Taeyong Sim

Prediction of gait speed from plantar pressure using artificial neural networks

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.

Upper torso and pelvis linear velocity during the downswing of elite golfers

BackgroundDuring 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.MethodsThe 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.ResultsAs 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).ConclusionsSuch 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.

Quasi-stiffness of the knee joint in flexion and extension during the golf swing

Abstract Biomechanical understanding of the knee joint during a golf swing is essential to improve performance and prevent injury. In this study, we quantified the flexion/extension angle and moment as the primary knee movement, and evaluated quasi-stiffness represented by moment–angle coupling in the knee joint. Eighteen skilled and 23 unskilled golfers participated in this study. Six infrared cameras and two force platforms were used to record a swing motion. The anatomical angle and moment were calculated from kinematic and kinetic models, and quasi-stiffness of the knee joint was determined as an instantaneous slope of moment–angle curves. The lead knee of the skilled group had decreased resistance duration compared with the unskilled group (P < 0.05), and the resistance duration of the lead knee was lower than that of the trail knee in the skilled group (P < 0.01). The lead knee of the skilled golfers had greater flexible excursion duration than the trail knee of the skilled golfers, and of both the lead and trail knees of the unskilled golfers. These results provide critical information for preventing knee injuries during a golf swing and developing rehabilitation strategies following surgery.

Predicting Complete Ground Reaction Forces and Moments During Gait With Insole Plantar Pressure Information Using a Wavelet Neural Network

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 < 0.05 or 0.01). The results indicated that the proposed model has improved performance compared to previous prediction models.

Improved determination of dynamic balance using the centre of mass and centre of pressure inclination variables in a complete golf swing cycle

Abstract Golf requires proper dynamic balance to accurately control the club head through a harmonious coordination of each human segment and joint. In this study, we evaluated the ability for dynamic balance during a golf swing by using the centre of mass (COM)–centre of pressure (COP) inclination variables. Twelve professional, 13 amateur and 10 novice golfers participated in this study. Six infrared cameras, two force platforms and SB-Clinic software were used to measure the net COM and COP trajectories. In order to evaluate dynamic balance ability, the COM–COP inclination angle, COM–COP inclination angular velocity and normalised COM–COP inclination angular jerk were used. Professional golfer group revealed a smaller COM–COP inclination angle and angular velocity than novice golfer group in the lead/trail direction (P < 0.01). In the normalised COM–COP inclination angular jerk, the professional golfer group showed a lower value than the other two groups in all directions. Professional golfers tend to exhibit improved dynamic balance, and this can be attributed to the neuromusculoskeletal system that maintains balance with proper postural control. This study has the potential to allow for an evaluation of the dynamic balance mechanism and will provide useful basic information for swing training and prevention of golf injuries.

Multi-class biological tissue classification based on a multi-classifier

Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher (p<0.05 or <0.01) than that of existing single classifiers for all electrode types. In particular, the classification accuracy of the proposed model was highest when the 3mm invasive electrode (Type-II) was used (sensitivity=97.33-100.00%; PPV=96.71-100.00%). The results of this study are an important contribution to achieving automatic optimal output power adjustment of USUs according to the properties of individual tissues.

A Novel Detection Model and Its Optimal Features to Classify Falls from Low- and High-Acceleration Activities of Daily Life Using an Insole Sensor System

In order to overcome the current limitations in current threshold-based and machine learning-based fall detectors, an insole system and novel fall classification model were created. Because high-acceleration activities have a high risk for falls, and because of the potential damage that is associated with falls during high-acceleration activities, four low-acceleration activities, four high-acceleration activities, and eight types of high-acceleration falls were performed by twenty young male subjects. Encompassing a total of 800 falls and 320 min of activities of daily life (ADLs), the created Support Vector Machine model’s Leave-One-Out cross-validation provides a fall detection sensitivity (0.996), specificity (1.000), and accuracy (0.999). These classification results are similar or superior to other fall detection models in the literature, while also including high-acceleration ADLs to challenge the classification model, and simultaneously reducing the burden that is associated with wearable sensors and increasing user comfort by inserting the insole system into the shoe.

How to quantify the transition phase during golf swing performance: Torsional load affects low back complaints during the transition phase

ABSTRACT The transition phase of a golf swing is considered to be a decisive instant required for a powerful swing. However, at the same time, the low back torsional loads during this phase can have a considerable effect on golf-related low back pain (LBP). Previous efforts to quantify the transition phase were hampered by problems with accuracy due to methodological limitations. In this study, vector-coding technique (VCT) method was proposed as a comprehensive methodology to quantify the precise transition phase and examine low back torsional load. Towards this end, transition phases were assessed using three different methods (VCT, lead hand speed and X-factor stretch) and compared; then, low back torsional load during the transition phase was examined. As a result, the importance of accurate transition phase quantification has been documented. The largest torsional loads were observed in healthy professional golfers (10.23 ± 1.69 N · kg−1), followed by professional golfers with a history of LBP (7.93 ± 1.79 N · kg−1), healthy amateur golfers (1.79 ± 1.05 N · kg−1) and amateur golfers with a history of LBP (0.99 ± 0.87 N · kg−1), which order was equal to that of the transition phase magnitudes of each group. These results indicate the relationship between the transition phase and LBP history and the dependency of the torsional load magnitude on the transition phase.

Comparison of Three Normalization Methods for 3D Joint Moment in the Asymmetric Rotational Human Movements in Golf Swing Analysis

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.

Analysis of sensory system aspects of postural stability during quiet standing in adolescent idiopathic scoliosis patients

BackgroundThe aim of this study was to quantitatively analyze quite standing postural stability of adolescent idiopathic scoliosis (AIS) patients in respect to three sensory systems (visual, vestibular, and somatosensory).MethodIn this study, we analyzed the anterior-posterior center of pressure (CoP) signal using discrete wavelet transform (DWT) between AIS patients (n = 32) and normal controls (n = 25) during quiet standing.ResultThe energy rate (∆EEYE%) of the CoP signal was significantly higher in the AIS group than that in the control group at levels corresponding to vestibular and somatosensory systems (p < 0.01).ConclusionsThis implies that AIS patients use strategies to compensate for possible head position changes and spinal asymmetry caused by morphological deformations of the spine through vestibular and somatosensory systems. This could be interpreted that such compensation could help them maintain postural stability during quiet standing. The interpretation of CoP signal during quiet standing in AIS patients will improve our understanding of changes in physical exercise ability due to morphological deformity of the spine. This result is useful for evaluating postural stability before and after treatments (spinal fusion, bracing, rehabilitation, and so on).