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.

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 Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology

Micromachines ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 411 ◽  
Author(s):  
Jae-Neung Lee ◽  
Yeong-Hyeon Byeon ◽  
Keun-Chang Kwak
Keyword(s):  
Mean Squared Error ◽  
Inertial Sensor ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Sensor Technology ◽  
Support Vector ◽  
Softmax Classifier ◽  
National Team ◽  
Motion Data

This paper discusses the classification of horse gaits for self-coaching using an ensemble stacked auto-encoder (ESAE) based on wavelet packets from the motion data of the horse rider. For this purpose, we built an ESAE and used probability values at the end of the softmax classifier. First, we initialized variables such as hidden nodes, weight, and max epoch using the options of the auto-encoder (AE). Second, the ESAE model is trained by feedforward, back propagation, and gradient calculation. Next, the parameters are updated by a gradient descent mechanism as new parameters. Finally, once the error value is satisfied, the algorithm terminates. The experiments were performed to classify horse gaits for self-coaching. We constructed the motion data of a horse rider. For the experiment, an expert horse rider of the national team wore a suit containing 16 inertial sensors based on a wireless network. To improve and quantify the performance of the classification, we used three methods (wavelet packet, statistical value, and ensemble model), as well as cross entropy with mean squared error. The experimental results revealed that the proposed method showed good performance when compared with conventional algorithms such as the support vector machine (SVM).

scholarly journals Gesture Recognition Algorithm of Human Motion Target Based on Deep Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhonghua Xia ◽  
Jinming Xing ◽  
Changzai Wang ◽  
Xiaofeng Li
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Time Domain ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Recognition Time ◽  
Overlap Ratio

There are some problems in the current human motion target gesture recognition algorithms, such as classification accuracy, overlap ratio, low recognition accuracy and recall, and long recognition time. A gesture recognition algorithm of human motion based on deep neural network was proposed. First, Kinect interface equipment was used to collect the coordinate information of human skeleton joints, extract the characteristics of motion gesture nodes, and construct the whole structure of key node network by using deep neural network. Second, the local recognition region was introduced to generate high-dimensional feature map, and the sampling kernel function was defined. The minimum space-time domain of node structure map was located by sampling in the space-time domain. Finally, the deep neural network classifier was constructed to integrate and classify the human motion target gesture data features to realize the recognition of human motion target. The results show that the proposed algorithm has high classification accuracy and overlap ratio of human motion target gesture, the recognition accuracy is as high as 93%, the recall rate is as high as 88%, and the recognition time is 17.8 s, which can effectively improve the human motion target attitude recognition effect.

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.

A Gesture Recognition Algorithm Based on PCA and BP Neural Network

2013 ◽  
Vol 734-737 ◽  
pp. 3053-3056
Author(s):  
Hong Yi Li ◽  
Jun Jie Chen ◽  
Xin Li ◽  
Di Zhao
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Gesture Recognition ◽  
Bp Neural Network ◽  
Recognition Algorithm ◽  
High Dimensional ◽  
Nonlinear Mapping ◽  
Testing Time ◽  
Intelligent Robot ◽  
Highly Nonlinear

Gesture recognition has many applications in fields such as the intelligent robot, human computer interaction and so on. The classical BP neural network has its advantages in modeling the highly nonlinear mapping from features to gesture meanings, and could avoid hard-coded feature extraction. However, it usually takes a rather long training and testing time, especially in dealing with redundant high dimensional data. To address this drawback, we combine the BP neural network and PCA, and propose an improved algorithm. Experiments demonstrate the feasibility and efficiency of the proposed algorithm by comparing with the classical one.

scholarly journals Motion Classification and Features Recognition of a Traditional Chinese Sport (Baduanjin) Using Sampled-Based Methods

2021 ◽  
Vol 11 (16) ◽  
pp. 7630
Author(s):  
Hai Li ◽  
Hwa Jen Yap ◽  
Selina Khoo
Keyword(s):  
Neural Network ◽  
Processing Time ◽  
Nearest Neighbor ◽  
Inertial Sensor ◽  
K Nearest Neighbor ◽  
Motion Classification ◽  
Motion Recognition ◽  
One Dimensional ◽  
Motion Data ◽  
Sensor Measurement

This study recognized the motions and assessed the motion accuracy of a traditional Chinese sport (Baduanjin), using the data from the inertial sensor measurement system (IMU) and sampled-based methods. Fifty-three participants were recruited in two batches to participate in the study. Motion data of participants practicing Baduanjin were captured by IMU. By extracting features from motion data and benchmarking with the teacher’s assessment of motion accuracy, this study verifies the effectiveness of assessment on different classifiers for motion accuracy of Baduanjin. Moreover, based on the extracted features, the effectiveness of Baduanjin motion recognition on different classifiers was verified. The k-Nearest Neighbor (k-NN), as a classifier, has advantages in accuracy (more than 85%) and a short average processing time (0.008 s) during assessment. In terms of recognizing motions, the classifier One-dimensional Convolutional Neural Network (1D-CNN) has the highest accuracy among all verified classifiers (99.74%). The results show, using the extracted features of the motion data captained by IMU, that selecting an appropriate classifier can effectively recognize the motions and, hence, assess the motion accuracy of Baduanjin.

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.

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