An Empirical Study of Gait and Speed Based on Smart Wearable Devices
Objective To establish a rapid prediction model based on intelligent wearable devices to provide reference for selection and trainingof athletes.Method Pearson correlation test and stepwise regression analysis were performed on the gait data of 8 students majoring in track and field in Physical Education College of Tianshui Normal University by SPSS.Results There was a significant positive correlation between the slope and the proportion of forefoot landing and the average stride length(P＜0.01).There was a significant negative correlation between the slope and the proportion of heel landing(P＜0.01).There was a significant negative correlation between the slope and the valgus amplitude(P＜0.05).There was a significant positive correlation between speed and activity calories,steps,forefoot landing ratio,average stride frequency,average stride length,average flight time,average touchdown flight ratio,and average landing impact force(P＜0.01).There was a significant negative correlation between and the proportion of full palm landing,the proportion of heel landing,and the average touchdown time(P＜0.01).Conclusion The average accuracy of the running bean function algorithm of the intelligent wearable device was high.The collected data was helpful to the guidance of the athletes in the training process,and can provide some reference for the selection of athletes.It was recommended that CyweeMotion use the software algorithm and the data captured by the six-axis motion sensor in real time to increase the readable number of the swing angle on the App side,which provided a certain reference for improving the runner's thigh and calf folding technology and improves the running efficiency.According to the speed model formula,in order to improve the speed of athletes more scientifically and effectively,more steps,strides,touchdown time and other improvement exercises should be added in the training process.
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