With the rapid development of electronic science and technology, the research on wearable devices is constantly updated. Still, the current wearable devices are not yet comprehensive in recognizing and analyzing the movement patterns of special sports. Based on this, this paper improves wearable devices for table tennis, a single sport, and realizes the recognition and evaluation of table tennis players’ motor skills based on the pattern recognition method and hierarchical evaluation system. First, a table tennis player’s motion information acquisition device was designed and the collected motion data were processed. Secondly, a sliding window was designed to segment the collected motion data and establish a feature database of six table tennis benchmark movements. Secondly, the motion features were constructed based on feature engineering, and after dimensionality reduction, the different models were used for action skill recognition. Finally, a hierarchical evaluation system of movement skills was established and the loss functions of different evaluation indexes were given. The experimental results show that in table tennis players’ action skill recognition, the neural network model based on motor feature extraction proposed in this paper improves the recognition accuracy by 6.5% than the traditional convolutional neural network, and in table tennis players’ action skill evaluation, the ANOVA shows that the hierarchical evaluation system proposed in this paper has a significance level of p<0.05 for the athletes’ professionalism level. The conclusion of the study indicates that the proposed paper table tennis players’ motor skills recognition and hierarchical evaluation assessment system can realize the accurate recognition of athletes’ motor skills and the precise determination of their professional level.

With the impact of artificial intelligence on the traditional UAV industry, autonomous UAV flight has become a popular research field at present. Based on the demand for research on key technologies for autonomous UAV flight, this paper studies UAV flight state recognition. This paper is based on multi-sensor acquisition of UAV on-board information, and uses the collected information for data fusion to complete UAV flight state identification. Firstly, UAV flight data acquisition and data preprocessing are carried out; secondly, UAV flight trajectory features are extracted based on multidimensional data fusion; finally, UAV flight state recognition model based on PCA-DAGSVM model is established. The results show that the algorithm based on multi-sensor data fusion has good recognition effect in the UAV flight state recognition problem. The recognition accuracy of the algorithm exceeds that of the random forest model, and its accuracy in the training set of UAV flight state recognition is more than 90%, and its accuracy in the test set is more than 80%.

With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm of UAV flight state recognition and trajectory prediction. Presenting an innovative approach focused on improving the precision of unmanned aerial vehicle (UAV) path forecasting via the identification of flight states, this study demonstrates its efficacy through the implementation of two prediction models. Firstly, UAV flight data acquisition was realized in this paper by the use of multi-sensors. Finally, two models for UAV trajectory prediction were designed based on machine learning methods and classical mathematical prediction methods, respectively, and the results before and after flight pattern recognition are compared. The experimental results show that the prediction error of the UAV trajectory prediction method based on multiple flight modes is smaller than the traditional trajectory prediction method in different flight stages.

In this paper, a LabVIEW-based online monitoring and safety evaluation system for UAVs is designed to address the deficiencies in UAV flight state parameter monitoring and safety evaluation. The system consists of a lower unit for UAV recording and an upper unit on the ground. The lower unit collects and detects flight data and connects to the upper unit through a wireless digital transmission module via a serial port. The upper unit receives the data and carries out the monitoring and safety situation evaluation of the UAV. The lower unit of the system adopts multi-sensors to collect UAV navigation information in real time to achieve flight detection, while the upper unit adopts LabVIEW to design the UAV online monitoring and safety situation prediction system, enabling monitoring and safety situation prediction during UAV navigation. The test results show that the system can detect and comprehensively display the navigation information of the UAV in real time, and realize the safety evaluation and warning function of the UAV.