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The present study developed a variational autoencoder (VAE) capable of estimating the chronological age of subjects using feature values extracted from their teeth, and further determine how given teeth images affected the accuracy of estimation. The developed VAE was trained with first molar and canine teeth images, and a parallel-VAE structure was further constructed to extract common features shared by the two types of teeth in a more effective manner. The encoder of the VAE was designed to be combined with a regression model to estimate age. In an attempt to determine which parts of tooth images were more or less important when estimating age, a method of visualizing the obtained regression coefficient using the decoder of the VAE was developed. The developed age estimation model was trained with data obtained from a total of 910 individuals aged 10–79 years. This model showed a median absolute error (MAE) of 6.99 years, demonstrating its ability to estimate age accurately. Further, this method of visualizing the influence of particular parts of tooth images on the accuracy of age estimation using a decoder is expected to provide novel insights for future research on explainable AI.

Intertrochanteric (IT) femur fractures are the most common fractures in elderly people, and they lead to significant morbidity, mortality, and reduced quality of life. The different types of fractures require a careful definition to ensure accurate surgical planning and reduce the operation time, healing time, and number of surgical failures. In this study, a deep learning-based automatic multi-class IT fracture detection model was developed using computed tomography (CT) images and based on the AO/OTA classification method. The original CT image was resized and rearranged according to the fracture location and an unsharp masking filter was applied. A multi-class classification of nine different types of IT fractures and no fracture was performed using the faster regional-convolutional neural network (R-CNN). Bayesian optimization was also implemented to determine the optimal hyperparameter values for the faster R-CNN algorithm. In our proposed model, IT fractures classified into two classes showed an average accuracy of 0.97 ± 0.02, which was 0.90 ± 0.02 when classified into ten classes. Additionally, the detected region of interest from our proposed model showed minimum root mean square error and intersection over union values of 16.34 ± 47.01 pixels and 0.87 ± 0.12, respectively. In the future, our proposed automatic multi-class IT femur fracture detection model could allow clinicians to identify the fracture region and diagnose different types of femur fractures faster and more accurately. This will increase the probability of correct surgical treatment and minimize postoperative complications.

A switched substrate-shield inductor (SSI) topology in bulk CMOS is proposed which minimizes parasitic capacitance and substrate losses, while tuned magnetically induced currents facilitate inductor tunability. The high frequency behavior of the induced current is analyzed, resulting in intuitive insights and design guidelines for a high-performance SSI. An SSI prototype in 65-nm bulk CMOS achieves 34% inductance tunability with a quality factor of >10.3. A voltage-controlled oscillator (VCO) using SSI achieves 40.3% tuning range, from 21 to 31.6 GHz, and a phase noise of −119.1 ± 3.7 dBc/Hz at 10-MHz offset frequencies. The VCO core consumes 4.3 ± 0.2 mW from a 1-V supply.

Buckwheat sprouts that are synthesized during the germination process are rich in flavonoids, including orientin, vitexin, rutin, and their isomers (isoorientin, isovitexin, and quercetin-3-O-robinobioside, respectively). The purpose of this study was to optimize and validate an analytical method for separating flavonoid isomers in common buckwheat sprout extract (CSE). Factors, such as range, linearity, precision, accuracy, limit of detection, and limit of quantification, were evaluated for each standard using high-performance liquid chromatography (HPLC). On the basis of resolution and symmetry, a column temperature of 40 &deg, C with 0.1% (v/v) acidic water and acetonitrile as mobile phases, at a flow rate of 1 mL min&minus, 1 were determined to be the optimal analytical conditions. Calibration curves for orientin, isoorientin, vitexin, isovitexin, and rutin exhibited good linearity with correlation coefficients of 0.9999 over the 6.25&ndash, 100.00 &mu, g mL&minus, 1 range. Recovery values of 96.67&ndash, 103.60% confirmed that the method was accurate for all flavonoids. The relative standard deviations of intra-day repeatability and inter-day reproducibility confirmed method preciseness, with values of less than 5.21% and 5.40%, respectively. The developed method was used to analyze flavonoids in CSE, with isomers satisfactorily separated and simultaneously quantified. We demonstrated that the developed HPLC method can be used to monitor flavonoids in buckwheat sprouts.

