• Title/Summary/Keyword: Exercise Pose Estimation

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Design and Development of the Multiple Kinect Sensor-based Exercise Pose Estimation System (다중 키넥트 센서 기반의 운동 자세 추정 시스템 설계 및 구현)

  • Cho, Yongjoo;Park, Kyoung Shin
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.3
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    • pp.558-567
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    • 2017
  • In this research, we developed an efficient real-time human exercise pose estimation system using multiple Kinects. The main objective of this system is to measure and recognize the user's posture (such as knee curl or lunge) more accurately by employing Kinects on the front and the sides. Especially it is designed as an extensible and modular method which enables to support various additional postures in the future. This system is configured as multiple clients and the Unity3D server. The client processes Kinect skeleton data and send to the server. The server performs the multiple-Kinect calibration process and then applies the pose estimation algorithm based on the Kinect-based posture recognition model using feature extractions and the weighted averaging of feature values for different Kinects. This paper presents the design and implementation of the human exercise pose estimation system using multiple Kinects and also describes how to build and execute an interactive Unity3D exergame.

Behavior Pattern Prediction Algorithm Based on 2D Pose Estimation and LSTM from Videos (비디오 영상에서 2차원 자세 추정과 LSTM 기반의 행동 패턴 예측 알고리즘)

  • Choi, Jiho;Hwang, Gyutae;Lee, Sang Jun
    • IEMEK Journal of Embedded Systems and Applications
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    • v.17 no.4
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    • pp.191-197
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    • 2022
  • This study proposes an image-based Pose Intention Network (PIN) algorithm for rehabilitation via patients' intentions. The purpose of the PIN algorithm is for enabling an active rehabilitation exercise, which is implemented by estimating the patient's motion and classifying the intention. Existing rehabilitation involves the inconvenience of attaching a sensor directly to the patient's skin. In addition, the rehabilitation device moves the patient, which is a passive rehabilitation method. Our algorithm consists of two steps. First, we estimate the user's joint position through the OpenPose algorithm, which is efficient in estimating 2D human pose in an image. Second, an intention classifier is constructed for classifying the motions into three categories, and a sequence of images including joint information is used as input. The intention network also learns correlations between joints and changes in joints over a short period of time, which can be easily used to determine the intention of the motion. To implement the proposed algorithm and conduct real-world experiments, we collected our own dataset, which is composed of videos of three classes. The network is trained using short segment clips of the video. Experimental results demonstrate that the proposed algorithm is effective for classifying intentions based on a short video clip.

Design of Personalized Exercise Data Collection System based on Edge Computing

  • Jung, Hyon-Chel;Choi, Duk-Kyu;Park, Myeong-Chul
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.5
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    • pp.61-68
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    • 2021
  • In this paper, we propose an edge computing-based exercise data collection device that can be provided for exercise rehabilitation services. In the existing cloud computing method, when the number of users increases, the throughput of the data center increases, causing a lot of delay. In this paper, we design and implement a device that measures and estimates the position of keypoints of body joints for movement information collected by a 3D camera from the user's side using edge computing and transmits them to the server. This can build a seamless information collection environment without load on the cloud system. The results of this study can be utilized in a personalized rehabilitation exercise coaching system through IoT and edge computing technologies for various users who want exercise rehabilitation.

Non-face-to-face online home training application study using deep learning-based image processing technique and standard exercise program (딥러닝 기반 영상처리 기법 및 표준 운동 프로그램을 활용한 비대면 온라인 홈트레이닝 어플리케이션 연구)

  • Shin, Youn-ji;Lee, Hyun-ju;Kim, Jun-hee;Kwon, Da-young;Lee, Seon-ae;Choo, Yun-jin;Park, Ji-hye;Jung, Ja-hyun;Lee, Hyoung-suk;Kim, Joon-ho
    • 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