• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.209-216
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• 2009

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.84-95
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• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.444-448
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• 2008

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2:image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• The Journal of Information Systems
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• v.29 no.1
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• pp.201-224
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• 2020

Purpose The study focuses on the physical activity behavior change effect of smart wristband, which is the most popular type of fitness tracker nowadays. The purpose of the research is to investigate how people's workout behavior may change after wearing a smart band and examine what kind of role persuasive design plays in behavior change. Design/Methodology/Approach This research employed an experimental study to examine whether the user's workout behaviors changed after using wristband from the "Behavior Wizard" perspective. A representative smart wristband from a major vendor was selected as the objects of experimental study. In the experiment, by comparing users' workout behavior before and after using the wristband, behavior changes of all the experiment participants were classified into one of the 15 behavior change types. Users perceived persuasive design characteristics were measured and group differences were tested among different behavior change groups. Findings This research found that nearly half of the participants changed their workout behavior while half retained their workout status or no exercise status. Half of the participants who did not do exercise in their spare time started walking in the experiment. Results also showed that participants who started working out perceived higher levels of persuasive design devised into the smart band than participants who preserved no exercise status, except for facilitation and reward strategies. Participants who retained workout and those who increased workout frequency perceived no difference in smart band persuasive design.

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.390-405
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• 2021

Gait analysis trackers have been developed to monitor daily gait patterns to improve users' running performance and reduce the risk of injuries. A variety of gait analysis trackers are available on the market(e.g., foot pods, insoles). Depending on the type of gait analysis tracker, users' discomfort or satisfaction as well as required properties may differ. Hence, the purpose of this study was to compare and analyze user experience of three different types of commercial shoe-mounted gait analysis trackers and their mobile applications in a laboratory environment using questionnaires based on actual experiences of each product. Ten males and ten females who regularly enjoy walking and running exercises participated in the experiment. After the participants set up the tracker and application themselves without support from researchers, ten to thirty minutes' exercise was permitted on each product. Following this, the participants answered questionnaires containing evaluation variables on the device and mobile application, as well as satisfaction, intention to use, recommendation, and purchase. In addition, they were asked questions about the attractive features and shortcomings of each device and application. The results showed that the PRO-SPECS^® smart insole was preferred over the others for ease of use, perceived durability, psychological burden of the design, and usefulness of the information provided by the application. Along with the results of questionnaire, this study also discussed strategies and recommendations for future product design and development.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.1-8
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• 2019

Smart pole is one of mega trend in smart city development. It has multi-function to get as much as data from the street to better city management. And smart street lighting of smart pole as core part of smart city development is being implemented by many cities in globally. Refer to the latest edition of Navigant Research's Smart City Tracker includes smart city projects in 221 cities, a quarter of which are deploying smart street lighting ranging from initial pilots to citywide and regional deployments that span tens and even hundreds of thousands of lights. The most important feature of smart street lighting solution is "Connectivity" through the IoT technology. In order to implement the smart city, we should have so-called intelligent watchdog and mesh networked post for keeping the various smart city technology development progress and operation properly. Smart street lighting solution could be main infrastructure as an enabler for a range of smart city applications. We can talk about the expected role of smart street lighting for the smart city development on this paper.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1717-1726
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• 2018

This study is for looking into Factors affecting the Intention of using about Fitness Tracker based on IoT by Flow Theory and UTAUT Model. The independent variables of the IoT-based fitness tracker have been selected as Challenge, Skill Factors used in the Flow experience theory and selected the personal characteristics of health perception, enjoyment, Social Influence, and Facilitating Conditions. In addition UTAUT theory was used as parameter of Performance Expectancy and Effort Expectancy and intention of Use behavior was selected as a dependent variable. For verifying research hypothesis, This study collected 126 surveys data and carried out a statistical analysis by Smart PLS. The result shows that new Flow Experience and Personal Characteristic Factors with key variables in the UTAUT Model affect significantly to Intention of using about Fitness Wearable environment based IoT.

• International Journal of Advanced Culture Technology
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• v.8 no.1
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• pp.218-225
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• 2020

Purpose Wearable devices are widely used in our daily life. The purpose of this study is to investigate the relationship between persuasive designs of fitness trackers and users' physical activity behavior. Methods To test the research model, data was collected from a web-based survey in China, resulting in an effective sample of 166 usable questionnaires. The survey was restricted only to respondents who wear a fitness tracker. Results The sample surveyed in this study indicated that half of the respondents had been wearing a smart fitness tracker shorter than one year, and only 27% were long-time users (longer than two years). Dialogue support and social support strategies were both proved to be effective in increasing users' workout behavior intention. Social support strategies had a greater effect on behavior change than dialogue support strategies. Conclusion The findings from this study make several contributions to the practice. Wearable devices developers can employ the result from this study to help them design devices, which can persuade people to do more exercises and preserve a healthier life.

• Journal of Korea Multimedia Society
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• v.20 no.6
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• pp.893-910
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• 2017

In this paper, we propose a simple online multiple object (human) tracking method, LKDeep (Lucas-Kanade feature and Detection based Simple Online Multiple Object Tracker), which can run in fast online enough on CPU core only with acceptable tracking performance for embedded surveillance purpose. The proposed LKDeep is a pragmatic hybrid approach which tracks multiple objects (humans) mainly based on LK features but is compensated by detection on periodic times or on necessity times. Compared to other state-of-the-art multiple object tracking methods based on 'Tracking-By-Detection (TBD)' approach, the proposed LKDeep is faster since it does not have to detect object on every frame and it utilizes simple association rule, but it shows a good object tracking performance. Through experiments in comparison with other multiple object tracking (MOT) methods using the public DPM detector among online state-of-the-art MOT methods reported in MOT challenge [1], it is shown that the proposed simple online MOT method, LKDeep runs faster but with good tracking performance for surveillance purpose. It is further observed through single object tracking (SOT) visual tracker benchmark experiment [2] that LKDeep with an optimized deep learning detector can run in online fast with comparable tracking performance to other state-of-the-art SOT methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9A
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• pp.694-701
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• 2003

In this paper, we present a hardware structure and new features of a smart antenna BTS (Base Transceiver Station) for CDMA2000 1X system. The proposed smart antenna BTS is a composite system consisting of many subsystems, i.e., array antenna element, frequency up/down converters, AD (Analog-to-Digital) and DA (Digital-to-Analog) converters, spreading/despreading units, convolutional encoder/Viterbi decoder, searcher, tracker, beamformer, calibration unit etc. Through the experimental tests, we found that the desired beam-pattern in both uplink and downlink communications is provided through the calibration procedure. Also it has been confirmed that the adaptive beamforming algorithm adopted to our smart antenna BTS is fast and accurate enough to support 4 fingers to each user. In our experiments, commercial mobile terminals operating PCS (Personal Communication System) band have been used. It has been confirmed that the smart antenna BTS tremendously improves the FER (Frame Error Rate) performance compared to the conventional 2-antenna diversity system.