• 한국멀티미디어학회논문지
• /
• 제15권8호
• /
• pp.1059-1066
• /
• 2012

오늘날 스마트폰의 발전으로 스마트폰 내장 센서를 통해 사용자의 개인 정보를 쉽게 파악 할 수 있고 원한다면 사용자의 위치를 실시간으로 알아낼 수 있다. 그리하여 센서를 통해 추출된 데이터를 통해 동작인식과 생활 패턴 인식에 관한 연구가 급증하고 있다. 본 논문에서는 기존의 동작 인식 연구에서 추출되는 데이터를 정형화하기 위해 동작 데이터를 모델링하였다. 본 논문의 일상 동작 모델링은 이론적 분석이다. 동작을 크게 두 가지로 분류시켜 가속도 센서만으로 인식 가능한 기본 동작을 물리적 동작으로 정의하고 그 외 목적과 대상, 장소를 포함하는 모든 동작을 논리적 동작으로 분류시켰다. 모델링 된 데이터를 기반으로 각 동작의 특성에 맞게 가시화 하는 방안을 제안하였다. 본 연구를 통해 인간의 일상생활을 동작별로 간편하게 표준화 할 수 있고 기존의 동작 인식 연구에서 추출되는 동작 데이터를 사용자의 요구에 따라 가시화 할 수 있다.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
• /
• pp.861-864
• /
• 2011

Since national GHG reduction target by 2020 has been presented in Korea, the role of railroad has been reinforced within transport system due to the allocation of reduction target into sector. So, it is necessary to manage activity data systematically for the calculation of GHG emission in railroad. Now, the activity data of diesel consumption for NIR(National Inventory Report) are provided from oil supply and demand statistics. On the other hands, the activity data collected directly from railroad operating companies are used for GHG & Energy Target Management Act. This study aimed to assess the GHG emissions using two kinds of activity data related to the diesel consumption of railroad in 2009 and 2010. As a result, GHG emissions based on oil supply and demand statistics was 636 thousands ton $CO_{2e}$, but the activity data collected from railroad operating companies showed 649 thousands ton $CO_{2e}$ in 2009. Also, the gap of $CO_{2e}$ emission was increased in 2010. These trends were caused because oil supply and demand statistics included total diesel sales volume during 1 year and the activity data collected from railroad operating companies were the amount of diesel consumption only at railcar operation and maintenance step. In conclusion, it is important to develop the management and verification system of activity data with high reliability to substitute oil supply and demand statistics in railroad sector.

• 국제학술발표논문집
• /
• The 8th International Conference on Construction Engineering and Project Management
• /
• pp.443-452
• /
• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• 한국보건간호학회지
• /
• 제14권2호
• /
• pp.304-317
• /
• 2000

The purpose of this study is to identify the effect of the voluntary program experience with curriculum on self-esteem and depression in nursing college students. The research design utilized in this study was one group pre test-post test design. The data were gathered two times with questionnaire. First data were gathered before voluntary activity. And second data were gathered after instruction and five times voluntary activity. The data were analized by frequency, paired t-test. t-test. and ANOVA using the SAS program. The results were as follows 1. After the activity the scores of self-esteem were significantly higher than before the activity. 2. After the activity the scores of depression were significantly lower than before the activity. 3. The relationship between self-esteem and depression were negatives in both before and after the activity. 4. Before voluntary activity the scores of self-esteem in the class choosing the Culture Exploration were significantly high. but after the activity the scores of self-esteem were not significantly different. 5. As opinion on attendance of lecture class of voluntary activity program. before voluntary activity the scores of depression were not significantly different. but after the activity the scores of depression were significantly different.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권4호
• /
• pp.2060-2077
• /
• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• 대한물리의학회지
• /
• 제19권2호
• /
• pp.99-106
• /
• 2024

