• Health Policy and Management
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• v.27 no.2
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• pp.177-185
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• 2017

Background: The mental problems of the elderly are at issue as a serious social phenomenon. The purpose of this study is to identify risk factors affecting the mood disorders of the elderly. Methods: The subjects were 1,779,236 aged ${\geq}65$ and participated in health screening. Dependent variable was mood disorders. Independent variables were consisted of community level (regional deprivation index and healthcare resources) and individual level (sex, age, insurance type, disability, smoking, alcohol, physical activity, body mass index, and healthcare utilization). Multilevel logistic regression was performed. Results: At the individual level, women, employed insured, severely disabled people, heavy alcohol drinkers, high-intensity physical activity, body mass index, and patients who had chronic disease and severe disease were significantly associated with mood disorders. As the age has increased, it has let increase of mood disorders. At the community level, as the regional deprivation index has increased by 1, mood disorders has been increased by 1.005 times. The intra-class coefficient was 7.04%. Conclusion: We found individual and community level factors are associated with mood disorders. Systematic approach is essential to reduce mood disorders.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.137-142
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• 2019

This paper Various services exist to detect and monitor abnormal event. However, most services focus on fires and gas leaks. so It is impossible to prevent and respond to emergency situations for the elderly and severely disabled people living alone. In this study, AI model is designed and compared to detect abnormal event of heart rate signal which is considered to be the most important among various bio signals. Specifically, electrocardiogram (ECG) data is collected using Physionet's MIT-BIH Arrhythmia Database, an open medical data. The collected data is transformed in different ways. We then compare the trained AI model with the modified and ECG data.

• Therapeutic Science for Rehabilitation
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• v.9 no.2
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• pp.99-117
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• 2020

Objective : The purpose of this study was to study the effects of occupation-based community rehabilitation on occupational performance skills and activities of daily living in stroke disabled persons living in the community, and to investigate the changes in occupation quality and satisfaction. Methods : In this single-subject ABA design study with follow-up evaluation, one severely disabled person diagnosed with stroke who lived in the community was recruited. The procedure consisted of a total of 25 sessions for 17 weeks. Intervention was according to occupation-based community rehabilitation, and the researcher visited the subject's home. Individualized intervention was applied according to the OTIPM. The intervention was composed of task assignment and feedback, home environment modification, information-related caregiver education, and community resource network. The evaluation of each session included the changes in the frequency of occupational performance skills, the quality of occupational performance in daily life, and the changes in occupational satisfaction, activities of daily living, quality of life, and maintenance of in the occupational performance skills during follow-up. The results were visually analyzed using a bar graph and a linear graph. Results : The results showed that the occupation-based community rehabilitation improved activities of daily living such as putting on socks, shoes slip-on, and upper body dressing garment within reach. Within the framework of the AMPS, it was confirmed that the quality of occupational performance was improved in all the subjects, and the degree of satisfaction also improved. Conclusion : This study showed that occupation-based rehabilitation can improve the occupational performance skills of stroke home disabled people positively affect the quality of occupational performance in daily life. Therefore, I think it is meaningful that useful for them.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.309-338
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• 2010

In this paper, we studied the brain-computer interface (BCI). BCIs help severely disabled people to control external devices by analyzing their brain signals evoked from motor imageries. The findings in the field of neurophysiology revealed that the power of $\beta$(14-26 Hz) and $\mu$(8-12 Hz) rhythms decreases or increases in synchrony of the underlying neuronal populations in the sensorymotor cortex when people imagine the movement of their body parts. These are called Event-Related Desynchronization / Synchronization (ERD/ERS), respectively. We implemented a BCI-based mouse interface system which enabled subjects to control a computer mouse cursor into four different directions (e.g., up, down, left, and right) by analyzing brain signal patterns online. Tongue, foot, left-hand, and right-hand motor imageries were utilized to stimulate a human brain. We used a non-invasive EEG which records brain's spontaneous electrical activity over a short period of time by placing electrodes on the scalp. Because of the nature of the EEG signals, i.e., low amplitude and vulnerability to artifacts and noise, it is hard to analyze and classify brain signals measured by EEG directly. In order to overcome these obstacles, we applied statistical machine-learning techniques. We could achieve high performance in the classification of four motor imageries by employing Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) which transformed input EEG signals into a new coordinate system making the variances among different motor imagery signals maximized for easy classification. From the inspection of the topographies of the results, we could also confirm ERD/ERS appeared at different brain areas for different motor imageries showing the correspondence with the anatomical and neurophysiological knowledge.