• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.10
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• pp.865-878
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• 2017

To realize the healthy life of human, mental, physical, and environmental factors must be managed continuously and stably. In order to manage human health, the 21st century healthcare field is essential ongoing interactions and convergence with ICT technologies. Such demands have created a convergence of technologies (fusion technology) in combination with the heterogeneous technologies. And, with the convergence of medical technology and ICT technologies, the development of personalized therapy environments is created. Advances in ICT-converged healthcare services are progressing due to the development of diverse wearable devices. Such ICT fusion system is exponentially increasing the complexity of the ICT convergence healthcare system and is resulting in various technical, institutional, environmental, and cultural issues. This study explores the status of developments in ICT healthcare technologies from the past to date, identifies major technology and policy issues to address these challenges. Finally it will recommend healthcare policies and a future road-map.

• Korean Journal of Materials Research
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• v.33 no.7
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• pp.295-301
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• 2023

Thermoelectric (TE) energy harvesting, which converts available thermal resources into electrical energy, is attracting significant attention, as it facilitates wireless and self-powered electronics. Recently, as demand for portable/wearable electronic devices and sensors increases, organic-inorganic TE films with polymeric matrix are being studied to realize flexible thermoelectric energy harvesters (f-TEHs). Here, we developed flexible organic-inorganic TE films with p-type Bi_0.5Sb_1.5Te₃ powder and polymeric matrices such as poly(3,4-eethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) and poly (vinylidene fluoride) (PVDF). The fabricated TE films with a PEDOT:PSS matrix and 1 wt% of multi-walled carbon nanotube (MWCNT) exhibited a power factor value of 3.96 µW·m^-1·K^-2 which is about 2.8 times higher than that of PVDF-based TE film. We also fabricated f-TEHs using both types of TE films and investigated the TE output performance. The f-TEH made of PEDOT:PSS-based TE films harvested the maximum load voltage of 3.4 mV, with a load current of 17.4 µA, and output power of 15.7 nW at a temperature difference of 25 K, whereas the f-TEH with PVDF-based TE films generated values of 0.6 mV, 3.3 µA, and 0.54 nW. This study will broaden the fields of the research on methods to improve TE efficiency and the development of flexible organic-inorganic TE films and f-TEH.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.579-586
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• 2022

Modular building is a fast-growing construction method, mainly due to its ability to drastically reduce the amount of time it takes to construct a building and produce higher-quality buildings at a more consistent rate. However, while modular construction is relatively safer than traditional construction methods, workers are still exposed to hazards that lead to injuries and fatalities, and these hazards could be controlled using emerging smart technologies. Currently, limited information is available at the intersection of modular construction, safety risk, and smart safety technologies. This paper aims to investigate what aspects of modular construction are most dangerous for its workers, highlight specific risks in its processes, and propose ways to utilize smart technologies to mitigate these safety risks. Findings from the archival analysis of accident reports in Occupational Safety and Health Administration (OSHA) Fatality and Catastrophe Investigation Summaries indicate that 114 significant injuries were reported between 2002 and 2021, of which 67 were fatalities. About 72% of fatalities occurred during the installation phase, while 57% were caused by crushing and 85% of crash-related incidents were caused by jack failure/slippage. IoT-enabled wearable sensing devices, computer vision, smart safety harness, and Augment and Virtual Reality were identified as potential solutions for mitigating identified safety risks. The present study contributes to knowledge by identifying important safety trends, critical safety risk factors and proposing practical emerging methods for controlling these risks.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.787-800
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• 2023

