Online position recognition and correction method for sports athletes

A self-powered intelligent integrated sensing system for sports skill monitoring

The use of green intelligent sensing systems which are based on triboelectric nanogenerators have sparked a surge of research in recent years. The development has made significant contributions to the field of promoting human health. However, the integration of an intelligent sensing system with multi-directional triboelectric nanogenerators (TENGs) remains challenges in the field of motion monitoring. To solve this research issue, this study designed a self-powered multifunctional fitness blanket (SF-MFB) which incorporates four TENGs, features multi-sensors and wireless motion monitoring capabilities. It presents a self-powered integrated sensing system which utilizes four TENG sensing units to monitor human motion. Each TENG sensing unit collects the mechanical energy generated during motion. The system is composed of SF-MFB, Bluetooth transmission terminal, and upper computer analysis terminal. Its main purpose is to wirelessly monitor and diagnose human sports skills and enables real-time human-computer interaction. The TENG integrated self-powered sensing system demonstrates practicality in sports skills monitoring, diagnosis, human-computer interaction and entertainment. This research introduces a novel approach for the application of TENG self-powered intelligent integrated sensing system in health promotion.

Recognition of Human Body Feature Changes in Sports Health Based on Deep Learning

With the rapid development of social economy and the extensive and in-depth development of national fitness activities, national physical fitness monitoring and research work has achieved rapid development. In recent years, the application of deep learning technology has also achieved research breakthroughs in the field of computer vision. How deep learning technology can effectively capture motion information in sample data and use it to realize the recognition and classification of human actions is currently a research hot spot. Today’s popularization of various shooting devices such as mobile phones and portable action cameras has contributed to the vigorous growth of image data. Therefore, through computer vision technology, image data is widely used in practical application scenarios of human feature recognition. This paper proposes a deep learning network based on the recognition of human body feature changes in sports, improves the recognition method, and compares the recognition accuracy with the original method. The experimental results of this paper show that the result of this paper is 1.68% higher than the original recognition method, the accuracy rate of the improved motion history image is increased by 14.8%, and the overall recognition rate is higher. It can be seen from the above experimental results that this method has achieved good results in human body action recognition.

Deep Learning and Improved HMM Training Algorithm and Its Analysis in Facial Expression Recognition of Sports Athletes

Facial expressions are an auxiliary embodiment of information conveyed in the communication between people. Facial expressions can not only convey the semantic information that people want to express but also convey the emotional state of the speaker at the same time. But for sports athletes in training and competitions, it is usually not convenient to communicate directly. This paper is based on deep learning and an improved HMM training algorithm to study the facial expression recognition of sports athletes. It proposes the construction of deep learning of multilayer neural network, and the rank algorithm is introduced to carry out face recognition experiments with traditional HMM and class-specific HMM methods. The experimental results show that, with the increase of rank value, the class-specific recognition rate is up to 90%, the detection rate is 98% and the time-consuming is 2.5 min, which is better than HMM overall.

Activity classification and analysis during sports training session using fuzzy model

Sport training is a sporting performance preparation phase that consists of four parts: Training in conditioning, technical Training, Training in attitude, Training in psychology. The challenging factor of the sports training session is the realization of approximate and uncertainty. The valued idea involves the definition of fuzzy sets and rules and membership functions to overcome the challenges. A fuzzy logical explanation enables successful ambiguous situations, which are complex, continuous, and more practical, closer to the real world and human thought. The concept, which is highly valued, includes defining fluids and rules and membership functions to test training exercises for strength. This paper proposed a Fuzzy, assisted artificial intelligence monitoring framework (FAAIMF) that evaluates all athletes’ behaviors in an outdoor training setting using wearable inertial sensors. Data obtained from sensor fitted machines, feedback, and proper implementation requirements are considered in the design. The Random Forest Classifier uses a Discreet Transform Wavelet (DWT) to effectively and accurately effectively and accurately id. Second, the relative orientation of the wearable inertial sensors on a shield and thigh of a material from which the knee angle of flexion-extension is determined. The proposed method in various non-constrained settings for the exact classification of sports activities and accurate movement techniques assessment.

Behavior recognition based on track space-time characteristics

Under the influence of novel corona virus pneumonia epidemic prevention and control, higher requirements for behavior recognition in complex environment are put forward. The accuracy of traditional methods for sports training is not high, so a method is needed to improve the local action recognition to assist sports training. In the process of behavior recognition, if only the track is regarded as an independent individual, the information of its neighbor will be ignored. Therefore, we use KNN algorithm to get the nearest neighbor trajectory. In order to calculate the rich neighborhood information around the track, this paper calculates the complex relationship between the center track and the neighborhood track from four different angles, including absolute motion, relative motion, distance relationship and direction relationship. Then, from the four different perspectives of variance, discrete coefficient, skewness and kurtosis, this paper proposes a large interval nearest neighbor coding method. This method makes the four eigenvalues complement each other and improves the ability of describing complex and changeable behaviors. The experimental results show that the coding method proposed in this paper can be used for behavior recognition according to different transformation matrix.