A study on the application of sensor network for teaching rock climbing in college students’ health monitoring

In this paper, a real-time health monitoring system based on multi-source sensors is constructed using a modularized approach, and the mechanism of generating human physiological parameters such as pulse wave, blood pressure saturation and heart rate is introduced. After that, an adaptive Kalman filtering algorithm is used to obtain the actual state and real values of the real-time health monitoring system. Finally, a linear regression model of blood oxygen saturation value and R-value was established by Lambert-Beer law to monitor college students’ physical health, and the application effect of this system was analyzed by taking rock climbing teaching as an example. The results show that the system measures heart rate, arterial oxygen saturation, systolic blood pressure, and diastolic blood pressure with very high accuracy and minimal error. There were no significant differences between the experimental group and the control group prior to the experiment in terms of upper limb specific strength and the 6 technical assessment items (P > 0.05). After 12 weeks of training, the differences between the experimental and control groups before and after the experiment were significant (P < 0.05). However, the mean values of pull-ups, hanging feelers and 6 categories of techniques in the experimental group improved by 10.71, 9.79 cm and 15.35%-17.70%, respectively; while the control group improved by 5.33, 3.66 cm and 6.14%-7.41%. In contrast, the enhancement effect of the experimental group is more noticeable. It can be seen that the real-time health monitoring system proposed in this paper is effective in teaching college students rock climbing.