This paper is a study aimed at facilitating a comprehensive understanding of the music metaverse platform that will emerge in the era of Web 3.0 and exploring productive strategies for its construction. We examine the significance of the metaverse music platform from various perspectives and investigate the developmental process of digital music platforms from Web 1.0 to 3.0. Subsequently, assuming the emergence of metaverse platforms as a transition to Web 3.0, we align this transition with technological(VR technology, wearable devices, generative AI), cultural(digital avatars, fandom), and economic(NFT) discussions related to Web 3.0. These discussions are integrated with the developmental strategies of the metaverse music platform. Through this study, we hope to enhance the understanding of the metaverse music platform and provide insights into potential construction strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• Composites Research
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• v.36 no.5
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• pp.342-348
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• 2023

There has been increasing demand for real-time monitoring of body and ambient temperatures using wearable devices. Graphene-based thermistors have been developed for high-performance flexible temperature sensors. In this study, the temperature coefficient of resistance (TCR) of monolayer graphene was controlled by coating polyethylenimine (PEI) on graphene surfaces to enhance its temperature-sensing performances. Monolayer graphene grown by chemical vapor deposition (CVD) was wet-transferred onto a target substrate. To facilitate the interfacial doping by PEI, the hydrophobic graphene surface was altered to be hydrophilic by oxygen plasma treatments while minimizing defect generation. The effect of PEI doping on graphene was confirmed using a back-gated field-effect transistor (FET). The CVD-grown monolayer graphene coated with PEI exhibited an improved TCR of -0.49(±0.03) %/K in a temperature range of 30~50℃.

• Journal of Korean Biological Nursing Science
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• v.25 no.4
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• pp.306-315
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• 2023

Purpose: This study aimed to evaluate the rest-activity circadian rhythm (RAR) using data obtained from wearable actigraph devices in hospitalized older adults with mild cognitive impairment (MCI), and to investigate its relationship with salivary alpha amylase (sAA). Methods: This secondary data analysis used data from the Hospitalized Older Adults' Cognition and Physical Activity Study. Actigraph data for 3-4 days were analyzed for RAR. RAR indices such as interdaily stability (IS), intradaily variability (IV), activity level during the most active 10-hour period and during the most least active 5-hour period, and relative amplitude (RA) were calculated. Data on sAA collected in the morning and general characteristics, including body mass index (BMI), were analyzed. Results: Data from 92 hospitalized older adults with MCI were analyzed. The IS, IV, RA were 0.23, 0.73, 0.88, respectively. The average level of sAA was 77.02 U/mL, and a higher level of sAA was significantly associated with better IS and RA in the regression analysis, while age, BMI, and cognitive level were not. BMI showed positive correlations with IS and RA. Conclusion: RAR in the hospitalized older adults with MCI was attenuated, showing especially low IS, which implies they failed to maintain regular and repetitive 24-hour RAR. Increased sAA and BMI were associated with robust RAR. Nurses need to pay attention to maintain robust RAR in hospitalized older adults with MCI, and strategies should be developed to improve their RAR.

• Smart Media Journal
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• v.12 no.11
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• pp.1-9
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• 2023

Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.

• Journal of the Korean Society of Safety
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• v.39 no.1
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• pp.41-54
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• 2024

Efforts to reduce on-site safety incidents have expanded, leading to active research in this domain. However, a systematic analysis to improve the utility of technology is lacking. In this study, we conducted a survey on the various institutional and technical improvement measures to promote the application of smart construction safety technology over three years after the implementation of the "Smart Safety Equipment Support Project." The results showed that financial constraint was the primary obstacle in the adoption of this innovation. Fostering a flexible environment in the utilization of management fees and financial support of projects was determined to aid in the extensive application of the technology. Ensuring cost efficiency and user-friendliness were principally necessary for technical enhancements in the smart construction safety technology. Technologies, such as VR/AR safety education, real-time location tracking, wearable devices, and innovation on streamlining safety-related work efficiency, had been anticipated to contribute to on-site safety. Operating a smart safety control center was expected to be beneficial in the systematic securing of data and reduction of safety blind spots. Effective methods had been suggested to overcome the barriers that hindered the development and application of smart construction safety technology. This study facilitates in the technological improvements in this field.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.