Water Dragon Boat Training Monitoring System Based on Multisensor Data Fusion Technology
With the development of science and technology, a variety of electronic devices have entered our lives, making our lives more intelligent and making our work more effective. This article is aimed at studying the application of multisensor data fusion technology to the water dragon boat training moni...
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| Formato: | article |
| Lenguaje: | EN |
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Hindawi Limited
2021
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| Acceso en línea: | https://doaj.org/article/60b3f9406de640e0a9e546b14d3868ca |
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| Sumario: | With the development of science and technology, a variety of electronic devices have entered our lives, making our lives more intelligent and making our work more effective. This article is aimed at studying the application of multisensor data fusion technology to the water dragon boat training monitoring system. In that case, we can analyze the various physical indicators of dragon boat athletes based on the data reflected by these sensors, when they can reach their physical limits and can perform in the best state to obtain the best results. The sensor is used to decompose the relevant data of each part of the athlete’s limbs. This step is based on the image and understands the maximum value of the data to adjust the training goal. This article proposes some data fusion algorithms, using Kalman filter method, Bayesian estimation method, and DS evidence theory algorithm to compare data fusion systems, through the comparison to find the best fusion accuracy, and then get the most suitable method is then applied to this water dragon boat monitoring system to enhance the training efficiency of dragon boat athletes. The experimental results in this paper show that when the value of the parameter increases from 0.97 to 2.5, the average classification accuracy of the k-NN classifier decreases from 0.97 to 0.4, and the accuracy of the fusion results of the three fusion rules is also reduced correspondingly, but in this paper proposed, RP fusion rule still has better performance than the other two fusion rules. When the classifier is k-NN, the three fusion rules increase with the number of sensors, and the accuracy of the fusion results is correspondingly improved. However, the final fusion accuracy obtained by the RP fusion rule proposed in this paper is always better than NB integration rules, and WMV integration rules are higher. Through these analyses, a training program that is most suitable for dragon boat athletes can be worked out, so that the athletes will not be useless. Multisensor data fusion technology brings great convenience to water dragon boat training and can provide more reasonable and accurate data to explore a practical way on the basis of ensuring the safety of personnel. |
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