Human joint motion can be kinematically described in three planes, typically the frontal, sagittal, and transverse, and related to experimentally measured data. The selection of reference systems is ...a prerequisite for accurate kinematic analysis and resulting development of the equations of motion. Moreover, the development of analysis techniques for the minimization of errors, due to skin movement or body deformation, during experiments involving human locomotion is a critically important step, without which accurate results in this type of experiment are an impossibility. The traditional kinematic analysis method is the Angular-based method (ABM), which utilizes the Euler angle or the Bryant angle. However, this analysis method tends to increase cumulative errors due to skin movement. Therefore, the objective of this study was to propose a new kinematic analysis method, Position-based method (PBM), which directly applies position displacement data to represent locomotion. The PBM presented here was designed to minimize cumulative errors via considerations of angle changes and translational motion between markers occurring due to skin movements. In order to verify the efficacy and accuracy of the developed PBM, the mean value of joint dislocation at the knee during one gait cycle and the pattern of three dimensional translation motion of the tibiofemoral joint at the knee, in both flexion and extension, were accessed via ABM and via new method, PBM, with a Local Reference system (LRS) and Segmental Reference system (SRS), and then the data were compared between the two techniques. Our results indicate that the proposed PBM resulted in improved accuracy in terms of motion analysis, as compared to ABM, with the LRS and SRS.
Despite the recent advances in 3D camera technology, there is still a need to increase the accuracy of 3D reconstruction and a demand for a more user-friendly interface for camera calibration. To ...achieve this goal, the accuracy of the DLT method must be improved as well as the user convenience of the calibration method during motion capture using the wand. Therefore, in this study a new multi-camera calibration method for an optical motion capture system was developed. The proposed calibration procedure consists of two steps: (1) the calibration parameters are estimated using the DLT method from the three-axis calibration frame; and (2) the parameters estimated in the first step are improved iteratively through nonlinear optimization using a wand dance procedure. The objective function to be minimized is the 3D reconstruction error of the three-axis calibration frame and the difference between the actual distance and reconstructed distance of the marker at the wand. The proposed method was verified by comparing the RMS error and the mean difference of the proposed method, DLT method and wand method. In these experiments, the data obtained from a distance of 390mm between the markers and the wand was selected. In this analysis, no statistical significant difference between the proposed method and DLT method was observed, and the proposed method was more accurate than the other wand method. As a result, the proposed wand method holds great promise to increase the overall accuracy of the DLT algorithm and provide better user convenience.
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.
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.
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).
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.
Product lifecycle management (PLM) is an innovative manufacturing paradigm that allows a company’s engineering contents to be developed and integrated with all business processes through the entire ...product lifecycle in the extended enterprise. PLM extends PPR (product, process and manufacturing resource) content knowledge into other enterprise business processes by coupling e-business technologies with applications focused on the product development and production, such as ergonomic analysis. In PLM, most researches focus on the product information, which has been managed separately from manufacturing information including process, resource, and human. For activities such as process and operation planning, esign for manufacturing and assembly (DFMA) analysis, equipment and tool design, workstation design, the ergonomic analysis must be performed concurrently with the planning, and in a manner that integrates it with the entire product lifecycle. To achieve concurrent and integrated ergonomic analysis, an integrated schema that includes the product, process, manufacturing resource and human is essential. In this paper, PPR
+H
is defined and suggested as an XML-based approach to manage and integrate all the information necessary for ergonomic analysis. This approach includes the product, process, manufacturing resource, and human information in PLM, and also includes the relations among these elements. And, we develop the PPR
+H
Integrator to support a concurrent and integrated ergonomic analysis. This tool can extract PPR and human information from company’s diverse legacy systems, such as roduct data management (PDM) and manufacturing process management (MPM). We suggest implementations and present a case study for an automotive general assembly shop showing that effective and reliable ergonomic analysis is possible, and can be performed in a concurrent and integrated manner.
