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
Author(s):  
Felix - Constantin Adochiei ◽  
Ioana Raluca Adochiei ◽  
Radu Ciucu ◽  
Gladiola Pietroiu-Andruseac ◽  
Florin Ciprian Argatu ◽  
...  

2020 ◽  
Vol E103.D (5) ◽  
pp. 1067-1077
Author(s):  
Teruhiro MIZUMOTO ◽  
Yasuhiro OTODA ◽  
Chihiro NAKAJIMA ◽  
Mitsuhiro KOHANA ◽  
Motohiro UENISHI ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1464 ◽  
Author(s):  
Xiaoping Huang ◽  
Fei Wang ◽  
Jian Zhang ◽  
Zelin Hu ◽  
Jian Jin

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.


2020 ◽  
Vol 39 (4) ◽  
pp. 5965-5976
Author(s):  
Wei Zhu

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.


2004 ◽  
Author(s):  
Lutz Goldmann ◽  
Mustafa Karaman ◽  
Thomas Sikora

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