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Posture detection of athletes in sports based on posture solving algorithms

SYSTEMS AND SOFT COMPUTING 2024年 6卷

作者： Zhang, Huan Henan Coll Transportat Arts & Sports Dept Zhengzhou 450000 Peoples R China

With the rapid development of science and technology, the field of sports is constantly exploring and applying new technical means to improve the training effect and competitive level of athletes. Among them, the athletes' posture detection technology based on the attitude solving algorithm has been widely concerned in recent years. However, the current attitude solving algorithm has the limitation of low precision and low efficiency. Aiming at this, a new attitude solving algorithm is proposed. Firstly, the coordinate system is determined according to the theory of inertial navigation, and the attitude Angle is obtained by calculating the acceleration and magnetic induction intensity. Then the current attitude matrix is calculated according to the obtained attitude Angle. The initializing quaternion based on the attitude matrix is studied. Then, according to the advantages and defects of the three sensors, a complementary filtering algorithm is proposed for data fusion, so as to reduce the error of the final attitude solution. In order to further improve the accuracy of attitude detection, the complementary filter algorithm and double-layer kalman filter algorithm are combined to process the data, and finally the quaternion is updated. It can be seen that the detection error of the research constructed model is only 9.94%, and its three attitude angle errors are mainly concentrated between -0.5 degrees and 0.5 degrees The model constructed by the research can realize high-precision posture detection, which can provide more scientific and reliable training aids for gymnastics, which has very strict requirements for movements in sports. It has positive significance for the development of sports.

关键词： Sports training Complementary filtering algorithm kalman filtering algorithm Attitude solving Inertial navigation

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Designing an intelligent image detection and transmission system for the Internet of Things

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WIRELESS NETWORKS 2023年第3期29卷 1213-1222页

作者： Zhang, Jingya West Anhui Univ Coll Elect & Informat Engn Lu An 237158 Anhui Peoples R China

The Internet of Things (IoT) aims to meet the needs of smart services by combining multiple information intelligence tools and network technologies. In terms of technology, management, cost, policy, and security, the development of the IoT still faces many challenges. This study proposes an intelligent image detection and transmission system based on IoT communication, with the following primary objectives: First, this study aims to provide an early warning model based on kalman filtering Fast Fourier Transform Support Vector Machine (KF-FFT-SVM). It delivers early warning signals by analyzing and extracting historical features from surface motion data for a given period. The step size for spectrum analysis is determined by the signal frequency and is used to create a training dataset and train the SVM model. The use of a trained model for early warning can improve the accuracy of early warning evaluation. Second, the forward line of the video image is used as the necessary information in the content symbol retrieval process, and the information required for the structure is used to improve the quality as much as possible. Because of the search and influence of the transmission quality of digital components, the important data in the digital transmission space is used to ensure the accuracy of digital components while transmitting a small amount of energy. Third, when the device is connected to the network, other users can obtain information about the device via security breaches. The data must be transmitted by both parties using their own identities, which increases transmission security. The design idea of ZigBee technology follows the method of distributing information, such as food space through a zigzag-shaped dance, and low power consumption with low cost. Finally, to increase system efficiency, non-orthogonal multiple access technologies realize and transmit data from multiple users in time, frequency, and code zones while using various channels for each use

关键词： Internet of Things Communication Image transmission Intelligent image detection kalman filtering algorithm ZigBee technology

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Investigation of filtering algorithm for Noise Reduction in Displacement Sensing Signal

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IEEE SENSORS JOURNAL 2021年第6期21卷 7808-7812页

作者： Li, Changping Huang, Ruining Yi, Ying Bermak, Amine Chongqing Univ Posts & Telecommun Coll Commun & Informat Engn Chongqing 400065 Peoples R China Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Div Informat & Comp Technol Doha 34100 Qatar

With the emergence of new sensing technologies, soft substrate-based displacement sensing has gained popularity in the last decade. Nevertheless, performance degradation due to noise from unexpected vibrations of the substrate can affect the sensors accuracy and lead to poor measurements. Few studies have focused on noise reduction to improve the accuracy of the sensing data. Based on mathematical modeling, this paper compares the two most representative filtering algorithms based on minimum mean square error (MMSE), namely recursive least squares (RLS) and kalman algorithms for their predictive ability, accuracy, and reliability. Based on the modeling and comparison, we propose a kalman filtering algorithm to suppress the vibration noise in the displacement-based dosage sensor. The filter-processed data from the sensors measurement mainly contains the actual resistance change upon mechanical displacement of the sensor substrate and the undesired noise from its vibration. With the filtering algorithm, the noise can be reduced from the sensors measurement, and an accurate correlation between resistance change and displacement of the sensor can be established. This would provide a straightforward sensing signal that reflects the displacement, and in turn, achieves precise dosage monitoring in drug delivery devices, which can be used as a test-bed application for the sensor.

