• Journal of Acupuncture Research
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• v.40 no.1
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• pp.44-52
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• 2023

Background: Previous studies have investigated technology-aided needling training systems for acupuncture on phantom models using various measurement techniques. In this study, we developed and validated a vision-based needling training system (noncontact measurement) and compared its training effectiveness with that of the traditional training method. Methods: Needle displacements during manipulation were analyzed using OpenCV to derive three parameters, i.e., needle insertion speed, needle insertion angle (needle tip direction), and needle insertion length. The system was validated in a laboratory setting and a needling training course. The performances of the novices (students) before and after training were compared with the experts. The technology-aided training method was also compared with the traditional training method. Results: Before the training, a significant difference in needle insertion speed was found between experts and novices. After the training, the novices approached the speed of the experts. Both training methods could improve the insertion speed of the novices after 10 training sessions. However, the technology-aided training group already showed improvement after five training sessions. Students and teachers showed positive attitudes toward the system. Conclusion: The results suggest that the technology-aided method using computer vision has similar training effectiveness to the traditional one and can potentially be used to speed up needling training.

• Journal of Korean Public Health Nursing
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• v.26 no.3
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• pp.417-427
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• 2012

Purpose: The purpose of this study was to examine the effect of a 12-week combined exercise training program on the body composition, physical fitness levels, and metabolic syndrome profiles of obese women. Methods: Twelve obese women were assigned to the combined exercise training program group. The women underwent training for 70-90 min/d, three times per week for a period of 12 weeks. Paired samples t-tests were performed using SPSS ver. 17.0 for analysis of the results. Results: The results of this study showed that body-composition parameters such as weight, fat-free mass, body fat mass, body-mass index, body fat, waist-hip ratio, basal metabolic rate, and intra-abdominal fat, physical fitness parameters such as muscle strength, muscle endurance, flexibility, and cardiac endurance, and metabolic syndrome biomarkers such as triglyceride levels, high-density lipoprotein cholesterol levels, glucose levels, systolic blood pressure, and waist circumference before participation the training program differed significantly from those after participation in the training program (p<0.05). However, diastolic blood pressure before participation in the training program did not differ significantly from that after participation in the training program (p>0.05). Conclusion: We concluded that a 12-week combined exercise training program could be a good exercise program for improvement of the body composition, physical fitness levels, and metabolic syndrome profiles of obese women.

• Journal of Biomedical Engineering Research
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• v.29 no.2
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• pp.114-121
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• 2008

Within 2 to 5 months after stroke, patients recover variable degrees of function, depending on the initial deficit. An impaired hand function is one of the most serious disability in chronic stroke patients. Therefore, to evaluate the extent of motor dysfunction in the hemiplegic hand is important in stroke rehabilitation. In this paper, motor recoveries in 8 chronic stroke patients with Fugl-Meyer (FM) and white matter changes before and after the training program with a designed bilateral symmetrical arm trainer (BSAT) system were examined. The training was performed at 1 hr/day, 5 days/week during 6weeks. In all patients, FM was significantly improved after the 6-week training. Diffusion tensor imaging (DTI) results showed that tractional anisotropy ratio (FAR) and fiber tracking ratio (FTR) in the posterior internal capsule were significantly increased after the training. It seemed that the cortical reorganization was induced by the 6 week training with the BSAT. In all parameters proposed this study, a significant correlation was found between these parameters (FAR and FTR) and motor recoveries. This study demonstrated that DTI technique could be useful in predicting motor recovery in chronic hemiparetic patients.

• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.50-59
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• 2024

Objective : This study aimed to conduct a bibliometric analysis on pelvic parameter related research over the last 30 years, analyzing trends, hotspots, and influential works within this field. Methods : A comprehensive Web of Science database search was performed. The search yielded 3249 results, focusing on articles and reviews published from 1992 to 2022 in English. Data was analyzed using CiteSpace and VOSviewer for keyword, authorship, and citation burst analysis, co-citation analysis, and clustering. Results : The number of publications and citations related to pelvic parameters has increased exponentially over the last 30 years. The USA leads in publication count with 1003 articles. Top publishing journals include the European Spine Journal, Spine, and Journal of Neurosurgery: Spine, with significant contributions by Schwab, Lafage V, and Protoptaltis. The most influential articles were identified using centrality and sigma values, indicating their role as key articles within the field. Research hotspots included spinal deformity, total hip arthroplasty, and sagittal alignment. Conclusion : Interest in pelvic parameter related research has grown significantly over the last three decades, indicating its relevance in modern orthopedics. The most influential works within this field have contributed to our understanding of spinal deformity, pelvic incidence, and their relation to total hip arthroplasty. This study provides a comprehensive overview of the trends and influential research in the field of pelvic parameters.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.218-230
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• 2024

Recently, many studies have been conducted for safety management in construction sites by incorporating computer vision. Anchor box parameters are used in state-of-the-art deep learning-based object detection and segmentation, and the optimized parameters are critical in the training process to ensure consistent accuracy. Those parameters are generally tuned by fixing the shape and size by the user's heuristic method, and a single parameter controls the training rate in the model. However, the anchor box parameters are sensitive depending on the type of object and the size of the object, and as the number of training data increases. There is a limit to reflecting all the characteristics of the training data with a single parameter. Therefore, this paper suggests a method of applying multiple parameters optimized through data split to solve the above-mentioned problem. Criteria for efficiently segmenting integrated training data according to object size, number of objects, and shape of objects were established, and the effectiveness of the proposed data split method was verified through a comparative study of conventional scheme and proposed methods.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.517-524
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• 2008

In order to classify an satellite imagery into geospatial features of interest, the supervised classification needs to be trained to distinguish these features through training sampling. However, even though an imagery is classified, different results of classification could be generated according to operator's experience and expertise in training process. Users who practically exploit an classification result to their applications need the research accomplishment for the consistent result as well as the accuracy improvement. The experiment includes the classification results for training process used VITD polygons as a prior probability and training parameter, instead of manual sampling. As results, classification accuracy using VITD polygons as prior probabilities shows the highest results in several methods. The training using unsupervised classification with VITD have produced similar classification results as manual training and/or with prior probability.

