Research on basketball players’ action recognition based on interactive system and machine learning

2020 ◽  
pp. 1-11
Author(s):  
Jin Li ◽  
Deping Gu
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Inertial Sensor ◽  
Recognition Algorithm ◽  
Sensor Technology ◽  
Interactive System ◽  
Learning Technology ◽  
Human Body Model ◽  
Basketball Players ◽  
Kalman Filter Algorithm

The difficulty of sports gesture recognition is the effective cooperation of hardware and software. Moreover, there are few studies on machine learning in the capture of the details of sports athletes’ gesture recognition. Therefore, based on the learning technology, this study uses the sensor with gesture recognition algorithm to analyze the detailed motion capture of sports athletes. At the same time, this study selects inertial sensor technology as the gesture recognition hardware through comparative analysis. In addition, by analyzing the actual needs of athletes’ gesture recognition, the Kalman filter algorithm is used to solve the athlete’s posture, construct a virtual human body model, and perform sub-regional processing, so as to facilitate the effective identification of different limbs. Finally, in order to verify the validity of the algorithm model, the basketball exercise is taken as an example for experimental analysis. The research results show that the basketball gesture recognition method used in this paper is quite satisfactory.

scholarly journals Adaptive Recognition of Motion Posture in Sports Video Based on Evolution Equation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Rui Yuan ◽  
Zhendong Zhang ◽  
Yanyan Le ◽  
Enqing Chen
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Inertial Sensor ◽  
Differential Evolution Algorithm ◽  
Recognition Algorithm ◽  
Sensor Technology ◽  
Noise Interference ◽  
Multisensor Data Fusion ◽  
Motion Data ◽  
Artificial Intelligence Technology

In the field of sports, the formulation of existing training plans mainly relies on the manual observation and personal experience of coaches. This method is inevitably subjective. The application of artificial intelligence technology to the training of athletes to recognize athletes’ posture can help coaches assist in decision-making and greatly enhance athletes’ competitive ability. The human body movements embodied in sports are more complicated, and the accurate recognition of sports postures plays an active and important role in sports competitions and training. In this paper, inertial sensor technology is applied to attitude recognition in motion. First, in order to improve the accuracy of attitude calculation and reduce the noise interference in the preparation process, this article uses differential evolution algorithm to apply attitude calculation to realize multisensor data fusion. Secondly, a two-level neural network intelligent motion gesture recognition algorithm is proposed. The two-level neural network intelligent recognition algorithm effectively recognizes similar actions by splitting the traditional single-level neural network into two-level neural networks. Experiments show that the experimental method designed in this article for the posture in motion can obtain the motion information of the examinee in real time, realize the accurate extraction of individual motion data, and complete the recognition of the motion posture. The average accuracy rate can reach 98.85%. There is a certain practical value in gesture recognition.

Evaluation of maker space index system based on machine learning and intelligent interactive system

2020 ◽  
Vol 39 (4) ◽  
pp. 5941-5952
Author(s):  
Yang Chunhe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Index System ◽  
Evaluation System ◽  
Learning System ◽  
Interactive System ◽  
Learning Technology ◽  
Integrated Learning System ◽  
Performance Evaluation System ◽  
Innovation Space

Machine learning technology is the core of artificial intelligence and the basis of computer intelligence. In recent years, machine learning technology has integrated and developed different learning methods, and the research of integrated learning system with more flexible and efficient form is also emerging. In this paper, the authors analyze the maker space index system based on machine learning and intelligent interactive system. As a comprehensive innovation and entrepreneurship platform, mass innovation space has the characteristics of both existing entrepreneurship service system and knowledge innovation driven. Through the index score calculation, the related evaluation system is constructed, the final score of social support system is 61.4.Multi-factor performance evaluation system based on machine learning and artificial intelligence,this paper reveals the development and change law of maker space, and provides theoretical basis for the future operation and decision-making of maker space.

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 Software Implementation of Gesture Recognition Algorithm Using Computer Vision

2021 ◽  
Vol 6 (1) ◽  
pp. 21-26
Author(s):  
Vladyslav Kotyk ◽  
◽  
Oksana Lashko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Gesture Recognition ◽  
Recognition Algorithm ◽  
Software Implementation ◽  
Speech Impairments ◽  
Language Recognition ◽  
Sign Language Recognition

This paper examines the main methods and principles of image formation, display of the sign language recognition algorithm using computer vision to improve communication between people with hearing and speech impairments. This algorithm allows to effectively recognize gestures and display information in the form of labels. A system that includes the main modules for implementing this algorithm has been designed. The modules include the implementation of perception, transformation and image processing, the creation of a neural network using artificial intelligence tools to train a model for predicting input gesture labels. The aim of this work is to create a full-fledged program for implementing a real-time gesture recognition algorithm using computer vision and machine learning.

scholarly journals Application of Machine Learning in Air Hockey Interactive Control System

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7233
Author(s):  
Ching-Lung Chang ◽  
Shuo-Tsung Chen ◽  
Chuan-Yu Chang ◽  
You-Chen Jhou
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Interactive System ◽  
System Response ◽  
Learning Technology ◽  
Chip Design ◽  
Interactive Control ◽  
The Real ◽  
System Response Time ◽  
Linear Slide

In recent years, chip design technology and AI (artificial intelligence) have made significant progress. This forces all of fields to investigate how to increase the competitiveness of products with machine learning technology. In this work, we mainly use deep learning coupled with motor control to realize the real-time interactive system of air hockey, and to verify the feasibility of machine learning in the real-time interactive system. In particular, we use the convolutional neural network YOLO (“you only look once”) to capture the hockey current position. At the same time, the law of reflection and neural networking are applied to predict the end position of the puck Based on the predicted location, the system will control the stepping motor to move the linear slide to realize the real-time interactive air hockey system. Finally, we discuss and verify the accuracy of the prediction of the puck end position and improve the system response time to meet the system requirements.

EFFICIENCY IMPROVEMENT OF ROAD SURFACE INSPECTION USING MACHINE LEARNING TECHNOLOGY

2017 ◽  
Vol 73 (2) ◽  
pp. I_409-I_415
Author(s):  
Naoko FUKUSHI ◽  
Daishiro KOBAYASHI ◽  
Seiji IWAO ◽  
Ryosuke KASAHARA ◽  
Nobuyoshi YABUKI
Keyword(s):  
Machine Learning ◽  
Road Surface ◽  
Surface Inspection ◽  
Learning Technology ◽  
Efficiency Improvement
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