Athlete’s physiological parameter monitoring system based on K-means and MTLS-SVM algorithm

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Yang Wu
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引用次数: 1

Abstract

In the non-medical model physiological parameter monitoring system, learning the monitoring parameters can improve the diagnostic and prediction accuracy. Aiming at the problems of insufficient information mining and low prediction accuracy in multi-task time series, the supervised and semi-supervised learning methods in machine learning are combined to predict the physiological status of remote health monitoring objects. This method uses the K-means algorithm to cluster the same type of data and use the Multitasking Least Squares Support Vector Machine (MTLS-SVM) to train historical data for trend prediction. In order to evaluate the effectiveness of the method, the MTLS-SVM method is compared with the K-means and MTLS-SVM methods. It can be seen from the experimental results that the body temperature data measured by the GY-MCU90615 is close to that of the digital thermometer. Moreover, the body temperature speed collected by the GY-MCU90615 can reach the millisecond level, which can well meet the needs of the system. The research shows that the method has higher prediction accuracy and has a breakthrough significance for the monitoring of athletes’ physiological parameters.
基于K-means和MTLS-SVM算法的运动员生理参数监测系统
在非医学模型生理参数监测系统中,对监测参数的学习可以提高诊断和预测的准确性。针对多任务时间序列中信息挖掘不足、预测精度低等问题,将机器学习中的监督学习和半监督学习相结合,对远程健康监测对象的生理状态进行预测。该方法使用K-means算法对同类型数据进行聚类,并使用多任务最小二乘支持向量机(MTLS-SVM)训练历史数据进行趋势预测。为了评价该方法的有效性,将MTLS-SVM方法与K-means方法和MTLS-SVM方法进行了比较。从实验结果可以看出,gy - mc90615测量的体温数据与数字体温计接近。此外,gy - mc90615采集的体温速度可以达到毫秒级,可以很好地满足系统的需要。研究表明,该方法具有较高的预测精度,对运动员生理参数的监测具有突破性意义。
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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