No abstract

This study was performed to investigate the prevalence of musculoskeletal disorders in players of a variety of traditional Korean classical instruments: gayageum, geomungo, ajaeng, and haegeum. A large percentage of these musicians suffer from musculoskeletal pain of various body parts. However, there has been no research regarding the extent of musculoskeletal disorders in players of these instruments. Through a focus group interview, a questionnaire was developed to investigate musculoskeletal disorders. The questionnaire consisted of four parts: demographic factors, performance factors, musculoskeletal disorder symptoms, and musculoskeletal disorder experiences. Eighty-six expert players participated in this survey. The data from the survey were analyzed by correlation analysis and chi-squared analysis. Musculoskeletal disorders symptoms and severe pain were reported in the neck, shoulder, back and knee. These problems were statistically related to height for players of the gayageum and geomungo. In addition, the musculoskeletal disorder experience of geomungo players was correlated with age and career length. The symptoms of playing-related musculoskeletal disorders in traditional Korean music players were reported as being mostly observed in the neck, shoulder, back, and knee. In addition, these symptoms were related to various demographic factors such as age, height, career length, and hobby styles. The results of this study can be used as preliminary data for developing guidelines to prevent traditional Korean musical instrument players from developing musculoskeletal injuries.

This study evaluated the legibility difference between e-books and paper books from the viewpoint of readability, eye fatigue and subjective discomfort by using an eye tracker. The results showed that paper books provided a better experience than e-books. This indicates that the readability of e-books needs further improvement in relation to paper books.

Stroke is considered as a major cause of death and neurological disorders commonly associated with elderly people. Electrocardiogram (ECG) signals are used as a powerful tool in diagnosing stroke, and the analysis of ECG signals has become the focus of stroke research. ECG changes and autonomic dysfunction are reportedly seen in patients with stroke. This study aimed to analyze the ECG features and develop a classification model with highly ranked ECG features as input variables based on machine-learning techniques for diagnosing stroke disease. The study included 52 stroke patients (mean age 72.7 years, 63% male) and 80 control subjects (mean age 75.5 years, 39% male) for a total of 132 elderly subjects. Resting ECG signals in the lying down position are measured using the BIOPAC MP150 system. The ECG signals are denoised using the discrete wavelet transform (DWT) method, and the features such as heart rate variability (HRV), indices of time and spectral domains and statistical and impulsive metrics, in addition to fiducial features, are extracted and analyzed. Our results showed that the values of the HRV variables were lower in the stroke group, revealing autonomic dysfunction in stroke patients. A statistically significant difference was observed in low-frequency (LF)/high-frequency (HF), time interval measured after the S wave to the beginning of the T wave (ST) and time interval measured from the beginning of the Q wave to the end of the T wave (QT) (p < 0.05) between the groups. Our study also highlighted some of the risk factors of stroke, such as age, male sex and dyslipidemia (p < 0.05), that are statistically significant. The k-nearest neighbors (KNN) model showed the highest classification results (accuracy 96.6%, precision 94.3%, recall 99.1% and F1-score 96.6%) than the random forest, support vector machine (SVM), Naïve Bayes and logistic regression models. Thus, our study reported some of the notable ECG changes in the study participants and also indicated that ECG could aid in diagnosing stroke disease.

BACKGROUND: It is important for the designers and manufacturers to produce products with good usability and fit. The Korean anthropometric database is important as Korean industries focus on developing products with better usability. OBJECTIVE: To investigate how well the present national Size Korea anthropometric database adopted by companies and industries in Korea, in particular how well the dynamic anthropometric data are adopted. METHODS:The investigation methodology consisted of three stages: literature review, expert review and in-depth interviews. The literature review was based on a PubMed search. An online survey of 1,000 Korean civilians was carried out using a questionnaire developed by experts in anthropometry. Finally, industry professionals and professors participated in in-depth interviews. RESULTS:The anthropometric data appear to be used mainly by universities and research institutions in Korea. Many industries including the automobile, medical, shipping, mattress and construction industries need dynamic anthropometric data, such as range of motion, angle between body parts, spinal curvature, centre of pressure and so on. CONCLUSIONS: The Size Korea database-building process needs to be modified to take into account the needs of companies and industries.