• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.267-275
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• 2021

Smart application is developed in this paper by using an android-based platform to automatically determine the human emergency state (Lifesaver) by using different technology sensors of the mobile. In practice, this Lifesaver has many applications, and it can be easily combined with other applications as well to determine the emergency of humans. For example, if an old human falls due to some medical reasons, then this application is automatically determining the human state and then calls a person from this emergency contact list. Moreover, if the car accidentally crashes due to an accident, then the Lifesaver application is also helping to call a person who is on the emergency contact list to save human life. Therefore, the main objective of this project is to develop an application that can save human life. As a result, the proposed Lifesaver application is utilized to assist the person to get immediate attention in case of absence of help in four different situations. To develop the Lifesaver system, the GPS is also integrated to get the exact location of a human in case of emergency. Moreover, the emergency list of friends and authorities is also maintained to develop this application. To test and evaluate the Lifesaver system, the 50 different human data are collected with different age groups in the range of (40-70) and the performance of the Lifesaver application is also evaluated and compared with other state-of-the-art applications. On average, the Lifesaver system is achieved 95.5% detection accuracy and the value of 91.5 based on emergency index metric, which is outperformed compared to other applications in this domain.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.89-101
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• 2021

Scale-up of ICT startups is not easy because of limited capabilities, lack of resources, and immature networks for the business. Therefore, this research selected a representative startup succeeded in scale-up and applied the ERIS model in analyzing their scale-up process in the initial stages of scale-up. Analysis of qualitative data collected revealed that the entrepreneurs' experience, convergence of knowledge between diverse industries, participation in public-sector-led R&D, management of communication channels between customers and businesses, and utilization of project-oriented campaigns are found to be critical success factors in scaling up ICT startups. Academically, this study validates the utility of ERIS model in analyzing the scale-up process. For practitioners, this study will be used as a reference for strategic development in seucring the competitiveness of the initial market of ICT startups and scale-up.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1137-1144
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• 2020

This paper aims to provide strategic suggestions by analyzing technology trends related to big data, IoT, and artificial intelligence. To this end, analysis was performed using the 2018 national R&D information, and major basic analysis and language network analysis were performed. As a result of the analysis, research and development related to big data, IoT, and artificial intelligence are being conducted by focusing on the basic and development stages, and it was found that universities and SMEs have a high proportion. In addition, as a result of the language network analysis, it is judged that the related fields are mainly research for use in the smart farm and healthcare fields. Based on these research results, first, big data is essential to use artificial intelligence, and personal identification research should be conducted more actively. Second, they argued that full-cycle support is needed for technology commercialization, not simple R&D activities, and the need to expand application fields.

• Journal of Dental Rehabilitation and Applied Science
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• v.36 no.4
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• pp.203-210
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• 2020

By facing the era of the 4th industrial revolution, personalized medical services for patients are expanding with the development of information and communications technology. With these changes, digital medical devices have begun to be used to support diagnosis, patient monitoring, and decision-making of diseases, and recently software medical devices for the purpose of preventing, managing, or treating disorders or diseases have become popular. The aim of this article is to understand the current concept and status of Software as a Medical Device (SaMD), which are actively being carried out in the United States, and to find out what fields can be applied in the future. In addition, it intends to find out the Korean domestic policy trends related to smart healthcare and find out the application of digital software as a medical devices that can be used in dental clinic to keep pace with the upcoming changes in the medical field.

• Journal of the Korean Society of Physical Medicine
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• v.17 no.4
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• pp.151-160
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• 2022

PURPOSE: To evaluate the usability of a kiosk-based healthcare service that provides a fitness evaluation and customized exercise program for elderly or chronic musculoskeletal system patients. METHODS: To evaluate the usability of the customized exercise service program, healthy adults (n=20) from Welfare B, located in Gwangju, were selected and studied. Subjective safety, operability, and satisfaction of individual users were obtained as data by distributing questionnaires to subjects who experienced this program and having them fill out the questionnaire. For descriptive statistics related to the survey, frequency analysis was used to determine the frequency and ratio of the variable values of the measurement items. RESULTS: As a result of the usability evaluation, the average score was 4.166, and the average score of each item was 4.025 for safety, 4.272 for operability, and 4.143 for satisfaction. Most users obtained high satisfaction and positive impressions. CONCLUSION: The HARUFIT service, a user-customized exercise program used in this study, can be developed into a device that can improve self-management ability and increase understanding of health care by providing customized exercise based on the results of physical fitness evaluation. It is possible to diversify health management methods and maximize the effect of exercise by making exercise a habit of chronic musculoskeletal disease patients or the elderly using these smart devices.

