基于可穿戴式纳米生物传感器的人体运动数据挖掘算法  

Data mining algorithm for human motion based on wearable nano biosensor

作　　者：马宪敏[1] 崔元全 李放[3] MA Xianmin;CUI Yuanquan;LI Fang(Department of Eastern Language,Heilongjiang International University,Harbin 150025,China;Human Resources Office,Harbin Normal University,Harbin 150025,China;Department of Information Engineering,Heilongjiang International University,Harbin 150025,China)

机构地区：[1]黑龙江外国语学院东语系,哈尔滨150025 [2]哈尔滨师范大学人事处,哈尔滨150025 [3]黑龙江外国语学院信息工程系,哈尔滨150025

出　　处：《智能计算机与应用》2024年第8期220-224,共5页Intelligent Computer and Applications

基　　金：2023年度黑龙江省哲学社会科学研究规划项目(23JYB252);黑龙江省教育科学规划重点课题(GJB1422539)。

摘　　要：针对当前人体运动数据挖掘算法无法对实时数据进行采集与分析,导致人体运动数据挖掘正确率较低且时间较长的问题,提出基于可穿戴式纳米生物传感器的人体运动数据挖掘算法。首先,利用可穿戴式纳米生物传感器采集人体运动数据,将采集到的数据转换为二进制数据形式,并对转换后的数据进行清洗与补位处理;最后,使用萤火虫算法对K均值聚类方法进行优化,利用优化后的K均值聚类方法对清洗与补位后的数据进行聚类处理。实验结果表明,所提算法的召回率平均值为97.12%,数据挖掘正确率平均值为98.42%,为运动员生理指标的实时监测与分析提供重要的数据基础。To address the problem that current human motion data mining algorithms cannot collect and analyze real-time data,which leads to low correct rate and long time for human motion data mining,we propose a human motion data mining algorithm based on wearable nano-biosensor.Firstly,the human motion data is collected using the wearable nano-biosensor.Secondly,the collected data are converted into binary data form,and the converted data are cleaned and complemented.Finally,the K-mean clustering method is optimized using the firefly algorithm,and the optimized K-mean clustering method is used to cluster the cleaned and patched data.The results show that the average recall rate of the proposed algorithm is 97.12%and the average correct data mining rate is 98.42%,which provides an important data base for real-time monitoring and analysis of athletes′physiological indexes.

关 键 词：纳米生物传感器 人体运动 数据挖掘 数据清洗 K均值聚类算法 数据采集 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]