th , 2015; Revised: August 11 st , 2015; Accepted: August 20 th , 2015 Purpose: From the perspective of biomechanics, joint moments quantitatively show a subject’s ability to perform actions. In this study, the effect of normalization in the fast and asymmetric motions of a golf swing was investigated by applying three different normalization methods to the raw joint moment. Methods: The study included 13 subjects with no previous history of musculoskeletal diseases. Golf swing analyses were performed with six infrared cameras and two force plates. The majority of the raw peak joint moments showed a significant correlation at p < 0.05. Additionally, the resulting effects after applying body weight (BW), body weight multiplied by height (BWH), and body weight multiplied by leg length (BWL) normalization methods were analyzed through correlation and regression analysis. Results: The BW, BWH, and BWL normalization methods normalized 8, 10, and 11 peak joint moments out of 18, respectively. The best method for normalizing the golf swing was found to be the BWL method, which showed significant statistical differences. Several raw peak joint moments showed no significant correlation with measured anthropometrics, which was considered to be related to the muscle coordination that occurs in the swing of skilled professional golfers. Conclusions: The results of this study show that the BWL normalization method can effectively remove differences due to physical characteristics in the golf swing analysis.

This study proposes a method to measure the twisting angle of a rod during a spinal correction surgery in real-time without performing an alignment procedure by using the six-axis data (tri-axis acceleration, tri-axis rate gyro) of the IMU and a Tait-Bryan Euler angle algorithm. To determine whether the twisting angle calculation algorithm offered an improvement, typical procedures described in existing studies were implemented using the same experimental data and were then validated using a three-dimensional motion analysis system (Vicon 460 motion analysis system). The correlation coefficients and the RMSE of the proposed method were 0.904 and 0.680° in the servo-motor experiment, and 0.988 and 0.691° in the mock surgery experiment, respectively, and these values were not significantly different from those calculated through other methods in previous studies. Therefore, if the proposed method is used during surgery, an alignment procedure and the assumptions following the morphology of body, which are limitations of prior research, are not necessary. Also the twisting angle of the rod can be observed without using magnetic data of IMU in real time during surgery. It is expected that the correction loss, which is a serious problem that can occur in patients after spinal correction, could be prevented.

The goal of this study was to predict gait speed over the entire cycle in reference to plantar pressure data acquired by means of the insole-type plantar pressure measuring device (Novel Pedar-x system). To predict gait speed, the artificial neural network is adopted to develop the model to predict gait speed in the stance phase (Model I) and the model to predict gait speed in the swing phase (Model II). The predicted gait speeds were validated with actual values measured using a motion capturing system (VICON 460 system) through a five-fold cross-validation method, and the correlation coefficients (R) for the gait speed were 0.963 for normal walking, 0.978 for slow walking, and 0.950 for fast walking. The method proposed in this study is expected to be widely used clinically in understanding the progress and clarifying the cause of such diseases as Parkinsonism, strike, diabetes, etc. It is expected that the method suggested in this study will be the basis for the establishment of a new research method for pathologic gait evaluation.

Permanent facial nerve damage is one of the most critical complications that can occur during otologic and neurosurgical procedures. As a result, facial nerve monitoring equipment is now used in most of otologic surgeries. The monitoring devices used for this purpose usually evaluate the activity of facial muscle in response to stimulation to the facial nerve. For these purposes, electromyography and the train-of-four (TOF) method is commonly used clinically, but these approaches have an important limitation: this qualitative method only depends on visual information of finger movement. Thus, in this study, a new factor that can be used to quantitatively the stage of muscle relaxation at the facial nerve was developed based on the degree of muscle relaxation and electro-stimulation. Ketamine intravenous anesthesia and isoflurane inhalation anesthesia was given to 13 rabbits. A 28 G unipolar needle-electrode was inserted at an exposed facial nerve, and stimulated (0.1 mA–5.0 mA) with an electrical stimulator. Also, the degree of muscle relaxation based electromyographic signal acquisition was performed while increasing the dose of neuromuscular blockades. After data noise reduction and normalization, the collected EMG signals were divided into 33 variables, and a new evaluation factor was developed using statistical methods based on multiple linear regression models and principal component analysis. A new evaluation factor was identified that can be used to quantitatively estimate each phase in muscle relation. The evaluating factors that were calculated in accordance with the electrical simulation resulted in improved results (r = 0.96) when compared to the existing assessment methods. (T1 ratio r = 0.72; T4/T1 ration r = 0.52).

