scholarly journals Role of Human Body Posture Recognition Method Based on Wireless Network Kinect in Line Dance Aerobics and Gymnastics Training

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanhong Zhou
Keyword(s):  
Wireless Network ◽  
Human Body ◽  
Rapid Development ◽  
Human Motion ◽  
Body Posture ◽  
Human Bone ◽  
Recognition Method ◽  
Joint Point ◽  
Posture Recognition ◽  
Human Joint

With the rapid development of the information society, human body gesture recognition has become an important technology for human-computer interaction. This paper combines Kinect’s human bone monitoring technology with auxiliary gymnastics training. The gymnastics and dance training can correct students’ wrong movements in time through feedback and improve the training efficiency, so as to meet the needs of nature and harmony of human-machine interaction. In this paper, based on the wireless network Kinect, the human body posture recognition method and tracking technology are studied, and the joint point angle representation method based on the fixed axis is proposed, and the posture recognition method based on the joint point angle is improved, which can accurately recognize the human body posture. Aiming at the situation that the human joint points are occluded, the human joint point repair algorithm is improved. The algorithm is based on the proportion of human bone nodes and the characteristics of human motion, and based on geometric principles, it repairs the occluded points. The feasibility of the original joint point data, angle feature, and distance feature in expressing human behavior is analyzed through experiments, a standard gymnastics movement database is established, and new gymnastics movements can be entered at any time. A gymnastics auxiliary training system is designed, which can analyze and evaluate the exercises of the trainer from the joint point coordinates and the angle formed by the joints and provide the trainer with intuitive error correction prompts. The human body posture recognition method studied in this paper can accurately give the difference between the trainer’s movement and the standard movement, and the trainer can adjust the movement posture according to the prompts, improve the level of gymnastics, and achieve the purpose of auxiliary training. Experiments show that the algorithm model has an accuracy rate of 95.7% for human body posture recognition, and it plays a huge role in line dance aerobics and gymnastics training.

scholarly journals A Posture Recognition Method Based on Indoor Positioning Technology

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1464 ◽  
Author(s):  
Xiaoping Huang ◽  
Fei Wang ◽  
Jian Zhang ◽  
Zelin Hu ◽  
Jian Jin
Keyword(s):  
Kalman Filtering ◽  
Human Body ◽  
Recognition Rate ◽  
Indoor Positioning ◽  
Body Posture ◽  
Least Square ◽  
Error Performance ◽  
Extended Kalman Filtering ◽  
Key Points ◽  
Posture Recognition

Posture recognition has been widely applied in fields such as physical training, environmental awareness, human-computer-interaction, surveillance system and elderly health care. The traditional methods consist of two main variations: machine vision methods and acceleration sensor methods. The former has the disadvantages of privacy invasion, high cost and complex implementation processes, while the latter has low recognition rate for still postures. A new body posture recognition scheme based on indoor positioning technology is presented in this paper. A single deployed indoor positioning system is constructed by installing wearable receiving tags at key points of the human body. The distance measurement method with ultra-wide band (UWB) radio is applied to position the key points of human body. Posture recognition is implemented by positioning. In the posture recognition algorithm, least square estimation (LSE) method and the improved extended Kalman filtering (iEKF) algorithm are respectively adopted to suppress the noise of the distances measurement and to improve the accuracy of positioning and recognition. The comparison of simulation results with the two methods shows that the improved extended Kalman filtering algorithm is more effective in error performance.

Classification accuracy of basketball simulation training system based on sensor fusion and Bayesian algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5965-5976
Author(s):  
Wei Zhu
Keyword(s):  
Decision Making ◽  
Human Body ◽  
Behavior Pattern ◽  
Recognition Algorithm ◽  
Training System ◽  
Body Posture ◽  
Interactive Control ◽  
Advantages And Disadvantages ◽  
Whole Process ◽  
Posture Recognition

As a pattern recognition application direction, human body posture recognition provides decision-making basis for human body behavior pattern analysis of human-computer intelligent interactive control. Therefore, in a complete human-computer intelligent interaction system, human body posture recognition is a necessary link that can complete the human body’s behavioral characterization and make humanized decision-making. This paper studies the athlete’s posture recognition algorithm based on multi-sensor method and completes the whole process from data acquisition to data processing and model algorithm construction and verification. Moreover, this paper designs experiments to verify the model’s recognition results for athletes, and discusses the results, and analyzes the advantages and disadvantages of the model in this experiment. In addition, this study takes basketball action as an example to take identification analysis. The results show that the proposed method has certain practical effects and can provide theoretical reference for subsequent related research.