关键词： Sensors filtering algorithms kalman filters Noise measurement Resistance Noise reduction Estimation Displacement sensor kalman filtering algorithm noise reduction

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A Small Flow Point Signal Processing and Measurement Method for Ultrasonic Gas Meter

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MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA 2022年第2期37卷 251-259页

作者： Li, Y-Z Liu, G-Q Zhu, P-X Zhang, Y-H Li, M. East China Univ Technol Sch Mech & Elect Engn Nanchang 330013 Jiangxi Peoples R China

Due to the advantages of no loss of pressure and wide range, ultrasonic gas meter is becoming a hot research and application at home and abroad. For low power ultrasonic gas meter, a key problem is to improve the precision and stability of the small flow point measurement with the impact of circuit and environment and the influence of turbulence and pulsating flow. In this paper, the ultrasonic propagation time values and their difference values are achieved by using the method of threshold value and the one-half VCC "zero" point detection. The feature point of the arrival time is defined by the threshold value of the echo, which is the zero-crossing point of the next echo period. Propagation time and the differences can be obtained from several feature points that satisfy the threshold of echo. The measurement value at each time is obtained through the average filter process of these difference values. By using the improved kalman filtering algorithm in flow measurement data processing and adjusting the values of Q and R, the measuring accuracy and stability are improved, respectively, in the small flow point affected by the disturbance and pulsating flow. The latest application circuit introduced by TI company has been used in the ultrasonic gas meter circuit and system design, and the calibration experiment of ultrasonic gas meter has been completed. The experimental results show that the measurement accuracy and repeatability can meet the requirements of the application.

关键词： Small flow point Measurement method Ultrasonic gas meter kalman filtering algorithm

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A Comprehensive Detection System for Track Geometry Using Fused Vision and Inertia

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021年 70卷 1-15页

作者： Peng, Lele Zheng, Shubin Li, Peixing Wang, Yuling Zhong, Qianwen Shanghai Univ Engn Sci Sch Urban Railway Transportat Shanghai 201620 Peoples R China Shanghai Aerosp Equipment Mfg Gen Plant Res & Dev Shanghai 200245 Peoples R China

The track line suffers from track irregularity, subgrade settlement, rail surface defects, and gauge distortion. Measuring the track geometry quality plays an important role in rail administrations to safe operation and track maintenance. In order to meet the requirements of track daily inspection, this article describes a comprehensive detection system for the track geometry, which is designed by four cameras, an inclinometer, and an inertial measurement unit. Moreover, the detection system has the characteristics of simple structure, automatic detection, and multiple measurement parameters. First, to measure track alignment and irregularity, a multisensing calibration method is established to fuse visual sensors and inertial sensors. An accelerometer and a kalman filter are used to eliminate accumulated unrealistic drifts of accelerometer and gyroscope. Second, to measure the rail surface defects and gauge, the model of laser and machine vision is deduced to obtain the coordinate values in a unified coordinate system, and an improved iterative closest point method is developed to improve the matching accuracy. Finally, a series of experiments is carried out to validate the measuring performance. The results indicate that the proposed method can accurately measure the track geometry, with the maximum deviations of track alignment, track irregularity, rail defects, and gauge that are less than 0.5, 0.4, 0.3, and 0.1 mm, respectively. The detection system is not only simple in structure but also has multiple detection parameters, which is especially suitable for track daily maintenance.

关键词： Inertial measurement iterative closest point (ICP) method kalman filtering algorithm machine vision multisensor fusion rail gauge rail surface defects track alignment track geometry track irregularity

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Target Detection in Martial Arts Competition Video using kalman Filter algorithm Based on Multi target Tracking

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 895-904页

作者： Xin, Zhiguo Wuxi Vocat Inst Arts & Technol Dept PE Wuxi 214206 Peoples R China

To solve the low accuracy and poor stability in traditional object tracking methods for martial arts competition videos, a kalman filtering algorithm based on feature matching and multi object tracking is proposed for object detection in martial arts competition videos. Firstly, feature matching in multi target tracking is studied. Then, based on target feature matching, the kalman filtering algorithm is fused to construct a target detection model in martial arts videos. Finally, simulation experiments are conducted to verify the performance and application effectiveness of the model. The results showed that the average tracking errors of the model on the X and Y axes were 3.86% and 3.38%, respectively. At the same time, the average accuracy and recall rate in the video target tracking process were 93.64% and 95.48%, respectively. After 100 iterations, the results gradually stabilized. This indicated that the constructed model could accurately detect targets in martial arts competition videos. It had high tracking accuracy and robustness. Compared with traditional object detection methods, this algorithm has better performance and effectiveness. The kalman filter algorithm based on feature matching and multi target tracking has broad application prospects and research value in target detection in martial arts competition videos.

