scholarly journals Analysis of Body Behavior Characteristics after Sports Training Based on Convolution Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xinliang Zhou ◽  
Shantian Wen
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Feature Detection ◽  
Performance Test ◽  
High Accuracy ◽  
The Body ◽  
Convolution Neural Network ◽  
Sports Training ◽  
Detection Model ◽  
Behavior Characteristics

The use of artificial intelligence technology to analyze human behavior is one of the key research topics in the world. In order to detect and analyze the characteristics of human body behavior after training, a detection model combined with a convolutional neural network (CNN) is proposed. Firstly, the human skeleton suggestion model is established to analyze the driving mode of the human body in motion. Secondly, the number of layers and neurons in CNN are set according to the skeleton feature map. Then, the output information is classified according to the fatigue degree according to the body state after exercise. Finally, the training and performance test of the model are carried out, and the effect of the body behavior feature detection model in use is analyzed. The results show that the CNN designed in the study shows high accuracy and low loss rate in training and testing and also has high accuracy in the practical application of fatigue degree recognition after human training. According to the subjective evaluation of volunteers, the overall average evaluation is more than 9 points. The above results show that the designed convolution neural network-based detection model of body behavior characteristics after training has good performance and is feasible and practical, which has guiding significance for the design of sports training and training schemes.

scholarly journals Alcoholic brain injury via 8-layer customized deep convolution neural network

STEMedicine ◽  
2021 ◽  
Vol 2 (8) ◽  
pp. e97
Author(s):  
Ziquan Zhu ◽  
Mackenzie Brown
Keyword(s):  
Neural Network ◽  
Brain Injury ◽  
Diagnostic Errors ◽  
The Body ◽  
Convolution Neural Network ◽  
Comparison Results ◽  
Deep Convolution Neural Network ◽  
Sensitivity Specificity ◽  
And Behavior ◽  
Fully Connected

Alcohol can act quickly in the human body and alter mood and behavior. If alcohol is consumed in excess, it will accumulate in the organs of the body, especially in the liver and brain. To a certain extent, the symptoms of alcoholism will appear. So far, the main method of diagnosis of alcoholic brain injury is through MRI images by radiologists. However, this is a very subjective diagnosis. Radiologists may be affected by external factors, such as physical discomfort, lack of rest, inattention, etc., resulting in diagnostic errors. In this paper, we proposed a novel 8-layer customized deep convolution neural network for alcoholic brain injury detection, which contains five convolution layers, five pooling layers, and three fully connected layers. There are three improvements in this paper, (i) Based on deep learning, we proposed a method for automatic diagnosis of alcoholic brain injury; (ii) We introduced Dropout to the proposed structure to improve robustness; (iii) Compared with other state-of-the-art approaches, the proposed structure is more efficient. The experimental results showed that the sensitivity, specificity, precision, accuracy, F1, MCC and FMI were 96.14±1.99, 96.20±1.47, 95.98±1.54, 96.17±1.55, 96.05±1.62, 93.34±3.11, 96.06±1.62 respectively. According to comparison results, our method performed the best. The proposed model is effective in detecting alcoholic brain injury based on MRI images.

scholarly journals Detection of Neurons in the Proteomic and Genomic Image Data

2019 ◽  
Vol 8 (4) ◽  
pp. 11151-11157
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Sensory System ◽  
Image Data ◽  
Input Image ◽  
The Body ◽  
Basic Unit ◽  
Biomedical Data ◽  
Information Flows ◽  
Network Method

Nowadays, the major biomedical data required for diagnosing the disease is neurons in the nerve cell. Just a brief timeframe after the neuron became recognized as the basic unit of the sensory system, the main endeavors were made to appraise the quantity of neurons in various parts of the sensory system. During the previous century, an incredible number of techniques have been utilized in making such gauges. In spite of the fact that the most generally utilized and acknowledged strategy is that of direct including in the magnifying lens, different systems, including photographic, projection, homogenate, programmed, and visual strategies have been planned. And in this project we are taking a brain tissue as an image data and from that image we are finding the number of neurons which are active in state for the first 24 hrs. and again check for 48 hrs. and finally for 72 hrs. so we here find how neurons are responding after giving information to a body and that information flows through nerves of the body and reaches to the neurons present in a human brain and the neurons react to the information and we take the data that how many neurons are responding to the information that is given to a human body. So, by finding the number of neurons responding to the information given to human body we could estimate the neurons which are alive, and which are dead by this we could declare the mental status of a person. So we are finding the number of neurons with the help of neural network method using MATLAB software and we created a page with the help of MATLAB so we can give input image in the page and the code we written will help to check the number of neurons.

scholarly journals Low-Power FPGA Implementation of Convolution Neural Network Accelerator for Pulse Waveform Classification

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 213
Author(s):  
Chuanglu Chen ◽  
Zhiqiang Li ◽  
Yitao Zhang ◽  
Shaolong Zhang ◽  
Jiena Hou ◽  
...  
Keyword(s):  
Neural Network ◽  
Low Power ◽  
Optimization Methods ◽  
High Accuracy ◽  
Convolution Neural Network ◽  
Pulse Waveform ◽  
Power Devices ◽  
Excellent Performance ◽  
Field Programmable ◽  
Working Frequency

In pulse waveform classification, the convolution neural network (CNN) shows excellent performance. However, due to its numerous parameters and intensive computation, it is challenging to deploy a CNN model to low-power devices. To solve this problem, we implement a CNN accelerator based on a field-programmable gate array (FPGA), which can accurately and quickly infer the waveform category. By designing the structure of CNN, we significantly reduce its parameters on the premise of high accuracy. Then the CNN is realized on FPGA and optimized by a variety of memory access optimization methods. Experimental results show that our customized CNN has high accuracy and fewer parameters, and the accelerator costs only 0.714 W under a working frequency of 100 MHz, which proves that our proposed solution is feasible. Furthermore, the accelerator classifies the pulse waveform in real time, which could help doctors make the diagnosis.

scholarly journals Facial Spots Detection Using Convolution Neural Network

2020 ◽  
pp. 71-83
Author(s):  
Payal Bose ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recognition System ◽  
The Body ◽  
Convolution Neural Network ◽  
Biometric Authentication ◽  
Human Face ◽  
Security Issue ◽  
Global Issue ◽  
The Face

Nowadays security became a major global issue. To manage the security issue and its risk, different kinds of biometric authentication are available. Face recognition is one of the most significant processes in this system. Since the face is the most important part of the body so the face recognition system is the most important in the biometric authentication. Sometimes a human face affected due to different kinds of skin problems, such as mole, scars, freckles, etc. Sometimes some parts of the face are missing due to some injuries. In this paper, the main aim is to detect a facial spots present in the face. The total work divided into three parts first, face and facial components are detected. The validation of checking facial parts is detected using the Convolution Neural Network (CNN). The second part is to find out the spot on the face based on Normalized Cross-Correlation and the third part is to check the kind of spot based on CNN. This process can detect a face under different lighting conditions very efficiently. In cosmetology, this work helps to detect the spots on the human face and its type which is very helpful in different surgical processes on the face.

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