• Journal of Industrial Convergence
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• v.20 no.9
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• pp.71-79
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• 2022

This study comprised a systematic review and meta-analysis to determine how digital health care to relieve depression of the elderly. The CINAHL, Embase, Medline, DBpia, KERIS, KISS and RISS were used. As a result of the systematic search, a total of 4,071 studies were assessed and six studies were ultimately selected based on the inclusion and exclusion criteria. Of these, a total of five studies were available for meta-analysis; the effect size was calculated. The effect size of digital health care was statistically significant in reducing depression(SMD=-4.73, 95% CI -7.44 to -2.01, Z=3.41, p=.0007). Since only web-based programs are included in the analysis among types of digital health care, we suggest that we consider ways to reduce depression in the elderly by applying various digital health care in the future.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.241-250
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• 2022

It is known that chronic pain and injury of upper limb joint tissue in manual wheelchair users is usually caused by muscle imbalance, and the propulsion speed is reported to increase this muscle imbalance. In this study, kinematic variables, electromyography, and ultrasonographic images of the upper limb were measured and analyzed at two different propulsion speeds to provide a quantitative basis for the risk of upper extremity joint injury. Eleven patients with spinal cord injury for the experimental group (G_E) and 27 healthy adults for the control group (G_C) participated in this study. Joint angles and electromyography were measured while subjects performed self-selected comfortable and fast-speed wheelchair propulsion. Ultrasound images were recorded before and after each propulsion task to measure the acromiohumeral distance (AHD). The range of motion of the shoulder (14.35 deg in G_E; 20.24 deg in G_C) and elbow (5.25 deg in G_E; 2.57 deg in G_C) joints were significantly decreased (p<0.001). Muscle activation levels of the anterior deltoid, posterior deltoid, biceps brachii, and triceps brachii increased at fast propulsion. Specifically, triceps brachii showed a significant increase in muscle activation at fast propulsion. AHD decreased at fast propulsion. Moreover, the AHD of G_E was already narrowed by about 60% compared to the G_C from the pre-tests. Increased load on wheelchair propulsion, such as fast propulsion, is considered to cause upper limb joint impingement and soft tissue injury due to overuse of the extensor muscles in a narrow joint space. It is expected that the results of this study can be a quantitative and objective basis for training and rehabilitation for manual wheelchair users to prevent joint pain and damage.

• Korean Journal of Optics and Photonics
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• v.34 no.2
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• pp.45-52
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• 2023

In this study, we have investigated the effect of photobiomodulation (PBM) on corneal wound healing, using a low-power light-emitting diode (LED) at different wavelengths. We found that LEDs with wavelengths ranging from 623 to 940 nm had no significant cytotoxic effects on corneal epithelial cells. The effect of PBM on promoting cell migration was analyzed by scratch assay, and it was found that PBM at 623 nm significantly increased cell migration and promoted wound healing. Furthermore, the expression of genes related to cell migration and wound healing was analyzed, and it was found that PBM at 623 nm upregulated the expression of the genes FGF-1 and MMP2, which are known to promote cell proliferation and extracellular matrix degradation. These findings suggest that PBM with low-powered light at specific wavelengths, particularly 623 nm, could be utilized to treat corneal injury.

• Journal of Marine Bioscience and Biotechnology
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• v.15 no.2
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• pp.59-66
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• 2023

Dictyopteris divaricata, a type of marine brown algae, has been studied for its various biological properties, including anti-inflammatory, antidiabetic, and whitening effects. However, its potential antiobesity effects have not been extensively explored. This study aimed to examine the impact of D. divaricata ethanol extract (DDE) on adipocyte differentiation and adipogenesis using 3T3-L1 preadipocytes. Our results showed that when 3T3-L1 preadipocytes were treated with noncytotoxic concentrations of DDE there was a concentration-dependent decrease in fat accumulation rate and triglycerid production compared with the control. Furthermore, DDE significantly reduced the expression of transcription factors (PPARγ, C/EBPα, and SREBP-1) and fatty acid transport protein (FABP4), which are crucial for 3T3-L1 preadipocyte differentiation. These findings suggest that DDE may exhibit antiobesity effects by suppressing the expression of lipogenic transcription factors and fatty acid transport proteins. Therefore, DDE holds potential as a therapeutic agent for obesity.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.