Non-Intrusive Human Motion Recognition Using Distributed Sparse Sensors and the Genetic Algorithm Based Neural Network

2018 ◽  
Author(s):  
Farhad Pourpanah ◽  
Bin Zhang ◽  
Rui Ma ◽  
Qi Hao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Human Motion ◽  
Motion Recognition ◽  
Human Motion Recognition

scholarly journals Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yang Ju
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
Recognition Rate ◽  
Linear Representation ◽  
Random Projection ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Motion Recognition ◽  
Human Motion Recognition

Aiming at the problem that it is difficult to balance the speed and accuracy of human behaviour recognition, this paper proposes a method of motion recognition based on random projection. Firstly, the optical flow picture and Red, Green, Blue (RGB) picture obtained by the Lucas-Kanade algorithm are used. Secondly, the data of optical flow pictures and RGB pictures are compressed based on a random projection matrix of compressed sensing, which effectively reduces power consumption. At the same time, based on random projection compression data, it can effectively find the optimal linear representation to reconstruct training samples and test samples. Thirdly, a multichannel 3D convolutional neural network is proposed, and the multiple information extracted by the network is fused to form an output recognizer. Experimental results show that the algorithm in this paper significantly improves the recognition rate of multicategory actions and effectively reduces the computational complexity and running time of the recognition algorithm.

scholarly journals Human motion recognition based on SVM in VR art media interaction environment

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Fuquan Zhang ◽  
Tsu-Yang Wu ◽  
Jeng-Shyang Pan ◽  
Gangyi Ding ◽  
Zuoyong Li
Keyword(s):  
Genetic Algorithm ◽  
Dimensional Space ◽  
Dimensional Subspace ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Support Vector ◽  
Motion Recognition ◽  
Linear Discriminant ◽  
Classification Feature ◽  
Human Motion Recognition

AbstractIn order to solve the problem of human motion recognition in multimedia interaction scenarios in virtual reality environment, a motion classification and recognition algorithm based on linear decision and support vector machine (SVM) is proposed. Firstly, the kernel function is introduced into the linear discriminant analysis for nonlinear projection to map the training samples into a high-dimensional subspace to obtain the best classification feature vector, which effectively solves the nonlinear problem and expands the sample difference. The genetic algorithm is used to realize the parameter search optimization of SVM, which makes full use of the advantages of genetic algorithm in multi-dimensional space optimization. The test results show that compared with other classification recognition algorithms, the proposed method has a good classification effect on multiple performance indicators of human motion recognition and has higher recognition accuracy and better robustness.

scholarly journals Optimization of Human Motion Recognition Information Processing System Based on GA-BP Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuwei Zhao
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Bp Neural Network ◽  
Processing System ◽  
Human Motion ◽  
Motion Recognition ◽  
Information Processing System ◽  
Network Algorithm ◽  
Human Motion Recognition ◽  
Neural Network Algorithm

At present, there are some problems in the process of human motion recognition, such as poor timeliness and low fault tolerance rate. How to effectively identify the motion process accurately has become a hot spot in the optimization system. In the existing research studies, the recognition accuracy is not very good and the response time is long. To end this issue, the paper proposed an information processing system and optimization method of human motion recognition based on the GA-BP neural network algorithm. Firstly, a human motion recognition system based on dynamic capture recognition technology is designed, which realizes the recognition of motion information from common postures such as action span, speed change, motion trajectory, and other aspects in the process of human motion. Secondly, the proposed algorithm is used to comprehensively analyse and evaluate the motion state. Finally, experiments are designed to verify and analyse the results. Compared to some baseline methods in human motion recognition information systems, the system in this paper based on the GA-BP neural network algorithm has the advantages of higher data accuracy and response speed, which can quickly and accurately identify the muscle group change in the process of human motion, and it can also provide customized motion suggestions based on the results.

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