Abstract
Rock climbing is a sports activity that integrates competition, entertainment, and culture. With the development of the economy and the improvement in living standards, rock climbing has embarked on a path of self-development and has entered the lives of urban youth at an increasingly rapid rate. This paper studies the probabilistic model of rock climbing recognition based on time series of multi-information fusion sensors so that climbers can climb more standardized. Based on practice, this paper has conducted research and design on the hardware platform and actually applied it to the rock climbing environment. Through reasonable processing of rock climbing process data of rock climbers, a variety of rock climbing state characteristics are successfully extracted for fusion. Aiming at the quasi-periodical characteristics of acceleration changes at different points during human movement, a method for identifying human movement patterns based on gait event information is designed. This method intercepts the three-axis acceleration data collected by each accelerometer through key gait events. A data set used to identify human movement patterns is established. A corresponding LDA classifier is established for each data set to identify the current movement pattern, and finally the classification results of all the classifiers are voted on. The final experiment shows that the system can identify the climbing movement of the climber within 3 s. The method can achieve 95.84% of the comprehensive recognition accuracy of the four state modes of rock climbing.
Highlights
With the development of society, peopleβs living standards have improved a lot.At this time, they pay more attention to the use of leisure time and choose to go to nature
Outdoor sports are loved by more people
After the above-mentioned experiments, it can be shown that the rock climbing motion recognition probability of the multi-information fusion sensor time series has a high accuracy rate
Summary
With the development of society, peopleβs living standards have improved a lot. At this time, they pay more attention to the use of leisure time and choose to go to nature. Rock climbing is an outdoor sport developed today, which is an extreme sport and has a strong challenge It is loved by young people all over the world. The research on the probability model of rock climbing motion recognition based on the time series of multiinformation fusion sensors can be reduced. It turns out that the method he proposed is only suitable for some dynamic gesture recognition tasks and is not suitable for recognizing rock climbing motion models [1]. In the multi-sensor information fusion system, the measurement data time mismatch problem caused by the different sampling period or power-on time of each sensor is conducted with in-depth research It mainly introduces the research background, significance and status quo of multi-sensor pattern recognition probability in rock climbing, systematically expounds the main rock climbing pattern recognition methods, and makes necessary analysis
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