Intelligent Skiing Posture Detection and Recognition through Internet of Bodies

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
High Speed ◽  
Body Posture ◽  
Classification Model ◽  
Analytical Results ◽  
Posture Recognition ◽  
Detection And Recognition

The training of special ability of skiing should start from the control of body posture ability to highlight the characteristics of the sports. Thus, the athletes can have the sports ability in the process of high-speed skiing. This paper establishes a system to automatically recognize the skiing posture which can help athletes grasp the skiing postures. First, the skiing images are collected by distributed camera. Second, the skeleton features are extracted to learn a classification model which is used to recognize and adjust skiing postures. Lastly, the analytical results of posture recognition is returned to athletes through Internet of bodies. The framework can effectively recognize the skiing postures and provide athletes with training advices.

Design and implementation of a system for body posture recognition

2012 ◽  
Vol 70 (3) ◽  
pp. 1637-1650 ◽  
Author(s):  
Ali Asghar Nazari Shirehjini ◽  
Abdulsalam Yassine ◽  
Shervin Shirmohammadi
Keyword(s):  
Body Posture ◽  
Design And Implementation ◽  
Posture Recognition

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.

scholarly journals Development of an integrated game theory-optimization subground stratification model using cone penetration test (CPT) measurements

2021 ◽  
Author(s):  
Mohammad Sadegh Farhadi ◽  
Tim Länsivaara
Keyword(s):  
Game Theory ◽  
High Speed ◽  
Cone Penetration Test ◽  
Training Data ◽  
Classification Model ◽  
Grey Wolf Optimizer ◽  
Penetration Test ◽  
Cone Penetration ◽  
Transition Layers ◽  
Proposed Model

AbstractThe continuous cone penetration test (CPT) measurements provide an advantageous liable rapid tool for stratification and soil behavior classification that can be employed in the sustainable design of the infrastructures. However, the CPT measurements are often interpreted by geotechnical experts because of the involved complexities and uncertainties. In this study, a novel stratification and soil type behavior (SBT) classification model is developed to identify the transition and thicker layers by integrating the geotechnical knowledge with the three submodels of (a) locally estimated scatterplot smoothing (LOESS), (b) a game theory model known as Nash–Harsanyi (N–H) bargaining, and (c) grey wolf optimizer (GWO). The LOESS and integrated N–H bargaining-GWO models are, respectively, used to approximate the outliers in CPT measurements and identify the SBT and layer changes. Attractively, in the proposed model, the engineer has the opportunity to judge on the precision of the stratification profile regarding their own preferences in a project. Solving simple algebraic equations, high speed, identifying thick and the interlayer transition layers, and small required training data are the other advantages of the developed model. Finally, the applicability of the proposed model has been assessed in an example. The compared estimated and two other models’ stratification profiles highlighted the potential of the proposed model to identify thin transition layers.

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