Human body posture recognition using MPEG-7 descriptors

2004 ◽  
Author(s):  
Lutz Goldmann ◽  
Mustafa Karaman ◽  
Thomas Sikora
Keyword(s):  
Human Body ◽  
Body Posture ◽  
Posture Recognition

scholarly journals Intelligent Sports Training System Based on Artificial Intelligence and Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chunguang Li ◽  
Jianbiao Cui
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Human Body ◽  
Joint Angle ◽  
Rapid Development ◽  
Human Motion ◽  
Training System ◽  
Human Motion Analysis ◽  
Sports Training ◽  
Organizational System

All activities in training fields are for the improvement of athletes’ competitive abilities. A sports training system is an organizational system to achieve common goals. Competitive ability is one of the main manifestations of the evolution of the training system. With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports-assisted training to eliminate traditional sports training that relied purely on experience. Pose estimation obtains the position, angle, and additional information about the human body in the image in a two-dimensional plane or three-dimensional space by establishing the mapping relationship between the human body features and the human body posture. This article demonstrates a golf-assisted training system to realize the transformation from an experience-based sports training method to a human motion analysis method, using artificial intelligence and big data. The swing posture parameters of the trainer and the coach are obtained using the posture estimation of a human body. Based on this information, an auxiliary training system is built. The two parameters of the joint angle trajectory and the posture similarity are used as auxiliary indicators to compare the trainers. The joint angle trajectory is analyzed, and the coach is guided based on the similarity of the posture.

scholarly journals A REAL-TIME PHOTOGRAMMETRIC SYSTEM FOR MONITORING HUMAN MOVEMENT DYNAMICS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 561-566
Author(s):  
L. Chen ◽  
B. Wu ◽  
Y. Zhao
Keyword(s):  
Real Time ◽  
Human Body ◽  
Large Scale ◽  
Human Movement ◽  
Human Motion ◽  
Body Posture ◽  
Efficient Estimation ◽  
Process Unit ◽  
Movement Dynamics ◽  
Body Dynamics

Abstract. The human body posture is rich with dynamic information that can be captured by algorithms, and many applications rely on this type of data (e.g., action recognition, people re-identification, human-computer interaction, industrial robotics). The recent development of smart cameras and affordable red-green-blue-depth (RGB-D) sensors has enabled cost-efficient estimation and tracking of human body posture. However, the reliability of single sensors is often insufficient due to occlusion problems, field-of-view limitations, and the limited measurement distances of the RGB-depth sensors. Furthermore, a large-scale real-time response is often required in certain applications, such as physical rehabilitation, where human actions must be detected and monitored over time, or in industries where human motion is monitored to maintain predictable movement flow in a shared workspace. Large-scale markerless motion-capture systems have therefore received extensive research attention in recent years.In this paper, we propose a real-time photogrammetric system that incorporates multithreading and a graphic process unit (GPU)-accelerated solution for extracting 3D human body dynamics in real-time. The system includes a stereo camera with preliminary calibration, from which left-view and right-view frames are loaded. Then, a dense image-matching algorithm is married with GPU acceleration to generate a real-time disparity map, which is further extended to a 3D map array obtained by photogrammetric processing based on the camera orientation parameters. The 3D body features are acquired from 2D body skeletons extracted from regional multi-person pose estimation (RMPE) and the corresponding 3D coordinates of each joint in the 3D map array. These 3D body features are then extracted and visualised in real-time by multithreading, from which human movement dynamics (e.g., moving speed, knee pressure angle) are derived. The results reveal that the process rate (pose frame-rate) can be 20 fps (frames per second) or above in our experiments (using two NVIDIA 2080Ti and two 12-core CPUs) depending on the GPU exploited by the detector, and the monitoring distance can reach 15 m with a geometric accuracy better than 1% of the distance.This real-time photogrammetric system is an effective real-time solution to monitor 3D human body dynamics. It uses low-cost RGB stereo cameras controlled by consumer GPU-enabled computers, and no other specialised hardware is required. This system has great potential for applications such as motion tracking, 3D body information extraction and human dynamics monitoring.

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