• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.119-130
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• 2017

The purpose of the study is discovering the effects of virtual competitors on user in AR (Augment Reality) home training system. Specifically, the current research examined their effects on immersion, perceived competition, and leaning motivation. The paper tested three unexplored relationship. First, introducing virtual competitors in home training system will enhance user's immersion. Second, presenting virtual competitors in home training system will increase user's perceived competition. Third, virtual competitors in home training system will raise user's learning motivation. For empirical analysis, we developed home training system, which could check and give feedback automatically, based on user's posture. Using this AR home training system, the study empirically shows how and why virtual competitors affect users. The results give implications not only on service design; but also on the idea that virtual other could affect user's behavior.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.470-473
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• 2021

본 논문에서는 코로나-19로 인해 홈 트레이닝 시장이 성장하고 있는 상황 속에서 효율적인 운동을 위해 사용자의 식습관, 신체조건, 선호도 등을 바탕으로 적합한 운동을 추천해주는 시스템을 제안한다. 먼저 K-최근접 이웃 알고리즘을 활용해 비만의 정도에 따라 사용자를 분류하고, 운동 데이터를 소모 칼로리에 따라 클러스터링 한다. 다음으로 비만의 정도와 운동 레벨에 따라 정해진 추천 점수를 통해 사전 선호도 확률을 계산하고, 베이지안 네트워크를 통해 사후 확률을 구한다. 이를 바탕으로 특이값 분해 알고리즘(SVD)를 활용하여 사용자 맞춤형 운동을 추천한다. 제안 시스템의 성능을 검증하기 위해 비교 실험을 진행하여 회귀 문제 평가 척도인 RMSE 값 측면에서 성능을 분석하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.269-271
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• 2019

우리에겐 익숙하고 쉽게 느껴지지만, 누군가에겐 어려운 것들이 존재한다. 예로 시각 장애인에게는 거리를 돌아다니는 것, 거리를 달린다는 것은 상당한 용기가 필요한 일이다. 경북대학교 재학 중인 시각 장애인 김경훈 학우는 자신의 유튜브 채널에 등교하는 거리에 익숙해지도록 여러 번 걸어 다니며 연습하는 모습의 영상을 올린 적이 있다. 또 다른 시각장애인은 국민 청원에 트레이닝에 대한 지원 요청을 한 적이 있다. 우리는 이런 사례들을 조사하여 거리에 나가지 않아도, 위험요소를 생각하지 않아도 할 수 있는, 그 누구도 차별 받지 않고 모두가 자연스럽게 운동에 집중할 수 있는 홈 트레이닝 시스템을 구현하였다.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.233-239
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• 2021

Recently, the COVID-19 virus has caused people to stay indoors longer without going out. As a result of this, people's activity decreased sharply, and their weight gained. So people became more interested in health. Home training can be an alternative method to solve this problem. Accordingly, To find out the trends of home training, we collected articles from December 1, 2019, to November 30, 2020, using the news provided by BIG KINDS, a news analysis system. We analyzed frequency analysis, relational analysis according to weighting, and related word analysis with the program using the algorithm developed by BIG KINDS. In conclusion, first, it was found that home training is led by technology and the emergence of artificial intelligence. Second, it can be assumed that people mainly do home training using content and video services related to mobile carriers. Third, people had a high preference for Pilates in the sports category. It can be seen that the number of patent applications increased as the demand for exercise products related to Pilates increased. In the next study, we expect that this study will be used as primary data for various big data studies by supplementing the research methodology and conducting various analyses.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1183-1189
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• 2021

There have been recently an increasing number of people working out at home. However, many of them do not have face-to-face guidance from experts, so they cannot effectively correct their wrong pose. This may lead to strain and injury to those doing home training. To tackle this problem, this paper proposes a video data-based pose classification and correction system for home training. The proposed system classifies poses using the multi-layer perceptron and pose estimation model, and corrects poses based on joint angels estimated. A voting algorithm that considers the results of successive frames is applied to improve the performance of the pose classification model. Multi-layer perceptron model for post classification shows the highest accuracy with 0.9. In addition, it is shown that the proposed voting algorithm improves the accuracy to 0.93.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2095-2102
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• 2017

In this paper, we implemented the elderly home training contents provide individual exercise prescription according to the user's athletic ability and provide personalized program to the elderly individual. Health promotion is essential for overcoming the low health longevity of senior citizens preparing for aging population. Therefore, the lower body strengthening exercise to prevent falls is crucial to prevent a fall in the number of deaths of senior citizens. In this game model, the elderly are aiming at home training contents that can be found to feel that the elderly are going out of walk and exercising in the natural environment. To achieve this, Kinect extracts a specific bone model provide by the Kinect Sensor to generate the feature vectors and recognizes the movements and motion of the user. The recognition test using the Kinect sensor showed a recognition rate of about 80 to 97%.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.19-28
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• 2021

Nowadays, interest in health care is increasing due to Coronavirus (COVID-19), and a lot of people are doing home training as there are more difficulties in using fitness centers and public facilities that are used together. In this paper, we propose a personalized exercise recommendation algorithm using personalized propensity information to provide more accurate and meaningful exercise recommendation to home training users. Thus, we classify the data according to the criteria for obesity with a k-nearest neighbor algorithm using personal information that can represent individuals, such as eating habits information and physical conditions. Furthermore, we differentiate the exercise dataset by the level of exercise activities. Based on the neighborhood information of each dataset, we provide personalized exercise recommendations to users through a dimensionality reduction algorithm (SVD) among model-based collaborative filtering methods. Therefore, we can solve the problem of data sparsity and scalability of memory-based collaborative filtering recommendation techniques and we verify the accuracy and performance of the proposed algorithms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.141-142
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• 2019

본 논문에서는 현대사회에서 스마트 폰 보급의 활성화와 좋지 않은 자세로 오래 앉아 있는 학생들이 자가적으로 척추 측만증을 진단하여 스스로 인지하게 하는 것이 목표이다. 척추측만증은 통증이 나타나지 않아 질병으로인해 병원에서 진료받지 않으면 자가적으로 판단하기 어려울 뿐만 아니라 방치하면 추간판 탈출증 등 여러 척추 질환에 노출 되기 쉽다. 질환을 예방 하기 위해 자이로 센서와 균형센서를 이용하여 집이나 재활트레이닝 센터 및 학교 등에서 자가적으로 판단을 할 수 있다. 센서를 통한 자가적인 진단 정보는 휴대폰의 어플리케이션으로 전달되어 척추 측만증의 정도를 알 수 있고 그에 따른 운동방법이나 자세 교정 방법등을 알려주고 운동 프로그램을 설계하는 시스템을 개발하는데 목적이 있는 연구이다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.177-183
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• 2022

Recently, interesting of home training is getting bigger due to COVID-19. Accordingly, research on applying HAR(human activity recognition) technology to home training has been conducted. However, existing paper of HAR proposed static activity instead of dynamic activity. In this paper, the deep learning model where dynamic exercise posture can be analyzed and the accuracy of the user's exercise posture can be shown is proposed. Fitness images of AI-hub are analyzed by blaze pose. The experiment is compared with three types of deep learning model: RNN(recurrent neural network), LSTM(long short-term memory), CNN(convolution neural network). In simulation results, it was shown that the f1-score of RNN, LSTM and CNN is 0.49, 0.87 and 0.98, respectively. It was confirmed that CNN is more suitable for human activity recognition than other models from simulation results. More exercise postures can be analyzed using a variety learning data.