusting feedback voltage. Experimental results show the TMDTG has good performance with a scale factor of $283LSB/^{\circ}/s$ in X-axis and $220LSB/^{\circ}/s$ in Y-axis, respectively. The scale factor non-linearity is 0.09% in X-axis and 0.13% in Y-axis. Results from analytical models, simulations, and experiments demonstrate the feasibility of the proposed TMDTG.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1547-1552
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• 2003

In this paper, we present a planar single-crystalline silicon x-axis micro-gyroscope fabricated with a perfectly aligned vertical actuation combs on one silicon wafer, using the extended SBM technology. The fabricated x-axis micro-gyroscope has the resolution of 0.1 deg/sec, the bandwidth of 100 Hz. These research results allow integrating 6 axes inertial measurement (3 accelerations and 3 angular rates) on the same silicon substrate using the same process for the first time.

• Journal of Sensor Science and Technology
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• v.26 no.4
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• pp.235-238
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• 2017

In this study, the design of a tri-axis micromachined gyroscope is proposed and the vibration characteristic of the structure is analyzed. Tri-axis vibratory gyroscopes that utilize Coriolis effect are the most commonly used micromachined inertial sensors because of their advantages, such as low cost, small packaging size, and low power consumption. The proposed design is a single structure with four proof masses, which are coupled to their adjacent ones. The coupling springs of the proof masses orthogonally transfer the driving vibrational motion. The resonant frequencies of the gyroscope are analyzed by finite element method (FEM) simulation. The suspension beam spring design of proof masses limits the resonance frequencies of four modes, viz., drive mode, pitch, roll and yaw sensing mode in the range of 110 Hz near 21 kHz, 21173 Hz, 21239 Hz, 21244 Hz, and 21280 Hz, respectively. The unwanted modes are separated from the drive and sense modes by more than 700 Hz. Thereafter the drive and the sense mode vibrations are calculated and simulated to confirm the driving feasibility and estimate the sensitivity of the gyroscope. The cross-axis sensitivities caused by driving motion are 1.5 deg/s for both x- and y-axis, and 0.2 deg/s for z-axis.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.132-137
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• 2022

In this paper, we have studied the quadrature error reduction for the single drive 3-axis MEMS Gyroscope. There was a limitation of the previous study which is the z-axis quadrature error was large. To reduce this value, design methodologies were presented. And the methodologies included a different mesh application, z-rate spring structure change, and mass compensation for balancing of the structure. We conducted the modal analysis, drive mode analysis and sense mode analysis using COMSOL Multiphysics. As a result, a drive resonant frequency was 26003 Hz, with the x-sense, y-sense, z-sense being 26749 Hz, 26858 Hz, 26920 Hz, respectively. And the Mechanical sensitivity was computed at 2000 degrees per second(dps) input angular rate while the sensitivity for roll, pitch, and yaw was computed 0.011, 0.012, and 0.011 nm/dps respectively. And z-axis quadrature error was successfully improved, 2.78 nm to 0.95 nm, which the improvement rate was about 66 %.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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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.

• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.67-74
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• 2013

In this paper, we propose the pointing and correction algorithm for optimized performance based on Bluetooth communication. The error from the accelerometer sensor's output must be carefully managed as the accelerometer sensor is more sensitive to data change compared to that of the gyroscope sensor. Thus, we minimize the noise by applying the Kalman filter to data for each axis from the accelerometer. In addition, we can also obtain effect compensating the hand tremor by applying the Kalman filter to the data variation for x and y. In this study, we extract data through the Quaternion mapping process on data from the accelerometer and gyroscope. In turn, we can obtain a tilt compensation by applying a compensation algorithm with acceleration of the gravity of the extracted data. Moreover, in order to correct the inaccuracy on smart sensor due to the rapid movement of a device, we propose a adaptive pointing and correction algorithm using the genetic approach to generate the initial population depending on the user.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.2
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• pp.133-140
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• 2015

In this paper, a roll angle estimation method of a rolling airframe using a low grade GPS and a roll rate gyro is proposed. The strength of the received signal of the GPS antenna attached on the rolling airframe is maximized when the GPS satellite is placed on the plane determined by the x-axis of the rolling airframe and the GPS antenna axis. Under the assumption that the x-axis of the rolling airframe is coincident with its velocity vector, the roll angle of the rolling airframe is calculated from the relative position vector of the satellite to the GPS when the GPS signal strength becomes maximum. The Kalman filter combined with a roll rate gyro is introduced to increase the determination accuracy of the roll angle. The performance of the proposed method is verified via 6-DOF simulations.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.2
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• pp.161-168
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• 2015

In this paper, we present an inertial sensor-based gait distance measurement system using accelerometer, gyroscope, and magnetometer. To minimize offset and gain error of inertial sensors, we performed the calibration using the self-made calibration jig with 9 degrees of freedom. For measuring accurate gait distance, we used gradient descent algorithm to remove gravity error and used analysis of gait pattern to remove drift error. Finally, we measured a gait distance by double-integration of the error-removed acceleration data. To evaluate the performance of our system, we walked 10m in a straight line indoors to observe the improvement of removing error which compared un-calibrated to calibrated data. Also, the gait distance measured by the system was compared to the measurement of the Vicon motion capture system. The evaluation resulted in the improvement of $31.4{\pm}14.38%$(mean${\pm}$S.D.), $78.64{\pm}10.84%$ and $69.71{\pm}26.25%$ for x, y and z axis, respectively when walked in a straight line, and a root mean square error of 0.10m, 0.16m, and 0.12m for x, y and z axis, respectively when compared to the Vicon motion capture system.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.