Binocular Human Body Attitude Distance Localization Recognition Algorithm Based on Dual Convolution

2020 ◽  
Author(s):  
Jianming Sun ◽  
Wenbo Wang ◽  
Zidong Chen
Keyword(s):  
Human Body ◽  
Recognition Algorithm ◽  
Body Attitude

Hand Gesture Recognition Algorithm: A Real-Time Human-Body-Based Approach

2013 ◽  
Vol 303-306 ◽  
pp. 1338-1343
Author(s):  
Xin Xiong Li ◽  
Yi Xiong ◽  
Zhi Yong Pang ◽  
Di Hu Chen
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Human Body ◽  
Skin Color ◽  
Average Rate ◽  
Vital Role ◽  
Recognition Algorithm ◽  
Hand Gesture Recognition ◽  
High Tech ◽  
Hand Gesture

Despite the appearance of high-tech human computer interface (HCI) devices, pattern recognition and gesture recognition with single camera are still playing vital role in research. A real-time human-body based algorithm for hand gesture recognition is proposed in this paper. The basis of our approach is a combination of moving object segmentation process and skin color detector based on human body structure to obtain the moving hands from input images, which is able to deal with the problem of complex background and random noises, and a rotate correction process for better finger detection. With ten fingers detected, more than 1000 gestures can be recognized before concerning motion paths. This paper includes experimental results of five gestures, which can be extended to other conditions. Experiments show that the algorithm can achieve a 99 percent recognition average rate and is suitable for real-time applications.

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.

A Novel Human Pose Detection from Videos Algorithm Based on Motion Capture Data

2010 ◽  
Vol 20-23 ◽  
pp. 833-837
Author(s):  
Ou Yang Yi
Keyword(s):  
Human Body ◽  
Feature Matching ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Pose Recognition ◽  
Pose Detection ◽  
Spatio Temporal ◽  
Human Pose ◽  
Static Background ◽  
Change Monitoring

This video image of static background frame and deduction, the pixel, pixels for sports change monitoring and static pixels. By combining the feature of deformation of human body positioning movement of template, the human body pose detection algorithm put in spatio-temporal detection to human pose recognition using feature matching, accelerate matching speed probability. This method in the testing result is superior to other pose recognition algorithm, and also has the ability to quickly identify.

scholarly journals Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhesen Chu ◽  
Min Li
Keyword(s):  
Time Series ◽  
Human Body ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Human Body Model ◽  
Motion Data ◽  
Body Model ◽  
Lstm Network

This article analyzes the method of reading data from inertial sensors. We introduce how to create a 3D scene and a 3D human body model and use inertial sensors to drive the 3D human body model. We capture the movement of the lower limbs of the human body when a small number of inertial sensor nodes are used. This paper introduces the idea of residual error into the deep LSTM network to solve the problem of gradient disappearance and gradient explosion. The main problem to be solved by wearable inertial sensor continuous human motion recognition is the modeling of time series. This paper chooses the LSTM network which can handle time series as well as the main frame. In order to reduce the gradient disappearance and gradient explosion problems in the deep LSTM network, the structure of the deep LSTM network is adjusted based on the residual learning idea. In this paper, a data acquisition method using a single inertial sensor fixed on the bottom of a badminton racket is proposed, and a window segmentation method based on the combination of sliding window and action window in real-time motion data stream is proposed. We performed feature extraction on the intercepted motion data and performed dimensionality reduction. An improved Deep Residual LSTM model is designed to identify six common swing movements. The first-level recognition algorithm uses the C4.5 decision tree algorithm to recognize the athlete’s gripping style, and the second-level recognition algorithm uses the random forest algorithm to recognize the swing movement. Simulation experiments confirmed that the proposed improved Deep Residual LSTM algorithm has an accuracy of over 90.0% for the recognition of six common swing movements.

scholarly journals The Role of Computer Remote Monitoring Technology for Nursing Care in Elderly Breast Cancer Complications

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ling Wang ◽  
Huang Yan ◽  
Jing Yan ◽  
Liyuan Qian
Keyword(s):  
Breast Cancer ◽  
Pattern Recognition ◽  
Human Body ◽  
Medical Information ◽  
Fall Detection ◽  
The Elderly ◽  
Recognition Algorithm ◽  
Gastrointestinal Function ◽  
Medical Monitoring ◽  
Pattern Recognition Algorithm

Geriatric patients undergoing mastectomy have a weakened organism and slow recovery of gastrointestinal function after surgery, which may lead to various complications, affect the absorption of intestinal nutrients, and prolong the healing rate of wounds. Therefore, it is necessary to find an effective nursing program to promote the recovery of gastrointestinal function and prevent postoperative complications in elderly patients undergoing mastectomy. With the continuous development and advancement of computer and communication technologies, telecare is gaining more and more attention and has become an important part of medical information technology construction. Falls endanger the elderly and other special populations, especially after a sudden but unassisted fall, which may be life-threatening. Timely fall detection and rescue can win valuable time for treatment and rescue, which is very important to protect users’ health and improve medical monitoring. In order to provide better medical care to the elderly population and reduce the harm caused by falls, this paper will focus on the fall problem of the elderly in telecare. In order to facilitate the detection of falls of the elderly, we design an Android sensor-based data acquisition scheme, using the built-in acceleration sensor in the Android system to collect the human acceleration information, and through the JMS middleware technology, the collected data are transmitted to MATLAB for analysis and processing in real time. This paper preprocesses and synthesizes the collected human body data and visualizes the acceleration changes of various typical daily activities of the human body and breast cancer, then extracts the relevant data features according to the synthesized SVM curve, constructs a pattern recognition algorithm using the extracted features, and verifies the effectiveness of the pattern recognition algorithm through experiments.

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