• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.43 no.4
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• pp.256-261
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• 2017

Objectives: The concept of natural head position (NHP) was first introduced by Broca in 1862, and was described as a person's stable physiologic position "when a man is standing and his visual axis is horizontal." NHP has been used routinely for clinical examination; however, a patient's head position is random during cone-beam computed tomography (CBCT) acquisition. To solve this problem, we developed an accelerometer to record patients' NHP and reproduce them for CBCT images. In this study, we also tested the accuracy and reproducibility of our accelerometer. Materials and Methods: A total of 15 subjects participated in this study. We invented an accelerometer that measured acceleration on three axes and that could record roll and pitch calculations. Recorded roll and pitch data for each NHP were applied to a reoriented virtual image using three-dimensional (3D) imaging software. The data between the 3D models and the clinical photos were statistically analyzed side by side. Paired t-tests were used to statistically analyze the measurements. Results: The average difference in the angles between the clinical photograph and the 3D model was $0.04^{\circ}$ for roll and $0.29^{\circ}$ for pitch. The paired t-tests for the roll data (P=0.781) and the pitch data (P=0.169) showed no significant difference between the clinical photographs and the 3D model (P>0.05). Conclusion: By overcoming the limitations of previous NHP-recording techniques, our new method can accurately record patient NHP in a time-efficient manner. Our method can also accurately transfer the NHP to a 3D virtual model.

• 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 Internet Computing and Services
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• v.12 no.3
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• pp.17-26
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). The recognition rate of our algorithm was 97.34% better than that of the Actical device(91.74%) by 5.6%.

• Journal of Sensor Science and Technology
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• v.20 no.2
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• pp.131-136
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• 2011

In this study, a compact wireless vibration measurement system was developed using a 3-axial accelerometer in order to evaluate the vibration stimulation system. A low power microprocessor chip integrated with 2.4 GHz RF transceiver was used for the wireless data communication. To evaluate the system, the frequencies and accelerations from the vibration stimulation system were measured using an LVDT sensor and a vibration measurement system. The average frequency difference by the measurement system was less than 0.1 Hz, and the standard deviation of frequencies estimated by the LVDT sensor and the accelerometer was below 0.08 Hz. The developed system was applied to access a vibration stimulation system for the future study. The average acceleration difference of the central and peripheral point of the stimulation system was less than 0.0005 g(1 g=9.8 $m/s^2$), and the standard deviation of the acceleration was below 0.004 g, which shows the usefulness of the wireless vibration measurement system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.520-528
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• 2001

This paper investigates a resonant microaccelerometer that measures acceleration using a built-in micromechanical resonator, whose resonant frequency is changed by the acceleration-induced axial force. A set of design equations for the resonant microaccelerometer has been developed, including analytic formulae for resonant frequency, sensitivity, nonlinearity and maximum stress. On this basis, the sizes of the accelerometer are designed for the sensitivity of 10$^3$Hz/g in the detection range of 5g, while satisfying the conditions for the maximum nonlinearity of 5%, the minimum shock endurance of 100g and the size constraints placed by microfabrication process. A set of the resonant accelerometers has been fabricated by the combined use of bulk-micromachining and surface-micromachining techniques. From a static test of the cantilever beam resonant accelerometer, a frequency shift of 860Hz has been measured for the proof-mass deflection of 4.3${\pm}$0.5$\mu\textrm{m}$; thereby resulting in the detection sensitivity of 1.10${\times}$10$^3$Hz/g. Uncertainty analysis of the resonant frequency output has been performed to identify important issues involved in the design, fabrication and testing of the resonant accelerometer.

• Journal of Information Processing Systems
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• v.9 no.2
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• pp.333-348
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• 2013

Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately $91.33{\pm}0.67%$ for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.529-536
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• 2001

We investigate a surface-micromachined capacitive accelerometer with the grid-type electrodes surrounded by a perforated proof-mass frame. An electromechanical analysis of the microaccelerometer has been performed to obtain analytical formulae for natural frequency and output sensitivity response estimation. A set of prototype devices has been designed and fabricated based on a 4-mask surface-micromachining process. The resonant frequency of 5.8$\pm$0.17kHz and the detection sensitivity of 0.28$\pm$0.03mV/g have been measured from the fabricated devices. The parasitic capacitance of the detection circuit with a charge amplifier has been measured as 3.34$\pm$1.16pF. From the uncertainty analysis, we find that the major uncertainty in the natural frequency of the accelerometer comes from the micromachining error in the beam width patterning process. The major source of the sensitivity uncertainty includes uncertainty of the parasitic capacitance, the inter-electrode gap and the resonant frequency, contributing to the overall sensitivity uncertainty in the portions of 75%, 14% and 11%, respectively.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.9-16
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• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.7-14
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• 2017

A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accelerometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vector magnitude (SVM_Acc), angular velocity (${\omega}_{res}$), and angular variation (${\theta}_{res}$) were calculated using MATLAB. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ${\omega}_{res}$ was larger than 1.75 rad/s (TH2), and ${\theta}_{res}$ was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied, the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problem in distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were applied to the sequential processing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the specificity was determined to be 100% for the eleven ADL action sequences.

• Transactions on Electrical and Electronic Materials
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• v.8 no.6
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• pp.293-298
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• 2007

The aged are faced with increasing risk for falls. The aged have more fragile bones than others. When falls occur, it is important to detect this emergency state because such events often lead to more serious illness or even death. A implementation of PDA system, for detection of emergency situation, was developed using 3-axis accelerometer in this paper as follows. The signals were acquired from the 3-axis accelerometer, and then transmitted to the PDA through a Bluetooth module. This system can classify human activity, and also detect an emergency state like falls. When the fall occurs, the system generates the alarm on the PDA. If a subject does not respond to the alarm, the system determines whether the current situation is an emergency state or not, and then sends some information to the emergency center in the case of an urgent situation. Three different studies were conducted on 12 experimental subjects, with results indicating a good accuracy. The first study was performed to detect the posture change of human daily activity. The second study was performed to detect the correct direction of fall. The third study was conducted to check the classification of the daily physical activity. Each test lasted at least 1 min. in the third study. The output of the acceleration signal was compared and evaluated by changing various postures after attaching a 3-axis accelerometer module on the chest. The newly developed system has some important features such as portability, convenience and low cost. One of the main advantages of this system is that it is available at home healthcare environment. Another important feature lies in its low cost of manufacture. The implemented system can detect the fall accurately, so it will be widely used in emergency situations.