PURPOSE: This study is a data analysis study to determine the physical health problems and reasons for disabilities and activity limitation rates that limit daily life and social activities among the elderly population aged 75 or older in Korea. METHODS: This study data was extracted from the raw data of the 7th National Health and Welfare Survey (2016-2018). The subjects of this study were 1,995 elderly people (823 men, 1,172 women) aged 75 years or older. The collected data were analyzed using frequency analysis and logistic regression analysis. RESULTS: From 2016 to 2018, the activity limitation rate among the elderly population aged 75 or older in Korea was 20.6% for men, 24.6% for women, and 23.1% overall. The three major diseases with the highest frequency of activity limitations were back and neck problems (36.5%), arthritis and rheumatism (28.7%), and knee and leg pain (14.7%). Activity limitation due to old age was found to be 13.1%, making it the fourth most frequent reason. The rate of activity limitations due to mental retardation and obesity was found to be 0%. The three major activity limitation rates were significantly related to gender. CONCLUSION: The main diseases causing activity limitations among the elderly population aged 75 or older in Korea were back and neck problems, arthritis and rheumatism, and musculoskeletal diseases such as knee and leg pain. Therefore, it is believed that it can be used as basis data for reducing the activity limitation rate of the elderly population in the aging era.

• Journal of Information Technology Applications and Management
• /
• 제30권1호
• /
• pp.71-80
• /
• 2023

The purpose of this study is to promote understanding of physical activity and metabolic syndrome in middle-aged adults and to provide basic data of interventions development for the prevention and management of metabolic syndrome. Using the 2020 data for Korea National Health and Nutrition Examination Survey, 1,786 middle-aged adults between the ages of 40 and 64 with no missing data were analyzed. As a result of the study, 56.5(±2.1)% of men and 52.9(±1.81)% of women were sufficient activity group among physical activity, and the proportion of men was higher than that of women, but it was not statistically significant(t=1.27, p=.207). The prevalence of metabolic syndrome was 38.9(±2.1%) of men and 25.4(±1.5)% of women, the prevalence of men was significantly higher than that of women(t=5.12, p<.001). Compared to the insufficient activity group, the sufficient activity group had a 0.71(95% CI: 0.57~0.88) times the risk of developing low HDL(high density lipoprotein)- cholesterol(p=.002), and this pattern was maintained even after adjusting for age, education level, body mass index, smoking status, and drinking status(p=.002). Based on the results of this study, a physical activity and metabolic syndrome risk group in middle-aged adults should be selected, and physical activity promotion program to improve high density lipoprotein-cholesterol among metabolic syndrome indicators should be developed.

• International Journal of Computer Science & Network Security
• /
• 제21권2호
• /
• 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.

• 간호행정학회지
• /
• 제18권2호
• /
• pp.222-233
• /
• 2012

Purpose: This study was conducted to develop an evaluation framework for QI activity in medical institutions and to analyze QI activity cases by applying the developed evaluation framework. Method: A four-phase process was employed to develop the evaluation framework, and a descriptive survey was used for the QI case study. Data were collected in April, 2010 by examining 157 QI activity cases presented at conferences and published in Journal of Korean Society of Quality Assurance in Health Care over the past three years. Developed QI activity evaluation instruments were used for data collection. Data were analyzed using the SPSS 18.0 for Windows program. Result: A QI Activity Evaluation Framework was developed. This framework consisted of 45 items. The department with the highest level of QI participation was the nursing department. The most frequent QI activity theme was patient safety. QI activity levels in Korean medical institutions are relatively equalized without significant differences according to institution characteristics. Conclusions: From the quality aspect of QI activity, more systematic and scientific approaches are required to upgrade QI activity. This study could provide methodological guidelines for QI activity and be useful in setting goals and directions for QI activity in medical institutions in Korea.

• Journal of Multimedia Information System
• /
• 제2권2호
• /
• pp.233-240
• /
• 2015

Mobile devices are becoming increasingly sophisticated and the latest generation of smartphones now incorporates many diverse and powerful sensors. These sensors include acceleration sensor, magnetic field sensor, light sensor, proximity sensor, gyroscope sensor, pressure sensor, rotation vector sensor, gravity sensor and orientation sensor. The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper, we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity that a user is performing. To implement our system, we collected labeled accelerometer data from 10 users as they performed daily activities such as "phone detached", "idle", "walking", "running", and "jumping", and then aggregated this time series data into examples that summarize the user activity 5-minute intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users-just by having them carry cell phones in their pockets.