Purpose: Accuracy and stability during drug administration via syringe pumps are vital in intensive care units. However, the effect of frictional characteristic of the pump-equipped syringe and connection protocol of syringe pumps remain elusive. Materials and Methods: Here, these effects, pump arrangement in multiple connection on workload, and hydraulic pressure at the outlet of each syringe pump were evaluated. Results: Statistical analysis demonstrated that the driving workload and hydraulic pressure were substantially affected by variations in the mechanical quality of syringe and arrangement of the connected syringe pumps. Conclusion: Therefore, to increase accuracy and stability, clinics should use high-quality syringes and arrange syringe pumps to minimize positional variations due to different infusion rates in intensive care units.

This study aimed to develop a machine learning model for classifying elbow angles using sEMG signals. Previous research in rehabilitation and robotic arm control has often combined sEMG and IMU sensors to measure muscle activity and precise angles. However, in rehabilitation, utilizing sEMG signals alone may be more practical than using multiple sensors. The use of sensors like IMUs increases equipment costs and complicates data processing, making interpretation more challenging. In contrast, sEMG signals reflect muscle activation and can predict angles simply and effectively, making them suitable for assessing elbow movements. This study classified elbow flexion and extension angles into 15°, 30°, 45°, 60°, and 90° using only sEMG sensors. sEMG data were collected from the biceps brachii and triceps brachii muscles and analyzed using Random Forest, SVM, and XGBoost models to evaluate angle classification performance. The experimental results showed high accuracy for all three models, with SVM and XGBoost demonstrating particularly superior performance. These findings suggest that sEMG signals alone can effectively predict elbow angles in applications such as rehabilitation and arm control, providing a valuable tool for assessing and aiding recovery of motor functions in rehabilitation therapy.

Purpose: Forest healing programs conducted in forests or recreational forests are known to be very effective in stabilizing the mind and body. This effect is analyzed to be due to the five sensory stimulation factors such as natural scenery, natural sounds, and scents. Among them, this paper focused on sound. In particular, it was estimated that HFC (high frequency components) in the inaudible band among the sounds from the forest would help in stabilizing the mind and body. In this study, forest sounds (audible frequency ~ HFC) collected in deep mountains were applied to subjects and the effect of HFC on stabilizing the mind and body was analyzed. Materials and Methods: Forest sounds were collected from the forest and HFC were provided to subjects using high-performance audio. The subjects consisted of 18 people around 45 years of age (SD3.2), and the experiment was conducted in a chamber where noiselessness was guaranteed. The subjects were divided into two groups and HFC were stimulated auditory and tactile. In order to verify the homogeneity between the groups, the same experiment was repeated by cross-applying auditory and tactile sensations to each group. The change in alpha waves was measured using EEG before and after the experiment. Results: Forest sounds were collected by specifying more than 5 forests. HFC were present in all forests, and most were around 31KHz (31,616Hz, SD772). The effect of HFC was verified by reading alpha waves. Both auditory and tactile groups achieved significant results (p<0.002, p<0.0001), and the tactile group was more effective. Conclusion: This experiment was conducted at a very basic level to verify the existence of HFC in the forest and their effects, but it is thought to be sufficient to confirm the possibility. In this study, HFC collection in the forest was conducted at 2-4 PM in early June. HFC are thought to vary depending on the season, time, location, and surrounding environment. Since the changes in HFC and their effects on brain waves may also vary, further experiments will be necessary. It is thought that various technological and industrial applications are possible.

An emerging paradigm in the development of biomaterials for bone tissue regeneration involves creating scaffolds embedded with cells or bioactive molecules to promote bone tissue formation. In this context, cryogels have garnered significant attention as delivery carriers for bioactive molecules, owing to their highly interconnected porous structure. Among such molecules, calcium ions (Ca²⁺) and oxygen (O₂) are particularly notable for enhancing osteogenesis. In this study, we present gelatin-based cryogels as dual-delivery carriers for Ca²⁺ and O₂, designed to enhance the proliferation of preosteoblasts. These bioactive cryogels are fabricated through a calcium peroxide-mediated reaction followed by lyophilization. Our results demonstrate that the cryogels sustain the release of Ca²⁺ for up to 28 days while delivering O₂ rapidly over 24 hours. Notably, the Ca²⁺ released from the cryogels significantly accelerates osteogenic cell proliferation. In summary, we propose that these dual-releasing cryogels represent a promising platform for bone tissue engineering and regenerative medicine.

In-hospital patients who need long-term catheterized urinary support requests repetitive flushing of urine in the urine bag, which can increase the workload of nursing staffs. In this study, a deep learning-based liquid level monitoring technique (based on EfficientDet Lite models) that can estimate the status of in-bag liquid level in real-time from the periodically photographed bag images captured using a camera installed in front of the urine bag. In experiments, the error rates between the manually-calculated in-bag liquid area and the model-extracted in-bag liquid area were 1.05 ± 1.58%, and those between the manually-calculated scale area and the model-extracted scale area were 2.57 ± 2.54%, respectively. In addition, the error rates between the actual amount of in-bag liquid and the model-calculated in-bag liquid amount were 2.28 ± 4.97%, respectively. The implemented technique could successfully estimate the time-varying amount of liquid in the urine bag. It can be applied to develop a smart flush-request alarm system for patients with long-term catheterized urinary support that can reduce the workload of nursing staffs while increasing the safety of patients.

