Ahnryul Choi

Prediction of ground reaction forces during gait based on kinematics and a neural network model

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 segments 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.

Kinematic evaluation of movement smoothness in golf: relationship between the normalized jerk cost of body joints and the clubhead

BackgroundWhen the human body is introduced to a new motion or movement, it learns the placement of different body parts, sequential muscle control, and coordination between muscles to achieve necessary positions, and it hones this new skill over time and repetition. Previous studies have demonstrated definite differences in the smoothness of body movements with different levels of training, i.e., amateurs compared with professionals. Therefore, we tested the hypothesis that skilled golfers swing a driver with a smoother motion than do unskilled golfers. In addition, the relationship between the smoothness of body joints and that of the clubhead was evaluated to provide further insight into the mechanism of smooth golf swing.MethodsTwo subject groups (skilled and unskilled) participated in the experiment. The skilled group comprised 20 male professional golfers registered with the Korea Professional Golf Association, and the unskilled group comprised 19 amateur golfers who enjoy golf as a hobby. Six infrared cameras (VICON460 system) were used to record the 3D trajectories of markers attached to the clubhead and body segments, and the resulting data was evaluated with kinematic analysis. A physical quantity called jerk was calculated to investigate differences in smoothness during downswing between the two study groups.ResultsThe hypothesis that skilled golfers swing a driver with a smoother motion than do unskilled golfers was supported. The normalized jerk of the clubhead of skilled golfers was lower than that of unskilled golfers in the anterior/posterior, medial/lateral, and proximal/distal directions. Most human joints, especially in the lower body, had statistically significant lower normalized jerk values in the skilled group. In addition, the normalized jerk of the skilled group’s lower body joints had a distinct positive correlation with the normalized jerk of the clubhead with r = 0.657 (p < 0.01).ConclusionsThe result of this study showed that skilled golfers have smoother swings than unskilled golfers during the downswing and revealed that the smoothness of a clubhead trajectory is related more to the smoothness of the lower body joints than that of the upper body joints. These findings can be used to understand the mechanisms behind smooth golf swings and, eventually, to improve golf performance.

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.

Kinematic relationship between rotation of lumbar spine and hip joints during golf swing in professional golfers.

BackgroundUnderstanding the kinematics of the lumbar spine and hip joints during a golf swing is a basic step for identifying swing-specific factors associated with low back pain. The objective of this study was to examine the kinematic relationship between rotational movement of the lumbar spine and hip joints during a golf swing.MethodsFifteen professional golfers participated in this study with employment of six infrared cameras to record their golf swings. Anatomical reference system of the upper torso, pelvis and thigh segments, and the location of each hip and knee joint were defined by the protocols of the kinematic model of previous studies. Lumbar spine and hip joint rotational angle was calculated utilizing the Euler angle method. Cross-correlation and angle–angle plot was used to examine the degree of kinematic relationship between joints.ResultsA fairly strong coupling relationship was shown between the lumbar spine and hip rotational movements with an average correlation of 0.81. Leading hip contribution to overall rotation was markedly high in the early stage of the downswing, while the lumbar spine contributed greater towards the end of the downswing; however, the relative contributions of the trailing hip and lumbar spine were nearly equal during the entire downswing.ConclusionsMost of the professional golfers participated in this study used a similar coordination strategy when moving their hips and lumbar spine during golf swings. The rotation of hips was observed to be more efficient in producing the overall rotation during the downswing when compared to the backswing. These results provide quantitative information to better understand the lumbar spine and hip joint kinematic characteristics of professional golfers. This study will have great potential to be used as a normal control data for the comparison with kinematic information among golfers with low back pain and for further investigation of golf swing-specific factors associated with injury.

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.

Inter-joint coordination between hips and trunk during downswings: Effects on the clubhead speed

ABSTRACT Understanding of the inter-joint coordination between rotational movement of each hip and trunk in golf would provide basic knowledge regarding how the neuromuscular system organises the related joints to perform a successful swing motion. In this study, we evaluated the inter-joint coordination characteristics between rotational movement of the hips and trunk during golf downswings. Twenty-one right-handed male professional golfers were recruited for this study. Infrared cameras were installed to capture the swing motion. The axial rotation angle, angular velocity and inter-joint coordination were calculated by the Euler angle, numerical difference method and continuous relative phase, respectively. A more typical inter-joint coordination demonstrated in the leading hip/trunk than trailing hip/trunk. Three coordination characteristics of the leading hip/trunk reported a significant relationship with clubhead speed at impact (r < −0.5) in male professional golfers. The increased rotation difference between the leading hip and trunk in the overall downswing phase as well as the faster rotation of the leading hip compared to that of the trunk in the early downswing play important roles in increasing clubhead speed. These novel inter-joint coordination strategies have the great potential to use a biomechanical guideline to improve the golf swing performance of unskilled golfers.

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.

Prediction of Cobb-angle for Monitoring System in Adolescent Girls with Idiopathic Scoliosis using Multiple Regression Analysis

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.