Purpose: The purpose of this study was to select standing posture parameters that have a significant difference according to the severity of spinal deformity, and to develop a novel Cobb angle prediction model for adolescent girls with idiopathic scoliosis. Methods: Five normal adolescents girls with no history of musculoskeletal disorders, 13 mild scoliosis patients (Cobb angle: 10°-25°), and 14 severe scoliosis patients (Cobb angle: 25°-50°) participated in this study. Six infrared cameras (VICON) were used to acquire data and 35 standing parameters of scoliosis patients were extracted from previous studies. Using the ANOVA and post-hoc test, parameters that had significant differences were extracted. In addition, these standing posture parameters were utilized to develop a Cobb-angle prediction model through multiple regression analysis. Results: Twenty two of the parameters showed differences between at least two of the three groups and these parameters were used to develop the multi-linear regression model. This model showed a good agreement (R 2 = 0.92) between the predicted and the measured Cobb angle. Also, a blind study was performed using 5 random datasets that had not been used in the model and the errors were approximately 3.2 ± 1.8. Conclusions: In this study, we demonstrated the possibility of clinically predicting the Cobb angle using a non-invasive technique. Also, monitoring changes in patients with a progressive disease, such as scoliosis, will make possible to have determine the appropriate treatment and rehabilitation strategies without the need for radiation exposure.

In this study, we describe a method to predict 6-axis ground reaction forces based solely on plantar pressure (PP) data obtained from insole type measurement devices free of space limitations. Because only vertical force is calculable from PP data, a wavelet neural network derived from a non-linear mapping function was used to obtain 3-axis ground reaction force in medial-lateral (GRFML), anterior-posterior (GRFAP) and vertical (GRFV) and 3-axis ground reaction moment in sagittal (GRFS), frontal (GRFF) and transverse (GRFT) data for the remaining axes and planes. As the prediction performance of nonlinear models depends strongly on input variables, in this study, three input variables - accumulated PP with respect to time, center of pressure (COP) pattern, and measurements of the opposite foot, which are calculable only with a PP device - were considered in order to improve prediction performance. To conduct this study, the golf swing motions of 80 subjects were characterized as unilateral movement and GRF patterns as functions of individual characteristics. The prediction model was verified with 5-fold cross-validation utilizing the measured values of two force plates. As a result, prediction model (correlation coefficient, r=0.73-0.97) utilized accumulated PP and PP data of the opposite foot and showed the highest prediction accuracy in left-foot GRFV, GRMF, GRMT and right-foot GRFAP, GRFML, GRMF, GRMT. Likewise, another prediction model (r=0.83-0.98) utilized accumulated PP and COP patterns as input and showed the best accuracy in left-foot GRFAP, GRFML, GRMS and right-foot GRFV, GRMS. New methods based on the findings of the present study are expected to help resolve problems such as spatial limitation and limited analyzable motions in existing GRF measurement processes.

In general, three-dimensional ground reaction forces (GRFs) and ground reaction moments (GRMs) that occur during human gait are measured using a force plate, which are expensive and have spatial limitations. Therefore, we proposed a prediction model for GRFs and GRMs, which only uses plantar pressure information measured from insole pressure sensors with a wavelet neural network (WNN) and principal component analysis-mutual information (PCA-MI). For this, the prediction model estimated GRFs and GRMs with three different gait speeds (slow, normal, and fast groups) and healthy/pathological gait patterns (healthy and adolescent idiopathic scoliosis (AIS) groups). Model performance was validated using correlation coefficients (r) and the normalized root mean square error (NRMSE%) and was compared to the prediction accuracy of the previous methods using the same dataset. As a result, the performance of the GRF and GRM prediction model proposed in this study (slow group: r = 0.840–0.989 and NRMSE% = 10.693–15.894%; normal group: r = 0.847–0.988 and NRMSE% = 10.920–19.216%; fast group: r = 0.823–0.953 and NRMSE% = 12.009–20.182%; healthy group: r = 0.836–0.976 and NRMSE% = 12.920–18.088%; and AIS group: r = 0.917–0.993 and NRMSE% = 7.914–15.671%) was better than that of the prediction models suggested in previous studies for every group and component (p

