Optimized design of flexible sensors for snow and ice sports characterization and intelligent recognition of training technique movements

Abstract

Skiing performance is affected by a variety of external factors, and real-time monitoring of skiers' skiing status is very important for improving athletes' skiing technique. There are problems in the training monitoring of snow sports athletes in a wide range of venues, such as difficulty in accurately obtaining the parameters of sports status, lack of quantitative assessment of the training effect, and inability to provide timely feedback. And traditional sensors cannot adapt to cold, humid and other weather conditions, which affects their stability. Traditional sensors can only provide limited motion data and cannot comprehensively evaluate the technical level of skiers. At the same time, it is difficult to provide sufficiently accurate data in complex snowy environments. To address this problem, this study proposes a flexible sensor that can contact with the human body and sense athletes' body postures and movements in real time. By combining the flexible sensor with ice and snow sports equipment, intelligent recognition of athletes' movements can be realized. By monitoring and analyzing athletes' movements in real time, problems can be detected in time and targeted adjustments and training can be carried out. The findings of the experiment demonstrate that the accuracy of the time synchronization model, the time-domain high-pass filtering model, the zero-speed correction model, and the improved zero-speed correction model are, respectively, 97, 94, 92, and 90 when the size of the training set is 800. At the distance of 2000 m, The CF-TENG, H-TENG and CS-TENG response times are 18 ms, 20 ms and 28 ms. The results show that the designed flexible sensors have good performance and can be used in the daily training of cross-country skiing and alpine skiing professional sports teams.