Slip-trip falls are one of the leading causes of injury in dynamic constructive workplace environments. To solve the problem of slips-trip in the construction industry, it is important not only to ...predict falls in order to be able to apply various fall prevention systems, but also to detect and monitor slip-trip near-falls in order to eliminate various dangerous anomalies in a timely manner. In this study, we propose a deep learning-based method for predicting and classifying activities of daily living (ADLs), trip falls, slip falls, trip near-falls and slip near-falls using a waist-attached wearable device with an embedded single inertial measurement unit (IMU) sensor containing a 3-axis accelerometer and a gyroscope. A total of 34 young and healthy participants took part in experiment to collect accelerometer and gyroscope data while performing 8 types of ADLs, slip-trip falls, and slip-trip near-falls. The data was processed and then 30 features were extracted from them. The resulting feature data was then transformed using bicubic interpolation to fit the input data size of the modified interpretation of LeNet-5 Convolutional Neural Network (CNN) deep learning algorithm. After completing 5-folds cross validation with 5 evaluation criteria, the proposed prediction and classification method showed high performance with an accuracy of about 0.9039, a specificity of about 0.9753 and F1-score of about 0.9049. Therefore, it is believed that the method proposed in this study can be used not only to increase accuracy by reducing false alarms of systems for early detection and prevention of slip-trip falls, but also for various systems for detecting and timely elimination of dangerous anomalies in the construction workplace.
The purpose of this study was to select the appropriate input variables for the development of an expert system to analyze the gait asymmetry of patients with idiopathic scoliosis. Gait experiments ...were performed with 12 healthy female adolescents and 16 female adolescents with untreated adolescent idiopathic scoliosis. The experimental equipment included six infrared cameras and two ground reaction force platforms. By using a 3D human model, gait elements, kinematic and kinetic data were extracted. Self-organizing map and genetic algorithm were used for proper selection of input variables, and these methods were validated by using auto regression models, which were described in previous studies. Sixty gait variables based on a literature review were selected, and Self-organizing map was used to maintain the independency between the input variables, and the 39 independent retaining variables were chosen. Also, in order to identify the inputs exhibiting a significant relationship with the output, a genetic algorithm-general regression neural network was applied; and the frequency of the solution set was measured by genetic algorithm iteration. A stepwise method was applied based on the variables with high frequency, and final 11 input variables were selected. Furthermore, a back propagation artificial neural network with high accuracy 96.3(3.2)%, which can discriminate patients from the normal subjects, was developed with selected 11 input variables. Therefore, the results of this study can be used as input variables for the development of a gait asymmetry expert system.
A number of studies have examined the validity of using spectral parameters, such as median frequency (F
med
) and Dimitrov spectral index of muscle fatigue (FI
nsm5
) from the surface EMG signal ...during dynamic exercise, to assess muscle fatigue. Despite these studies, the ability to accurately predict endurance capacity using these spectral parameters during repetitive dynamic contractions is limited. The main purpose of this study was to examine the potential of using the incremental time, defined as the time when the Dimitrov spectral index increases to a certain value relative to the initial value, to predict the endurance time (T
end
), which was determined when the subject became exhausted and could no longer follow the fixed contraction cycle. Ten healthy subjects performed five sets of voluntary isotonic contractions until they could only produce 10% and 20% of their maximal voluntary contraction level (MVC). The T
end
for all subjects were within the following ranges: 157±62 s at 10% MVC; 75±31 s at 20% MVC. Spectral parameters such as median frequency and Dimitrov spectral index were extracted from every contraction segment and estimated using linear regressive analysis at every contraction. The initial slope of both spectral parameters and the incremental time of the Dimitrov spectral index were compared as a predictor of endurance time. Significant correlations were found: 1) between T
end
and contraction level (p<0.05) and 2) between T
end
and the incremental time when the Dimitrov spectral index was above 130% of the increment with respect to the initial value at 20% MVC (p<0.01). In conclusion, the incremental time of the Dimitrov spectral index could be used to describe the changes in the spectral content of the sEMG signal and could be used as a good predictor of endurance time in comparison to the initial slope of the median frequency.
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