In golf, it is crucial that unintended shots, such as slices, be minimized. However, it has proven rather difficult to improve golf performance via investigations of the causes of slicing, as this particular phenomenon is induced by a cooperative effect by each segment of the body, rather than by a single postural anomaly. Thus, the objective of this study was to isolate and characterize the factors causing slicing, and to present possibilities for the improvement of golf performance via the minimization of the number of slices executed, using a three dimensional motion capture system, combined with multiple regression analysis, artificial neural network, and fuzzy logic techniques. This study obtained some interesting results, such as the following: (1) We isolated 9 slice-inducing factors, using a stepwise method. (2) Our artificial neural network (ANN) accurately separated 'slice' from 'normal' shots (classification rate: 100%). (3)We could present the possibility of reducing the number of slice using the fuzzy logic. We expect that our data might be eventually used to improve golf performance.

Background During a golf swing, analysis of the movement in upper torso and pelvis is a key step to determine a motion control strategy for accurate and consistent shots. However, a majority of previous studies that have evaluated this movement limited their analysis only to the rotational movement of segments, and translational motions were not examined. Therefore, in this study, correlations between translational motions in the 3 axes, which occur between the upper torso and pelvis, were also examined. Methods The experiments were carried out with 14 male pro-golfers (age: 29 ± 8 years, career: 8.2 ± 4.8years) who registered in the Korea Professional Golf Association (KPGA). Six infrared cameras (VICON; Oxford Metrics, Oxford, UK) and SB-Clinc software (SWINGBANK Ltd, Korea) were used to collect optical marker trajectories. The center of mass (CoM) of each segment was calculated based on kinematic principal. In addition, peak value of CoM velocity and the time that each peak occurred in each segment during downswing was calculated. Also, using cross-correlation analysis, the degree of coupling and time lags of peak values occurred between and within segments (pelvis and upper torso) were investigated. Results As a result, a high coupling strength between upper torso and pelvis with an average correlation coefficient = 0.86 was observed, and the coupling between segments was higher than that within segments (correlation coefficient = 0.81 and 0.77, respectively). Conclusions Such a high coupling at the upper torso and pelvis can be used to reduce the degree of motion control in the central nervous system and maintain consistent patterns in the movement. The result of this study provides important information for the development of optimal golf swing movement control strategies in the future.

Kinetic information during human gait can be estimated with inverse dynamics, which is based on anthropometric, kinematic, and ground reaction data. While collecting ground reaction data with a force plate is useful, it is costly and requires regulated space. The goal of this study was to propose a new, accurate methodology for predicting ground reaction forces (GRFs) during level walking without the help of a force plate. To predict GRFs without a force plate, the traditional method of Newtonian mechanics was used for the single support phase. In addition, an artificial neural network (ANN) model was applied for the double support phase to solve statically indeterminate structure problems. The input variables of the ANN model, which were selected to have both dependency and independency, were limited to the trajectory, velocity, and acceleration of the whole segment's mass centre to minimise errors. The predicted GRFs were validated with actual GRFs through a ten-fold cross-validation method, and the correlation coefficients (R) for the ground forces were 0.918 in the medial-lateral axis, 0.985 in the anterior-posterior axis, and 0.991 in the vertical axis during gait. The ground moments were 0.987 in the sagittal plane, 0.841 in the frontal plane, and 0.868 in the transverse plane during gait. The high correlation coefficients(R) are due to the improvement of the prediction rate in the double support phase. This study also proved the possibility of calculating joint forces and moments based on the GRFs predicted with the proposed new hybrid method. Data generated with the proposed method may thus be used instead of raw GRF data in gait analysis and in calculating joint dynamic data using inverse dynamics.