This study presents an advanced image processing algorithm for analyzing Multiple Allergen Simultaneous Test (MAST) strips, addressing the limitations of manual interpretation methods. The algorithm is designed to analyze LG Chem's 108-allergen MAST strips, capable of simultaneously processing 15 strips in a single image under varying lighting conditions. Key to the algorithm's robustness is a novel statistical analysis method for grade classification that effectively minimizes light interference. The method utilizes a calibration strip to establish baseline measurements and employs pixel intensity comparisons between test and background areas. This approach allows for accurate result recognition across five levels: negative, weak positive, moderate positive, strong positive, and very strong positive. The algorithm incorporates adaptive thresholding and morphological operations for precise strip detection and segmentation. Crucially, it performs individual analyses on each detection area, calculating pixel intensity means (PIM) for both test and background regions. By comparing the PIM differences against calibrated thresholds, the system can accurately classify results even under non-uniform lighting conditions. Experimental results demonstrated 100% accuracy across various strip positions and lighting scenarios, showcasing the algorithm's resilience against environmental factors such as ambient light variations and structural obstructions. This high accuracy was maintained even when strips were placed in areas with significantly different light intensities, proving the effectiveness of the statistical approach in mitigating light interference. While the study was limited to laboratory conditions, the algorithm's ability to adapt to varying light conditions suggests its potential for improving MAST strip analysis in clinical settings. This automated approach significantly enhances the efficiency and consistency of allergy diagnostics, offering a solution that balances accuracy with practicality in real-world clinical applications.

Recently, biometric signals such as facial recognition and fingerprint recognition have become widely used for user authentication. However, as spoofing attacks targeting these biometric signals have increased, serious issues such as identity theft and financial fraud have arisen. To address this, this paper proposes a more secure user authentication method using sEMG signals, which, even for the same muscle, vary across individuals. Through this approach, the potential of sEMG signals for user authentication is validated. A Siamese network is employed to allow authentication even with a small amount of data. The participants performed a fist-clenching motion 120 times, and the data pairs were created such that pairs from the same person were labeled as 1, and pairs from different people were labeled as 0. Using this data, five-fold cross-validation was conducted on the Siamese network, achieving an average accuracy of 97.37%. During testing, an average accuracy of 95.83% was observed. Additionally, the system demonstrated excellent performance, with a Precision of 99.89%, Recall of 92.99%, and F1 Score of 96.28%.

In medical tumor cell ablation study, the use of irreversible electroporation is rapidly increasing because of its convenience, fast time recovery, and minimum injury of adjacent organs or vessels in the treatment. Since applications of different kinds of electroporation pulse parameter to a tumor make a big difference in the cell death results, it is very difficult to standardize pulse parameters for the clinical application. Thus, electroporator capable of outputting a variety of pulse type is essential in such study. However, current commercial electroporators are so expensive and the choices are limited. This study is to develop a new inexpensive electroporator and accompanied electrodes satisfying currently required performances at the practical clinical applications. To achieve this purpose, we carefully wrote the specification by pertaining literature and conventional products comparison study, and designed by using high-end embedded technology. As a result of the tests, we could satisfy pre-assigned electroporator specification, and for co-developed electrodes, we were successful to get official certifications from Korea authority. Additionally, we performed in-vivo animal experiment with the developed electroporator and electrodes in a Korea authorized facility, and found significant results as expected.

In this study, we conducted a brief review of 254 studies on blood pressure estimation using photoplethysmography (PPG). The analysis revealed a growing trend in research on blood pressure prediction using PPG. Initially, simple methods like regression analysis were commonly used, but recent studies predominantly employ deep learning techniques. Additionally, many studies use other signals in conjunction with PPG for blood pressure estimation. There were numerous cases where PPG signals were measured using wearable devices or custom hardware, and a significant number of studies utilized publicly available datasets. The features used in blood pressure prediction were classified into six categories for analysis, and it was found that the combination of two feature categories was the most common approach. Overall, Temporal features were the most frequently used. Based on these findings, it is suggested that future research on blood pressure estimation using PPG should carefully consider factors such as signal characteristics, preprocessing, and feature extraction when developing models.