• The Journal of Korean Physical Therapy
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• v.16 no.1
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• pp.32-48
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• 2004

The purpose of this study was to evaluate the change of functional ambulation profile(FAP) and temporal-spatial gait parameters in hemiplegic patient by forceful respiratory exercise. 28 Hemiplegic patients due to stroke was randomized in 3 groups, forceful expiratory training(FET), forceful inspiratory training(FIT) and control group. In the experimental groups, ordinary physical therapy with forceful expiratory training and forceful inspiratory training for 20 minutes duration 3 times per week for 6 weeks were respectively performed. In the control group, only ordinary physical therapy was done. FAP and temporal-spatial gait parameters was measured at before and after experiments. The results of this experimental study were as follows : 1. In comparison of FAP before and after experiment, the FAP was significantly increased in the FET and FIT group (p<.01). In comparison of difference of FAP among 3 groups, there was the significant difference between the FIT group and the control group (p<.05). 2. The results of temporal-spatial gait parameters are as follows : 1) In comparison of gait velocity before and after experiment, the gait velocity was significantly increased in the FET and FIT group (p<.05). In comparison of difference of the gait velocity among 3 groups, there was the significantly difference between the FIT group and the control group (p<.05). 2) In comparison of gait cadence before and after experiment, the gait cadence was significantly increased in FIT group (p<.05). In comparison of the difference of the gait cadence among 3 groups, there was no significant difference between the FIT group and the control group (p>.05). Based on these results, it is concluded that the forced respiratory exercise program for 6 weeks can be improve the FAP and temporal-spatial gait parameters in hemiplegic patients. Therefore, the forced respiratory exercise is useful to improve the walking ability in hemiplegic patients.

• PNF and Movement
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• v.20 no.2
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• pp.295-306
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• 2022

Purpose: This study aimed to investigate the effect of robot-assisted gait training on the active ranges of motion, gait abilities, and biomechanical characteristics of gait in patients who underwent lower extremity surgery, and to verify the effectiveness and clinical usefulness of robot-assisted gait training. Methods: This study was conducted on 14 subjects who underwent lower extremity surgery. The subjects participated in robot-assisted gait training for 2 weeks. The active ranges of motion of the lower extremities were evaluated, and gait abilities were assessed using 10-m and 2-min walk tests. An STT Systems Inertial Measurement Unit was used to collect data on biomechanical characteristics during gait. Spatiotemporal parameters were used to measure cadence, step length, and velocity, and kinematic parameters were used to measure hip and knee joint movement during gait. Results: Significant improvements in the active ranges of motion of the hip and knee joints (flexion, extension, abduction, and adduction) and in the 10-m and 2-min walk test results were observed after robot-assisted gait training (p < 0.05). In addition, biomechanical characteristics of gait, spatiotemporal factors (cadence, step length, and velocity), and kinematic factors (gait hip flexion-extension, internal rotation-external rotation angle, and knee joint flexion-extension) were also significantly improved (p < 0.05). Conclusion: The results of this study are of clinical importance as they demonstrate that robot-assisted gait training can be used as an effective intervention method for patients who have undergone lower extremity surgery. Furthermore, the findings of this study are clinically meaningful as they expand the scope of robot-assisted gait training, which is currently mainly applied to patients with central nervous system conditions.

• Speech Sciences
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• v.5 no.1
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• pp.7-21
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• 1999

This paper proposes an improved training procedure in speech recognition based on the continuous density of the Hidden Markov Model (CDHMM). Of the three parameters (initial state distribution probability, state transition probability, output probability density function (p.d.f.) of state) governing the CDHMM model, we focus on the third parameter and propose an efficient algorithm that determines the p.d.f. of each state. It is known that the resulting CDHMM model converges to a local maximum point of parameter estimation via the iterative Expectation Maximization procedure. Specifically, we propose two independent algorithms that can be embedded in the segmental K -means training procedure by replacing relevant key steps; the adaptation of the number of mixture Gaussian p.d.f. and the initialization using the CDHMM parameters previously estimated. The proposed adaptation algorithm searches for the optimal number of mixture Gaussian humps to ensure that the p.d.f. is consistently re-estimated, enabling the model to converge toward the global maximum point. By applying an appropriate threshold value, which measures the amount of collective changes of weighted variances, the optimized number of mixture Gaussian branch is determined. The initialization algorithm essentially exploits the CDHMM parameters previously estimated and uses them as the basis for the current initial segmentation subroutine. It captures the trend of previous training history whereas the uniform segmentation decimates it. The recognition performance of the proposed adaptation procedures along with the suggested initialization is verified to be always better than that of existing training procedure using fixed number of mixture Gaussian p.d.f.