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Abstract
The application of wireless sensors in sports competitions is becoming more and more common. This research mainly discusses the simulation training of e‐sports players based on a wireless sensor network. Under the same experimental conditions, in order to avoid mutual influence and interference between the hand grip test and continuous endurance load, the exercise experiment for each subject was repeated twice. At the same time, the EMG signal collection and reaction time test during the endurance load are performed. All tests are data records before and after 40 minutes of the DOTA game competition. Before the start of each experimental test, the experimental equipment is calibrated and the parameters of the required indicators are set; the software was opened to run and checked whether it is normal; before the measurement, let the subjects perform simple preparation activities, train the subjects, and understand and be familiar with the action essentials required by the test to reduce the error. The original surface EMG signal recorded directly uses the built‐in signal processing function in the MR‐XP 1.08 master edition software to perform full‐wave rectification and smoothing. Processing of original EMG data: firstly, the EMG signal during endurance contraction is intercepted. In order to exclude individual differences in sEMG indicators of different subjects, the starting point is the first rise of each subject to 60% MVC or 25% MVC. The arrival time is the end point. In e‐sports, the reaction speed when the prompt is effective is significantly faster than when the prompt is invalid (p < 0.05). At this time, the time interval between the cue prompt and the target stimulus is 500 ms. This study is helpful to improve the athletes’ technical and tactical level.
Highlights
The wireless sensor network is a highly application-related network
Yu et al has developed a fall detection system based on the hidden Markov model (HMM), which can use a single motion sensor to automatically detect falls for actual home monitoring scenarios
The processing of the original EMG data is as follows: first, intercept the EMG signal during endurance contraction, in order to exclude the individual differences in sEMG indicators of different subjects, with each subject’s first rise to 60% MVC or 25% MVC as the starting point; the arrival time is the end point
Summary
The wireless sensor network is a highly application-related network. For different application requirements, the hardware structure of the wireless sensor network node is not the same, but its core components are basically the same. Shen et al studied the reliability and applicability of active and continuous smart phone authentication using motion sensor behavior in various operating scenarios and systematically evaluated the uniqueness and durability of the behavior They provide accurate and finegrained representations of user touch actions, their research process lacks data [1]. Yurtman and Barshan proposed a novel noniterative direction estimation method based on the physical and geometric characteristics of acceleration, Wireless Communications and Mobile Computing angular velocity, and magnetic field vector to estimate the direction of the motion sensor unit. They obtain the orientation of the sensor unit according to the rotation quaternion transformation between the sensor unit frames. They collect data sets from experiments that simulate falls and normal activities, the research process lacks theoretical foundation [4]
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