Major Depressive Disorder is a significant mental health condition triggered by various social and environmental factors. Traditional diagnostic methods for depression primarily rely on subjective assessments, including self-reported surveys and psychiatric consultations, which often lack objectivity and consistency. To address these limitations, this study developed a machine learning-based depression prediction model using ensemble learning techniques and applied SHAP to quantitatively analyze the contribution of individual features to model predictions. The study utilized real-world data collected over three years from 160 participants using smartwatches. To increase the size and diversity of dataset, a segmentation technique was applied resulting in 3,080 independent samples. The dataset included sleep metrics, heart rate, step count, and PHQ-9 survey results. Ensemble learning models were trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. Among these, XGBoost, LightGBM, and CatBoost achieved the highest accuracy of 99.50%. SHAP analysis identified key predictors of depression, with sleep metrics specifically variability in individual sleep duration and average night sleep duration emerging as the most significant contributors across all ensemble learning models. Specifically, night sleep duration, variability in overall sleep states, proportion of Deep sleep during night sleep, variability in REM sleep duration, and proportion of REM sleep during total daily sleep were found to be important features for detection MDD. Future work aims to enhance diagnostic accuracy and robustness by expanding the datasets and feature variables, integrating advanced deep learning, and leveraging explainable AI with wearable device data to enable personalized, early detection and intervention for depression prevention and management.

This study aimed to evaluate the clinical efficacy and safety of transcutaneous electrical stimulation (TES) in patients with severe ocular surface disease. TES has been recognized as a potential treatment for enhancing corneal epithelial healing and stabilizing the tear film, offering a solution to the limitations of conventional therapies that provide only temporary symptom relief. A single-blind, sham-controlled, prospective clinical trial was conducted with seven patients suffering from persistent corneal epithelial defects. The participants were divided into a TES group receiving TES therapy and a control group receiving sham stimulation. The primary outcome was the change in the corneal epithelial defect area, and the secondary outcomes included the change in the corneal sensitivity, tear secretion, and tear film breakup time (TBUT). Each patient used the device daily for 30 minutes over a 4-week period. After four weeks, the TES group showed a significant reduction in the corneal epithelial defect area, decreasing to 10% of the initial value, while the control group exhibited a 132% increase (p = 0.034). However, no statistically significant changes were observed in corneal sensitivity, tear secretion, and TBUT, suggesting limited effects on tear secretion and tear film stability in severe ocular surface disease. TES can effectively promote corneal epithelial healing in patients with severe ocular surface disease.

Ultrasound technology is being integrated with optical systems in biomedical, industrial, and scientific applications. The development of transparent ultrasound transducers enables successful integration by allowing precise alignment between optical and acoustic paths. However, acoustic characterization of transparent ultrasound transducers has mainly focused on basic imaging parameters, while comprehensive evaluation of acoustic output and safety validation remains limited. This study aims to provide comprehensive acoustic characterization and safety validation of transparent ultrasound transducer (TT) through systematic comparison with opaque ultrasound transducer (OT). In this study, TT and OT were fabricated with the same structural design and systematically evaluated by measuring acoustic, electrical, and safety parameters. Both exhibited similar performance with center frequencies (18-19 MHz), bandwidths (44-54%), electrical impedances (59 Ω and 30 Ω), and similar beam diameter (0.75-0.83 mm). The acoustic output parameters of TT (1.14 MPa, 4.29 mW/cm²) were comparable to OT (1.12 MPa, 4.42 mW/cm²), with mechanical (0.26) and thermal indices (<0.01) within safety guidelines. A systematic comparative analysis between TT and OT, focusing on acoustic characteristics and safety parameters, provides essential reference data. This comprehensive evaluation enables more informed decisions in selecting and implementing TTs in optical-ultrasound integrated systems.

Clinical ultrasound is a powerful diagnostic tool that enables the non-invasive detection of various diseases without the risks associated with radioactive exposure. The addition of Doppler imaging enhances its capabilities by allowing the evaluation of blood flow, which is crucial for diagnosing vascular conditions. However, the accuracy of vascular imaging is often compromised by strong clutter signals, which interfere with the detection of blood flow signals. While conventional clutter filtering techniques, such as Singular Value Decomposition (SVD), can effectively separate these signals, they are computationally intensive and may not perform well in real-time applications. Furthermore, detecting signals from microvessels is particularly challenging due to their low intensity, necessitating more advanced filtering techniques. In this study, we propose a novel clutter filtering approach based on a deep learning framework for improve vascular imaging. Furthermore, it does not rely on the use of contrast agents, making it safer and more accessible for clinical use. By overcoming the limitations of existing techniques, this framework has the potential to significantly advance the field of vascular ultrasound imaging.

Global supply chain disruptions, exemplified by the COVID-19 pandemic, have underscored the necessity for a stable supply system for essential medical devices. While South Korea has a well-established management system for essential medicines, there is a lack of a system for medical devices critical to patient care. The high dependence on imported medical devices in South Korea may lead to potential disruptions in supply, which pose substantial social implications. This study conducts a comparative analysis of the advanced systems employed by the United States, the European Union, and Japan, with the aim of proposing the establishment of a management system for essential medical devices in South Korea. Based on this analysis, the study identifies key strategies for the designation and stabilization of the supply of medical devices and suggests directions for enhancing the resilience of the healthcare system and emergency response capabilities. This includes incorporating criteria for designating essential medical devices, implementing preemptive measures, and strengthening supply chain management within South Korea.