关键词： Multi target tracking kalman filtering algorithm martial arts competition videos target detection feature matching

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Deformation Monitoring of Monopole Communication Towers Based on Multi-Source Data Fusion

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BUILDINGS 2023年第11期13卷

作者： Ji, Xiaopeng Ren, Liang Fu, Xing Zhang, Qing Li, Hao Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China North China Elect Power Univ Hebei Key Lab Elect Machinery Hlth Maintenance & F Baoding 071003 Peoples R China

Monopole communication towers play an irreplaceable role in modern communication systems. Because of its high flexibility, lateral loads control the deformation of a monopole communication tower. Dynamic displacement can be used to describe structural vibration characteristics and real-time deformation situations. Therefore, this article proposes a monopole communication tower deformation monitoring method based on multi-source data fusion using dynamic displacement as the evaluation indicator. This method calculates the strain displacement using the strain-mode superposition methodology. Then, the strain displacement and acceleration obtained are processed using kalman filtering technology to reconstruct the real-time displacements of the monopole communication tower. The effectiveness of this method was verified using numerical simulations and model experiments, respectively. In addition, parametric analysis shows that this method is suitable for processing multi-rate data, has good noise resistance in strong noise environments, and can effectively reconstruct the displacement near the tower bottom. The results indicate that the method has favorable robustness and can accurately reconstruct low-frequency and high-frequency displacements of the monopole communication tower.

关键词： monopole communication tower multi-source data fusion tower displacement SSI method strain displacement kalman filtering algorithm

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The construction of a practical platform for party building and ideological and political education based on information fusion

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Zhang, Bin Li, Yuan Yu, Yuxue Changchun Univ Chinese Med Coll Marxism Changchun 130117 Jilin Peoples R China Changchun Univ Chinese Med Student Affairs Off Changchun 130117 Jilin Peoples R China Changchun Univ Chinese Med Coll Tradit Chinese Med Changchun 130117 Jilin Peoples R China

Party building and ideological and political education have received much attention in recent years, and the construction of its work practice platform is particularly important. In order to improve the level of party building and ideological and political education in colleges and universities, In this paper is based on the information fusion technique to fuse relevant factors after data, feature, and decision levels. Using sampling calculation, the percentage of relevant factors was calculated as 37%, 38%, and 25%, and the kalman filter algorithm was used to test the information data. The two factors that accounted for more were selected as the platform construction indexes, and the information was fused with the maximum likelihood estimation and D-S algorithm to build the party construction and ideological and political education work practice platform. It is evident that the practice platform based on information fusion technology can promote the development of ideological and political education.

关键词： Information fusion techniques Maximum likelihood estimation kalman filtering algorithm D-S algorithm Platform construction

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A Smart Multi-Rate Data Fusion Method for Displacement Reconstruction of Beam Structures

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SENSORS 2022年第9期22卷 3167-3167页

作者： Zhang, Qing Fu, Xing Sun, Zhiguo Ren, Liang Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116023 Peoples R China China Earthquake Adm Key Lab Bldg Collapse Mech & Disaster Prevent Langfang 065201 Peoples R China

Dynamic displacement plays an essential role in structural health monitoring. To overcome the shortcomings of displacement measured directly, such as installation difficulty of monitoring devices, this paper proposes a smart reconstruction method, which can realize real-time intelligent online reconstruction of structural displacement. Unlike the existing approaches, the proposed algorithm combines the improved mode superposition methods that is suitable for complex beam structures with the kalman filtering approach using acceleration and strain data. The effectiveness of the proposed multi-rate data fusion method for dynamic displacement reconstruction is demonstrated by both numerical simulation and model vibration experiment. Parametric analysis shows that the reconstruction error is only 5% when the noise signal to noise ratio is 5 dB, illustrating that the proposed algorithm has excellent anti-noise performance. The results also indicate that both the high-frequency and low-frequency components of the dynamic displacements can be accurately reconstructed through the proposed method, which has good robustness.

关键词： structural health monitoring multi-rate data fusion dynamic displacement reconstruction strain mode shape kalman filtering algorithm

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Protection and detection of physical hazards for the intelligent system under industrial Internet of Things by neural network

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SECURITY AND PRIVACY 2023年第2期6卷

作者： Wang, Rongxia Liu, Zhifang He, Fen Yang, Weihuang Guangzhou Nanyang Polytech Coll Sch Informat Engn Guangzhou 510925 Guangdong Peoples R China

Aiming at detecting and avoiding many security risks brought by various intelligent equipment and sensors in industrial Internet of Things (IoT) intelligent system, the kalman back propagation neural network (kalman-BPNN) algorithm is designed, based on the neural network and with the help of the unique advantages of kalman filtering algorithm and back propagation neural network (BPNN) algorithm. Introductions are made on the architecture of IoT and the theory of system security, and descriptions are made on the principle and process of related algorithms in detail. The advantages of traditional algorithms are integrated to design a kalman-BPNN algorithm. The simulation experiment analysis is carried out, and the designed detection method is compared with the traditional detection method. The voltage test of the kalman-BPNN algorithm based on the neural network is no more than 4.1%, as the experiment shows. The true positive rate of kalman-BPNN algorithm is 91.121%, which is very close to the value of 1, indicating that the algorithm has a great probability to predict the normal voltage. The false positive rate is 9.341%, which is very close to 0, indicating that the algorithm has a small probability of false judgment.

关键词： intelligent system of the industrial IoT kalman filtering algorithm kalman-BPNN algorithm neural network

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