• Fashion & Textile Research Journal
• /
• v.22 no.5
• /
• pp.607-616
• /
• 2020

Health care wearables are devices that are attached to or combined with the human body to improve the health care capabilities of the human body that can be safely and adjustable according to preference. This study provided direction for future research on healthcare wearables in the field of clothing science, considering trends observed in this field from 2010 to 2019. Over the last 10 years, 812 studies have been conducted on healthcare wearables in Korea. Research has increased significantly since 2015, with a large number of articles published in this field. The research for this study was broken down into the following categories: technology development, marketing analysis, and technology analysis. The results according to the research method demonstrated that development and production methods were used most frequently, followed by trend analysis, experiment and evaluation, and survey. An analysis of keywords in the articles studied revealed that device, healthcare, big data (biometric data and database), and healthcare convergence technologies were trending. Similarly, detailed research on healthcare wearable devices and related technologies was actively being conducted. However, focusing on fiber, textiles, design, and clothing articles, in relation to the field of clothing in healthcare wearables, only 81 articles were found on this topic (10.0%), which was low compared to other studies. Therefore, it was determined that more research on healthcare wearables is necessary in the field of clothing.

• Journal of Information Processing Systems
• /
• v.20 no.2
• /
• pp.159-172
• /
• 2024

The number of healthcare products available for pets has increased in recent times, which has prompted active research into wearable devices for pets. However, the data collected through such devices are limited by outliers and missing values owing to the anomalous and irregular characteristics of pets. Hence, we propose pet behavior recognition based on a hybrid one-dimensional convolutional neural network (CNN) and long short- term memory (LSTM) model using pet wearable devices. An Arduino-based pet wearable device was first fabricated to collect data for behavior recognition, where gyroscope and accelerometer values were collected using the device. Then, data augmentation was performed after replacing any missing values and outliers via preprocessing. At this time, the behaviors were classified into five types. To prevent bias from specific actions in the data augmentation, the number of datasets was compared and balanced, and CNN-LSTM-based deep learning was performed. The five subdivided behaviors and overall performance were then evaluated, and the overall accuracy of behavior recognition was found to be about 88.76%.

• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• pp.221-228
• /
• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• Proceedings of the Korea Technology Innovation Society Conference
• /
• 2013.10a
• /
• pp.845-845
• /
• 2013

• Review of Korea Contents Association
• /
• v.16 no.4
• /
• pp.14-19
• /
• 2018

• Journal of the Korea Convergence Society
• /
• v.11 no.4
• /
• pp.151-156
• /
• 2020

The purpose of this study is to design a process for developing wearable sports helmet devices by utilizing IoT server technology, focusing on sports where helmet wear is essential at sports sites. This enables customized training of athletes by continuously accumulating personal biometric information during training, checking players' condition based on data, and informing them of injury prevention and dangerous situations. In addition, the wearable device that can be useful when the training place is likely to damage the physical health due to heat waves or extremes can provide a foundation for improving the performance. Since such technology can be applied not only to the sports field but also to the society such as the industrial field or the underprivileged, it can be expected to be expandable.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.3
• /
• pp.267-276
• /
• 2020

Manufacturing workers are easily exposed to the risk of musculoskeletal disorders caused by repetitive tasks in their working environment. This is due to problems with occupational characteristics that repeatedly use the body. However, the current lack of monitoring systems for monitoring and prevention has led to an increase in workers' exposure to risks each year. This paper presents how to solve these problems in real working environment by producing wearable devices using IMU sensors. After wearing a wearable type device, the user's movement is judged through data analysis by receiving the rotation value according to musculoskeletal movement. At this time, the risk is determined by measuring the number of rotations of the user by eliminating bias and eliminating cumulative error, acquiring sophisticated data, and analyzing it in the form of dynamic threshold values. Using the wearable device proposed in this paper, the effect of this method could be checked through a web page measuring the number of rotations for elbow musculoskeletal disorders.

• Journal of Information Technology Applications and Management
• /
• v.26 no.1
• /
• pp.21-38
• /
• 2019

This study starts from the question, "Are people of the age 60 and over equally 'old?' "As the aging population has rapidly become a global issue, it is a timely question to think about whether it is appropriate to classify people aged 60 and over as senior citizens monolithically based on their chronological age. Thanks to the advancement of medical technology and ever-increasing life expectancy, there may be more differences than we thought in terms of cognitive and behavioral patterns among the elderly population. In order to further investigate this question, this study focuses on technology acceptance behavior of 132 participants over the age of 60 towards a wearable healthcare device. The results show that there were interesting behavioral differences among participants depending on their cognitive capabilities. More specifically, participants with high cognitive capability (Superagers) consider the usefulness and the social aspects (social norm and image) of using wearable healthcare technology. Whereas for those with relatively low cognitive capability (non-Superagers), usefulness of using the technology was not a significant factor, and they mainly considered social norm and image. Our findings imply that the current monolithic application of chronological age to classify the elderly population should be carefully reconsidered because people aged over 60 years old may not always share homogeneous cognitive and behavioral patterns.

• The Journal of the Korea Contents Association
• /
• v.15 no.5
• /
• pp.498-506
• /
• 2015

As smart phone market has been rapidly saturated, wearable devices have been emerging as a new growth power in the post smart phone era. This study aims to comprehend the influential factors of purchasing intention of product features(perceived usefulness, perceived ease of use) and individual characteristics(innovation, fashion leadership, self-efficacy, concern for health) of wrist wearable device. The result shows that fashion leadership and health concern among consumers' individuality, and perceived usefulness and perceived usability among product features are proved to be significant factors. This means that both usefulness and usability have significant impacts on purchase intention of wearable device and product development should be made to enhance user experience.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.29 no.5
• /
• pp.284-288
• /
• 2016

Among the various physiological information that could be obtained from human body, heartbeat rate is a commonly used vital sign in the clinical milieu. Photoplethysography (PPG) sensor is incorporated into many wearable healthcare devices because of its advantages such as simplicity of hardware structure and low-cost. However, healthcare device employing PPG sensor has been issued in susceptibility of light and motion artifact. In this paper, to develop the real-time heart rate measurement device that is less sensitive to the external noises, we have fabricated an ultra-small wireless LC resonant pressure sensor by MEMS process. After performance evaluation in linearity and repeatability of the MEMS pressure sensor, heartbeat waveform and rate on radial artery were obtained by using resonant frequency-pressure conversion method. The measured data using the proposed heartbeat rate measurement system was validated by comparing it with the data of an commercialized heart rate measurement device. Result of the proposed device was agreed well to that of the commercialized device. The obtained real time heartbeat wave and rate were displayed on personal mobile system by bluetooth communication.